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Tutorial - Food Security III

1. In memory File on disk Variables leave empty for all variables Options Display only variable names Display only general information Do not abbreviate variable names Display variable number along with name Clear dataset from memory F Replace data in memory with describe report ok Cancel Submit At the bottom of the dialog box there are three icons on the left The first a question mark opens the Help screen to explain the options in the dialog box The second an R LR resets the information in the dialog box so that nothing has been selected The third icon Copy commands to clipboard when clicked copies the command that is built based on the selections you make in the dialog box to the clipboard You can then switch to the do file and paste the command into the do file On the right hand side there are the choices to click on Ok Cancel or Submit If you choose the dialog box remains open so that you can select another option within the dialog box without having to open the box again If you choose the dialog box closes The command is automatically executed whether you choose Submit or Ok We want a description of all variables therefore we can leave the list of variables blank Before you click 23 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations on OK click on the Copy bu
2. 58 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation 4 Copy the command to open this datafile from the Results window switch to the Do File Editor and paste the command into the do file Delete the reference to the folder Why do we delete the folder reference 5 Save the do file to the name session2 do We now want to convert all production from the different crops into kilograms To find the conversion factor appropriate for each case in the production file c q4 dta we need to look up the product and unit in the conver dta file We will merge the information from this file into the file in memory the production file The variable with the conversion factor will then be available to calculate the total kgs produced In Stata we want to use the joinby command for this merge It can be found through the menus with the following choice Data Combine datasets Form all pairwise combinations within groups The input files for a merge must be sorted by the key variable s key variables are those variables you are using to match by between the two files Since there is a unique conversion factor for each product unit combination both our product variable and our unit variable are the key variables Both data files must be sorted by the variables we will match by The CONVER DTA file is already sorted by prod and unit However if we want to sort that file we will have
3. 60 001 max 4 61 and older generate age_gp1 label age_label There is a limit to the line length that Stata will read so if the command is long you will need to place the last part of the command on a new line and use the continuation symbols Let s add a variable label to the new variable The Stata command is 48 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Variation on the second method The recode function label variable age_gp1 Age group second method Now compare the age_gp variable with the age_gp1 variable Use a cross tabulation tabulate2 command The counts should be identical The same results can be achieved by using a function along with the command to create a variable The function we will use is called recode We will combine that function with the Generate command The recode function takes three or more arguments The first argument is the variable name that you want to categorize The rest of the arguments are used to determine how to code the new variable 1 Select Create or change variables from the Data menu 2 Select Create new variable 3 Click on the reset button in the lower left hand corner of the dialog box LR if you need to remove any information that appears in the box 4 Under the Main tab type the name of the new variable in the Generate Variable box agecat 5 Click on the Generate v
4. list district vil hh mem ca3 if ca3 gt 70 list district vil hh mem ca2 ca3 if ca3 lt 15 amp ca2 lt 3 If the variables you want to list are in the order in the file that you want to see the data rather than list each of the names you can type the first variable then a dash then the last variable in the list e g list district ca3 if ca3 lt 15 amp ca2 lt 3 If we want to see the observations with the five lowest values and five highest values we would first sort by that 36 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Exercise 1 1 variable and list the first five cases and the last five cases Our example question would be What is the age of the 5 youngest heads of households and what is the age of the 5 oldest heads of households To achieve the list for 5 youngest heads we sort ascending by the relation to head variable ca2 and then the age variable ca3 The Stata commands are sort ca2 ca3 list district vil hh mem cal ca2 ca3 in 1 5 To list the 5 oldest heads we sort by ca2 ascending and ca3 descending negative symbol in front of the variable using the Stata command gsort gsort ca2 ca3 list district vil hh mem cai ca2 ca3 in 1 5 Reminder Any commands that are typed directly into the Command window should be copied to the Do file Editor and comments written to explain the commands Apply what you ve just learned about de
5. z is a local macro name which is set to each value in the variable levels The values we know are 1 2 and 3 In the first loop of this programming command z is equal to 1 in the second loop z is equal to 2 etc quartile z refers to a variable name where the value of z is appended to the name quartile e g quartilel quartile2 quartile3 etc district z means that for the first loop district is equal to 1 for the second loop district is equal to 2 etc Very important note The macro name z must be surrounded by a left single quote found in the upper left hand corner or the keyboard to the left of the key with the number 1 and a right single quote found on the key to the left of the lt Enter gt key If you do not use the left single quote you will see an error message that says in red invalid name Be sure that you end the first line with a left curly brace e g and that you place on a line by itself after all commands that you want to be included in the loop a right curly brace e g Since we have 3 districts 3 new variables will be created with names of quartilel quartile2 quartile3 7 Now that we have each district divided into 4 equal groups we want all the information to be in just one variable We will create another variable and fill it with the information from the variables created above We create a new variable and fill it 81 Stata 13 Sample Session Sec
6. The tabulate command 26 35286 163 4359 22 81508 159 5101 2 523121 4 574581 15 61243 86 10356 4 938435 6 875536 We would like to produce a table that shows the distribution of cases according to their values using two or more categorical variables Stata calls this type of table a two way table Look at the household member questionnaire in the annex section Annex Table IA One thing you might be interested to know is how the gender of the respondents varied by their relationship to the head of household This would tell you for example how many females are heads of households The tabulate command will produce this type of table Make the household member file C qla dta the working data file 1 Click on the yellow open folder tool at the top left of the Toolbar 38 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations 2 Select the file c q1a dta 3 Click on Open to open the file 4 Copy the command for opening the file which appears in the Results window into the Do file Editor window Reminder You should add comments to your do file so that you can remember what and why you were doing specific commands when you developed the do file Several days or weeks from now you may not remember Comments in a do file start with slash asterisk and end with an asterisk slash this is a comment which runs to two lines Stata will not run multiple line
7. markup and control language files and print log and other files This window is not contained in the STATA 13 program window but stands alone and appears on the task bar as another icon Data Editor where you can view the data you have loaded into the program s memory Do file Editor text editor where you can build a do file a file that contains commands that Stata can execute This window is not contained in the STATA 13 window but stands alone and appears on the task bar as another icon Graph window only appears if the graph command is run Note the tool bar at the top under the menus which provides shortcuts to options in the Menus that are most commonly used You can switch between the windows within Stata by using the Window choice from the Menu Note that shortcuts are also listed e g to switch to the Variables window press lt Ctrl gt 4 to switch back to the Command window press lt Ctrl gt 1 Version 13 of Stata provides menus to help the user However the user can also type all the commands in the Command window Throughout this tutorial if the action desired can be done using the menus directions will be given on how to use the menus The Stata command that will do the same action will also be given so that you become familiar with the commands Stata provides a mechanism to paste commands into a do file that you can then execute You can send commands that appear in the Review window to th
8. 7 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok The unweighted mean is 29 54 which does not take into account the population differences between the states Using the weight option takes the population of each state into account before calculating the mean The Stata commands are use census dta clear summarize medage aweight pop summarize medage Survey weights are discussed in the next section An indicator variable is a special case of a categorical variable An indicator variable has two groups only whereas other categorical variables can have more than two groups Usually the values in indicator variables are 0 and 1 or no yes Examples of indicator variables are Is a person a citizen of the U S no yes Does a farmer use fertilizer no yes Stata can convert continuous variables to categorical and indicator variables and it can also convert categorical variables to indicator variables Suppose we want to create a new variable that indicates whether a person is 18 years old or older You could have generated a new variable and assigned it a value of 1 if ca3 gt 18 Then you would need a second step to recode the system missing to 0 There is another way to create this variable We will use the file c_q1a dta Open the file and then create a new variable using the generate command following the steps below 1 Click on File the
9. Survey estimation to account for design effectS nieee iaia canna gus dha wana aaas R canedicbs de Ma a san RSE A A EES aa E dundee a E E tases How to move Stata results into other applications Copying tables from the Results Window oo cccsssessesescsnenesesssesescesesesesescecsueseseseseseeeesenens Using Excel to create columns from the table ooo ccccsssesssssseseseseeeseseseseeesesceseseaeseseeneseneseseeeaneesenens ET l a T EE er S EA A A E ERE Scatter plot using by subcommand Ov rlaid graphs osani a EEE EAEE O ROA EAA O NELA A ORN Survey Estimation Accounting for Design Effects ANNEX I Stata Commands ANNEX I Questi nnaire cies fc ci cas th cseet ksi ace ans Ohh dae teen ce dad eevee cae ttle Dhue de die daa e ated vide USlietes ten e aS Stata 13 Sample Session Section 0 File Structure and Basic Operations for Stata 13 Stata 13 SAMPLE SESSION SECTION 0 File structure and Basic Operations for Stata 13 This section introduces the basic concept of levels of data the notion of cross sectional analysis and consequently the methods of data organization A brief description of the file structure of Stata is discussed It is essential that you read through this section before starting the cross sectional tutorial Overview When you open Stata 13 for the first time you will see five different windows within the main program the Results window in the center results of a command are displayed in t
10. arizon E Variables head 34 m 4 married arizoni Fase wife husb 24 f illiterat married arizoni ansi vige son daugh 12 m 4 single arizoni mwee Z son daugh 10 F 4 single arizoni Value Label vil son daugh 7 m illiterat single arizoni Notes E On the right side are two windows One is labeled Variables and the other is labeled Properties The Properties of the variable that is highlighted in the Variables window is showed in the Properties window If you select a different variable its properties are shown in the Properties window The variables properties are name label variable label type type of variable format value label name of the value label set attached to the variable and any notes that might be associated with the variable 13 Stata 13 Sample Session Section 0 File structure and Basic Operations for Stata 13 In the Data Editor some of the data are words and others are numbers Stata color codes the types of variables where string variable appear in red numeric variables without labels in black and categorical variables in blue where the value label is shown rather than the underlying numeric value Those variables with words in blue are showing the value labels for the values If you want to see the values rather than the labels click on the Tools in the Menu Select Value Labels Hide all Value Labels See the example below Change Mode to Edit Szal Variables Manager Value Labels
11. clustering and stratification We will use the svyset command to specify the method 1 Click on Statistics then Survey data analysis 2 Then click on Setup amp utilities then Declare survey design for dataset 3 Inthe Primary sampling unit box select cluster 4 Inthe Strata box select dist 5 Click on the Weights tab 6 Click on the radio button next to Sampling weight variable 7 Click on the drop down arrow for the Sampling weight variable box and select hhwgt 8 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK The Stata command is svyset cluster pweight hhwg strata dist vce linearized singleunit missing After running the command we see a summary of the command in the Results window pweight hhwgt VCE linearized Single unit missing Strata 1 dist SU 1 cluster FPC 1 lt zero gt 117 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation svy linearized total We can use the syvdesc command to look at the strata and PSU arrangement of the dataset 1 Click on Statistics then Survey data analysis Then click on Setup amp utilities then Describe survey data 3 We can specific a variable or just run the command to look at the complete dataset If we were interested to know which strata have only one sampling unit we could put a tick next the box labeled Display only the strata with
12. command is faster but the table command is more flexible tabulate one and two way tables of frequencies tabl produces one way tabulation for each variable tab2 produces two way tabulations of all combinations of the variables Let s compare the tabulate command with the table command to create two way tables Create a do file with all the proper commands at the beginning of the do file Refer to the do files you have already created You can copy several of the commands that you need and comments Remember to start the log file for this session Open the member file we created from Section 1 that 93 Stata 13 Sample Session Section 3 Tables and other Types of Analysis frequency row percentage column percentage head Age group 11 to 19 20 to 6 1275 86 2 22 47 25 8 06 90 9 26 44 184 25 63 68 15 4 0 0 00 83 0 00 0 55 38 46 TL 20 37 2 270 17 76 41 100 00 100 contains the age variable q1a age dta 1 2 3 4 File Open Select q1a age dta Click on Open Copy the command paste it into the new do file and add comments First do a simple two way table using the tabulate 1 ios From the menus click on Statistics then Summaries tables and tests then Frequency tables then Two way tables with measures of association The tabulate2 Two way tables dialog box opens In the Row variable box select ca2 In the Column variable box
13. do extension will be added to the name automatically 8 Click on Save The do file is now saved to disk Note that Stata color codes the text in the do file Blue text is a Stata command red is a string usually enclosed in double quotes subcommands are black comments are green The syntax colors can be changed by choosing Preferences under Edit from the Menu while in the do file editor We need to run all the commands from the do file to start the log and record the commands in the log file Block all the commands and click on the Execute Do button x There data file will be opened again since it will be part of the commands that are blocked We have opened the household member data file which is now the current file in memory Remember Stata only allows one file to be open at a time A key piece of information we need to know about a data file is what variables it contains We can find this out along with other information by using the Describe data command on the Data menu 1 From the Data menu select Describe data 2 There are several choices under this option Select Describe data in memory or in a file A dialog box opens There are several options in this dialog box With version 13 there is a choice in the dialog box to describe the data 22 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations in memory or a file on disk We will use the default In memory
14. drop _merge joinby prod using calories dta unmatched master __merge _merge check to be sure merge done correctly tab1 _merge compute total calories produced generate double cprod_tt qprod_tt calories add variable labels label variable qprod_tt Total production in kgs label variable cprod_tt Total calories produced 87 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation select only staple crops keep if prod 5 prod 6 prod 30 prod 31 prod 41 prod 44 prod 47 check to see that there are only 7 crops listed tabulate prod need to sum all calories produced by the household Using the collapse command collapse sum cprod_tt by district vil hh label variable cprod_tt Calories produced in staple foods describe verify you have the right average calories produced over whole sample summarize cprod_tt save the file save hh_file1 dta replace kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk Step 2 calculate adult equivalents based on age and gender Kk k k k k k k k k k k k k k k k k k kk kk k k kkk kk kkk kkk use c q1a dta clear generate ae 1 if ca4 1 amp ca3 gt 10 replace ae 84 if ca4 2 amp ca3 gt 10 amp ca3 lt 19 replace ae 72 if ca4 2 amp ca3 gt 20 replace ae 6 if ca3 lt 10 label variable ae Adult equivalents check the variable tabulate ae missin
15. file This means that the data from this file is now available for you to use Let s start with the data for Table IA Household Member Characteristics The data file that corresponds to this table is c qia dta To open this file perform the following steps 1 From the File menu select Open This will open the Open File dialog box 2 Ifyou have run the cd command you should see a 20 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Opening a data file The use command list of data files to be used with this tutorial Select the file c qia dta Click on the button to open the file The command appears in the Review window In the Review Window you will see the text use c qla dta clear 74 29 will be replaced with whatever the name of the directory is where you are working We want to create a do file to save our commands The command that was just executed appears in the Results window and the Review window Press lt PageUp gt The command which was just run now appears in the Command window Block the command and press lt Ctrl C gt to copy it Click on the button in the Tool Bar to open the Do File Editor Z7 and paste the command into this file lt Ctrl V gt or lt Right Click gt and choose Paste We want to copy the cd command as well Use the lt PageUp gt key until the cd command is in the Command window Copy and paste i
16. varlist exist thus rectangularizing the file the variable _fillin is added to the data _fillin is 1 for created observations and 0 for previously existing observations svy commands these are commands prefixed with svy and they pertain to commands used in analyzing survey data calculates and displays tables of statistics format varlist fmt formats numeric variables as follows number before the decimal indicates the length of the variable number after the decimal indicates number of decimal places g general numeric format 5 0g YH f fixed numeric format e g 5 2f e base 10 power strings are formatted as follows and can be 81 chars long its e g 10s Reshape notes The reshape command is particularly useful for files such as that shown in the following example Households were asked about the number of livestock owned for three types of livestock coded 330 331 and 335 To save on data entry time only those entries reporting any livestock were entered Missing livestock codes in the file therefore means that the household did not own the livestock associated with the code The file looks like this 122 Stata 13 Sample Session Annex I I Survey Instrument hh animcode num 206 331 70 217 331 65 217 335 8 221 330 1200 221 331 200 The above file could have been organized such that each household has only one line of information and the three animal types appear as three different v
17. you will get an error message To save to a file that already exists on the hard disk an additional subcommand must be added replace save hh_file2 dta replace We have created two files hh_file1 dta which contains the calorie production data for all households and hh_file2 dta which contains the adult equivalent data for all households We need to combine these files matching by district village and household to get both sets of data into one file To do this we use Combine datasets Merge two datasets under the Data menu choice We noted earlier that key variables are required for any merge When you re joining two files which are at the same data level as we re about to do it may not seem important to include key variables but it is The key variables determine which observations are to be combined Note You should never use Combine datasets without Key Variables because without them you have no guarantee that the program will combine the cases in the manner that you wish The command will execute without any warnings or error messages but the results may be incorrect 76 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation At this point if you have not closed Stata hh_file2 dta is still the working file A very important point Stata cannot merge two datasets unless they are both sorted in the order of the key variables One way to check to see
18. 01 28059 29 119 Stata 13 Sample Session Annex I I Survey Instrument Annexes ANNEX I Stata Commands This annex provides a brief reference guide and to explain the various functions of the Stata commands most commonly used This annex was developed by Ellen Payongayong The commands in the table below do not contain the full Stata syntax Note that commands can be abbreviated In the Help Syntax Viewer the syntax explanation will show how much of the command must be typed e g Summarize can be shortened to su or sum In this Help viewer the letters that are required for the command are underlined eg Se re save filenameZ saves current file in memory into filename if filename already ee a exists stata will not let you overwrite it save filename2 replace saves current file in memory into filename2 overwriting any file rere Sitter Prviter Eves cect tat trenty ated inanet save replace saves current file in memory into filename of that which is browse brings up the same data editor as in edit but will not allow you to change data describe gives a description of the data file number of observations number of variables list of variables variable type and width variable labels if any summarize gives basic summary statistics number of valid observations mean standard deviation minimum value and maximum value list lists observations keep retains in memory only those variables
19. AN n Types of files used by Stata and their extension names Dat file S sene n A AA RIS A BORER A SAA AREA AES LOG THOS ies cice cn Save eerie retain ea The log using command eee eeeeeneeee The emdlog using command 0 The log close command cceceeerees Do files iena AA SNIE Adding comments to the do file Th cdoedit COMM ANG isemi seni a EERE E E NAAN ETA N G Discussion of the Windows used in STATA The Do file Editor nrinn nnna A Rn R A T NARA R ON A A The Data Editor WINdOW ceciren Te REEE E ETE E N E A N OO EE adi sve EREE The edit command Saving the Stata Data File The save replace comman4d The Brower Window 0 cc08 The browse command The Stata Results Wir ows ii 5cs 56s 305 5 50 ek shea Es PRO tg hs EE OEE eai The Command Window 62 5 23 05 is Bhs eis ass Bas eee Bess Suess Se avn cc etna ou sa links a selste ube dhe does The Viewer Stata Graph window Summary of the Basic File Types SECTION 1 Basic functions Stata files Descriptives and Data Transformations Introductions sneer 8 iss g05ch dR sas statioes ds Nieto Sa a oe eae asta oo eSNG hoe NG ees cB ao Data files and the working file Working Directory oo cccccceesereesteeetenees The Cd COMMANG cece Opening a datatile ccc corel nw fae enn natn aed Gatien see nutans teed een R ete ated en ee The USE COMMA 4055 schs desssessossesvadh doses babes Gol doc
20. Before we close down the session we need to close the log file that has been recording the commands and output The command to close the log file is We can type this command in the Command window and run it and then copy and paste the command in the do file Before exiting Stata save the do file The file contains all 52 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations of the commands It is useful to keep this file so you can rerun the commands if you want review the commands and the output that is produced If you have not yet saved the file follow these instructions Otherwise click on the al L Save icon on the tool bar 1 Ifyou have not saved the do file make the Do file Editor the active window using its icon on the Windows taskbar 2 From the File menu select Save As 3 Enter the filename session1 The do extension will be added to the name automatically 4 Click on Save To exit Stata switch back to the Stata window 1 From the File menu select Exit A dialog box will open to say that Data have been changed without being saved Do you really want to exit 2 Click on Yes We do not need to save the newly created categorical variable We will not be using it again If you want to look at the log file that we just created open Stata 1 From the File menu select View A dialog box will open Choose File to View 2 Cl
21. Copy graph You could also right click on the graph itself and select Copy Graph 2 Open your word processor and click on Paste or lt right click gt and Paste You will not be able to edit this graph other than the size placement wrapping of text and other basic aspects allowed by your word processor unless you convert it to a Microsoft office drawing You can edit the graph in Stata first before copying into Word You can also save a Stata chart to a file 1 Click on File then Save A dialog box opens where you can type the name of the file The default extension is gph which is the format that Stata recognizes as a graph file If you save the file with a gph extension you can then open the graph again within Stata In the filename box type Cashew_trees and click on Save 3 Copy the command from the results window switch to the do file editor and paste the command You can also save the graph into several different formats such as windows metafile wmf Click on the drop down arrow next to the box labeled as Save as Type to see the different formats Word processors can import a graph with the extension wmf or tif into a graphic box 113 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation Scatter plot using by subcommand Overlaid graphs Once the graph window has been closed you cannot reopen it unless you have saved the graph to a file You can rerun t
22. Do file Editor active and select Save from the File menu or click on the diskette symbol on the Tool Bar A file created from the Do file Editor is called the command file It is a file containing only commands it never contains any of the data you may be analyzing with the commands You must save your data separately as described in the following section We suggest that you use the default extension of do when naming command files Examples of file names are Rep7 do dem all do and section1 do By storing your commands to a do file you can retrieve look at or modify sets of commands and rerun them To retrieve a do file into the editor open the Do File Editor pull down the File menu and select Open or you can click on the yellow file folder Bln the tool bar in the editor Select the file you wish to open and click on Open Once you have opened a specific file you can use the commands from the file without having to recreate or type them again If you make changes to the command file that you wish to keep make sure you save them to disk again Caution From Windows Explorer if you double click on a do file the Stata program will open and run all the commands in the do file immediately The do file will not be opened To opena do file from Windows Explorer right click on the file name and choose edit The application STATA will open and the do file will be opened in the Do File Editor When you
23. Enumerator Areas SEA were defined The primary sampling unit PSU for this sample is the SEA To identify each SEA as being unique the three variables district CSA and SEA must be combined into one variable District has 3 numbers CSA has 3 numbers and SEA has 2 numbers To create a new variable with these variables one must multiply the district variable by 100 000 add CSA multiplied by 100 and add SEA The Stata command is gen float cluster1 dist 100000 csa 100 sea We want to change the format of this variable so that we can easily read it to verify the variable has been created 116 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation correctly Use the format command format cluster1 9 0f Clusters may further be sampled in groups which are called strata The Zambia example uses province district as the strata Strata are considered to be statistically independent and can be analyzed as such A weight has already been calculated for each household The variable which contains this value is called hhwgft We need to compute the cluster variable We can use dist for the strata variable since it already contains the province value as part of the district code Close the browser and use the gen command to create the variable cluster1 To be able to use the survey commands we must first define the stratified random sampling method that was used to account for weighting
24. The keep if COMMANG 25 52 2 2 enne Sec jesende Ak ves cnssvehst d a death de dee dette uveredbaackedes Create a new file which is a household level file rather than a household product level file The collapse COMMAS lt 05 355 seh R E eeates en hee toe Step 2 Generate a household level file containing the number of adult equivalents per household 69 Create a variable with the adult equivalent for each person Th generate ss IE COMMANG cvcccisccteesSses a0 tess E AE AANE codek eed oh cad ces Sov ERESSE okey Saas deat eanatboadia deeded cane The replaces ic 1F COMMAND ouesse neste dire ers vero Meets tas ets ETERRA Replace missing values with a mean value oo ccc csesceesesesesceeeseseseseeceueseseseecsteneeneaeaeeeeees Calculate the adult equivalents for the household 0 ccc cece eseseeeeseseseseeeseeneseseseaeseeeesenens The collapse ComMan dive ecrin aiian aE nin ied eaten cedeenta tenn ATE deni iain Step 3 Merge the two files created in steps 1 amp 2 to compute calories produced per adult equivalent 76 THHEsMErGe COMMANG ae oinn eres yen AS Mie Bene e T aaa Aetna aed nace bet arias Calculate the total calories produced per adult equivalent per household for the year Computing quartiles sesban annn Riba red a a a a aa iiaa Th xtilecommand Using yc 35 2 5 8 8 anra naan a a a aea The for z in num 1 3 looping command 0 2 eee csesesssseseseseessseseseseseuesesenessaueseseseneseseeseseseseresesueseseneseseecs
25. cd C user Documents StataTraining data capture log close log using session4 append session 4 copying Tables and Graphs to a word processor tasks Your name date version 13 clear all macro drop _all Save this do file under the name session4 do We are now ready to open the household file that contains the tree ownership variable c hh dta 1 Click on File then Open 2 Select c hh dta and click on Open 3 Paste the command from the Review window to the do file editor Remove the folder reference Create the Histogram chart using the variable H57 number of trees owned 4 Select Graphics then Histogram 5 Inthe Variable box select H57 Number of cashew trees Note you can specify whether the variable is continuous or discrete 6 Under Bins V check the box next to width of bin and type 20 7 Under Y Axis click on the radio button next to Frequency 8 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK The Stata command is histogram h57 bin 20 frequency Another window will open and you should see a histogram chart that looks like the one below 112 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation Frequency 60 40 0 100 200 300 400 number of cashew trees To copy this graph to your word processor 1 Inthe Graph window click on Edit
26. commands available for summarize and tabulate refer to the Guide for STATA References S Z 33 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations oO oO 09 oO oO N gt z 0b loy 2 LL Q op at oO 0 20 40 60 80 age The list command You may want to look at the data selecting only specific cases rather than scrolling down through the data set to find a specific case or cases The list command gives you the option to select all or specific cases 1 From the Data then Describe data menu select List data list List values of variables Main by f in Options Summary Advanced Variables leave empty for all variables Column widths Default Compress width of columns in both table and display formats Use display format of each variable C Overtide minimum abbreviation of variable names 8 Characters L o Truncate string variables 10 Characters C Do not list observation numbers C Display all levels of factor variables Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations The list dialog box list List values of variables has 5 tabs where you can set specific parameters for the data that you want to list On the Main tab you can specify the variables to be listed or leave it blank to list all variables The default column width separates each variable b
27. comment to explain what you have done The Stata commands are generate double cprod_ae cprod_tt ae 365 label variable cprod_ae Calories produced per adult equivalent per day Computing quartiles Before we can produce the table we want we have to o create one more variable denoting which calorie The xtile command using if production quartile each household falls into within each district The Stata command to use is called xtile The definition of xtile is that it is a command that categorizes a variable into the specified quantiles and places the information into a new variable You can go to the Help to search for this command to get a detailed explanation Examples can be found under the Examples heading Since we want to divide the data into quartiles within each district we can use the if subcommand e g 1 Click on Statistics then Summaries tables and tests then Summary and descriptive statistics then Create variable of quantiles 2 The dialog box opens The xtile Create variable containing quantile categories dialog box opens In the New variable name box type q1 In the Expression box type cprod_ae In the Options section select 4 quantiles Click on the if in tab and in the If expression type district 1 7 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok DN The command is xtile q1 cprod_ae
28. format where Stata chooses the format c optional along with either e f or g will display a comma e g 1 000 03 Variable label Label describing the variable Value label If the variable has value labels the name of the label appears in this column Stata assigns a name to the label which contains the values and labels The label is then applied to the variable More will be said about value labels later There are several ways to view the labels and values for variables If you wish to see what labels have been defined for specific values for the variables that have value labels as indicated above you can run the command to create a codebook of the labels From the menus 1 From the Data menu select Data utilities Label utilities 2 Select Produce codebook of value labels towards the end of the dropdown menu 3 We will accept the default selections Click on the icon to copy the command to the clipboard and then click on Ok 4 Switch to the do file editor and paste the command In the Command window you could have also typed 25 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations more The label list command labelbook to obtain the same results This command describes only those variables with value labels It is a good command to use to document the value label names This output is quite long You will see more at the bottom of the Results
29. have no value for ca2 by ca2 sort summarize ca3 ca2 head Variable Mean Std Dev ca3 Al 5201 14 12719 ca2 wife husb Variable Mean Std Dev ca3 33 1871 11 80466 ca2 son daugh Variable Mean Std Dev ca3 8 133844 5 797507 ca2 mother fa Variable Mean Std Dev ca3 48 16667 22 09449 ca2 other rel Variable Mean Std Dev ca3 12 55245 10 06785 ca2 Variable Std Dev ca3 12 24745 Data Transformations After examining the results of the descriptive statistics you will often want to do data transformations A data transformation is an operation that takes an existing variable and either changes the values in a systematic way or uses the values to calculate a new variable The following example shows a common data transformation the 42 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Converting continuous variables to categorical variables The generate command The replace command The label variable command The label define command First method The generate command conversion of a continuous variable to a categorical variable The information we received from the summarize command is interesting but it might also be useful to see the actual distribution of the ages into groups or categories so we can tell
30. have opened a do file in this manner STATA automatically executes the command doedit nameofdofile do which you can see in the Results window Stata stores your data in a data file In addition to the values themselves a data file contains such things as variable labels and value labels formatting information missing value specifications notes etc Before you can do any data analysis in Stata 13 you must first tell Stata to open a Data file Select File from the menu select Open highlight a data file example c hh dta and click 12 Stata 13 Sample Session Open Data Editor window The edit command Dats itor a eaaa E Section 0 File structure and Basic Operations for Stata 13 The command is immediately run The data in the file are now available to be viewed in the Data Editor window In the Review window you see the command that opened the data file In the Variables window you see the list of variables that are available on There are 2 methods that you can use to look at the data The first opens the file in the Data Editor window In this window you can manually change the data so be careful when you use this method The other method opens the data in a browser window where you cannot change any of the values but you can sort and look at the data 1 The first method to view the data is to open the Data Editor window Click on the Data Editor button Es or in the Command window you can type
31. is named session1 txt and commands will be appended to anything that already exists in this file To close the log in the Command window type log close J Stata 13 Sample Session 3 Do files Adding comments to document commands in the do file Section 0 File structure and Basic Operations for Stata 13 Reminder The log file that is written in SMCL format can only be opened in Stata It is a specific format as mentioned earlier If you want to share your commands and results from the log files with another person who might not have Stata you can use the translate command to save the log file in the ASCII text format with the extension of log Any editor or word processor can open this file However in the word processor the font must be set to a fixed font such as Courier New Otherwise the output will be difficult to read A do file contains commands that Stata can execute Stata commands Extension do The do file is created in the Do file Editor The user can type commands or paste commands into the editor Other ways to create a do file are a You can create a log file that contains only the commands using the Cmdlog command see above b You can select the Review window click the right mouse button and select Save All The extension do will be automatically added to the file name you enter into the File name box c You can copy the command to the clipb
32. only reverse the variables rename unit p1a 63 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation The drop command Now that the kilograms have been calculated we need to look up the value of a kilogram in calories for each product This information is in the table lookup file called calories dta This file has two variables product prod and number of calories calories per kilogram The key variable is product prod In order to add the calorie conversion variable to the working data file we need to do another merge with keyed table lookup joinby This time the key variable only needs to be the product variable The data file has already been sorted by product see the previous merge so we don t need to sort it again Stata will reuse the merge variable again with the next join we do so we should drop this variable first since we no longer need it The command to delete a variable is called drop The Stata command is Now we are ready for the next join 1 From the Data menu select Combine datasets then select Form all pairwise combinations within groups The joinby Form all pairwise combinations within groups dialog box opens 2 To fill in the box labeled Filename of dataset on disk click on the Browse button Select the filename calories dta and click on Open 3 In the box labeled Join observations by groups formed from specific variables select p
33. open the file 69 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Create a variable with the adult equivalent for each person The generate if command The replace if command 3 Copy the command from the Review window and paste it into the do file editor delete the folder reference and add a comment to explain what you have done We want to calculate the adult equivalents for the household The adult equivalents value helps us to evaluate whether the household is producing enough food to sustain the household The variable is calculated using the age and the gender of each member of the household Some members need more food than others and these variables help to assign that value For example the adult equivalent value on average for a female 10 to 19 years old is about 84 as many calories as a male 10 years or older who is given a value of 100 Children under 10 need only 60 as many calories as the typical male 10 years and older The child male or female under age 10 is assigned a value of 60 adult equivalents For each person observation in the member file we need to look at the variables sex ca4 and age ca3 to calculate adult equivalents There are many different possibilities of values that could be assigned for adult equivalents We will use a very simple table for this exercise The rules we will use for calculating adult equivalents for thi
34. or a table has been produced it can be printed or incorporated into reports prepared using word processors or publishing programs Incorporating tables from Stata can be done using the copy and paste procedure You should save the log file as well in case you need other tables that were created Find the table in the session3 scml file that showed the count of the Tables relation of head to age group cross tabulation 1 Click on File then Log then View In the Choose File to View dialog box click on the Browse button 2 Select session3 smcl Click on Open Then click on Ok 4 Locate a table that you want to copy to your word processor Use your mouse to block the table 5 Press Ctrl C copy This key sequence copies what you have blocked 6 Now open your word processor software if it is not already open 7 Place your cursor where you want the table to appear Press Ctrl V paste to paste the table 8 In your word processor block the text that you just pasted Now change the font to a fixed font e g Courier New or Letter Gothic Click on Format Font and select the font The size of the font may need to be adjusted depending on the margins of your paper The default will be 12 and you may want to select 10 or 9 or 8 iv Below is an example of a table copied into a word processor before the font is changed to a fixed pitch relation to age gp head Otol0 11to1l9 20to 60 61 and older Total beh
35. or cases specified drop discards from memory all variables or cases specified tabulate generates one and two way frequency tables tabl generates one way table for each variable specified after the command log using filename saves all commands and related output into specified file the default format is SMCL for Stata Markup and Control Language file is given extension smcl log using filename text saves all commands and related output into an ASCII file with extension txt log off on close off temporarily suspends the log file switches it off on switches the log on and close closes the log file log using filename append adds subsequent commands to an existing log 120 Stata 13 Sample Session Annex I I Survey Instrument Command Description log using filename replace saves all commands and related output into the specified file overwriting said file if it already exists By opening a log file with cmdlog instead of log you record only what you type in the command window results are suppressed The same basic syntax applies for both cmdlog and log You can open both an smcl file and a log file help command accesses help feature of Stata sorts observations in ascending order according to the specified variable note 1 allows you to enter notes about the dataset note varname cee 2 allows you to enter notes about variable varname notes 3 calls up all notes in memory Notes ar
36. screen more indicates there is more information to be displayed but the display has paused so that you can view the first part of the output You will need to click on more several times to see the complete output To continue to the next screen you can click the lt spacebar gt or you can click on the more in blue font in the Results window or you could also click on the green button on the tool bar This button only shows on the icon bar if there is more output to be seen in the Results window If you wish not have the output displayed one screen at a time you can turn this feature off The command is You can include it at the beginning of the do file so that when you want to run the do file another time more will be turned off If you want to stop the listing from completing you can click on the However if you do that you will not get a complete listing of the labels for the values The value label is described with a list of the labels that have been defined and the variable s the label has been attached to You can select specific variables to look at those labels only From the menus 1 From the Data menu select Data utilities Label utilities 2 Select List value labels Select district and vil click on and switch to the do file editor to paste the command Switch back to the dialog box and click on Ok The listing shows you what values are assigned to a label Note A label na
37. select age_gp Under Cell Contents click in the box next to Within column relative frequencies to puta v Click in the box Y next to Within row relative frequencies Click on the Copy button switch to the do file editor paste the command switch back to the dialog box and click on OK The Stata command is tabulate ca2 age_gp column row Below is the output 60 61 and ol Total 296 41 343 30 11 95 00 00 3 83 67 2257 280 5 310 oe 1 61 00 00 59 10 20 20 39 31 0 718 32 0 00 00 00 94 0 00 47 24 5 1 6 33 16 67 00 00 80 2 04 0 39 16 2 143 19 1 40 00 00 55 4 08 9 41 628 49 ly 520 32 Bee 00 00 00 100 00 00 00 94 Stata 13 Sample Session Section 3 Tables and other Types of Analysis The table command Let s use table to produce a similar table However with the table command we cannot ask for row or column percentages the table command is generally used for summary statistics Frequency and Totals are possible to select from this command 1 From the menus click on Statistics then Summaries tables and tests then Other tables then Flexible table of summary statistics The table Flexible table or summary statistics dialog box opens 2 Under the Main tab select ca2 in the Row variable box 3 Click in the box VY next to Column variable and select age_gp in the box below 4 Inthe Statistics 1 replace None with select Frequency from the drop down box 5 Under the Options tab
38. the beginning of the do file as well set memory 70000 sets the size of memory set matsize 100 limits number of variables that can be specified in an estimation command 123 Stata 13 Sample Session Annex I I Survey Instrument ANNEX II Questionnaire Socio Economic Survey of Family Sector Farms in the Province of Nampula Angoche Monapo e Ribate July August 1991 Departamento de Pre os e Mercados Food Security Project Name of Household Head Household Number HH Aldeia VIL Distrito DIST Subset of questions from original questionnaire Filename c hh dta I HOUSEHOLD CHARACTER STICS H1 1 How many persons are in this household H4 4 Has your family always lived in this village l yes 2 no H8 8 Is your family registered as deslocada l yes 2 no H19 19 Do you presently have lands in fallow l yes 2 no H21 21 What is the total area of these fallowed parcels hectares H24 24 Do you have lands that you have completely abandoned l yes gt question 25 2 no gt question 27 H25 25 What is the total area of these abandoned lands hectares H26 26 What was the principal motive for abandoning these lands 1 no security 2 lands lost fertility 3 lack of labor 4 insect attacks 5 other We would like to ask you about the food crops you grow H29 29 Over the last five years have you increased or decreased the amount of land in food crops 1 increased 2 decreased 3 no change H31 31 Du
39. tick Add row totals and also tick Y Add column totals 6 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Submit 7 Write a comment in the do file to explain what the command does The Stata command is table ca2 age_gp contents freq row col Note the word contents can be abbreviated to c e g c freq The results are relation to Age group head 0 to 10 TI to 19 20 to 60 61 and older Total head 6 296 41 343 wife husband 25 280 5 310 son daughter 503 184 31 718 mother father 5 1 6 other relative 70 99 16 2 143 Total 573 270 628 49 1 520 We want to put something in place of the blanks for the cells with no data 1 Go back to the dialog box and under the Options tab tick Show missing statistics with period 2 Click on the Main tab and tick V Superrow variables Using the drop down arrow select district 3 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok 95 Stata 13 Sample Session Section 3 Tables and other Types of Analysis Comparison of the commands summarize tabulate and table 4 Write a comment in the do file to explain what the command does The table now shows a period full stop where there are no data and there are subtables for each district The following is a comparison of computing averages using sum
40. to open the file which closes the current file we have open to sort it then we can save it Then we can reopen the production file Once we have sorted the conver dta file and saved it it will never need to be sorted again Therefore we do not want to put that code into our do file We can just write a comment that the conver dta has been sorted correctly Our production file the current working file that is in memory must be sorted also by the variables we want to match by Note that the unit variable is named pla To sort the cases 1 From the Data menu select Sort The sort Sort data dialog box will open 2 There are two choices at the top of the dialog box Standard sort ascending and Advanced sort mixed ascending descending The default is Standard sort 59 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Rename any key variables in both files to the same name ascending We do not need to change anything 3 Inthe Variables box select prod and pla 4 Click on the copy icon and then click on Ok 4 Switch to the do file editor and paste the command The Stata command is sort prod pla Let s look at the two variables using the tabl command We can type in the Command window tab1 prod pla There are 1 693 cases with a product code Many of the cases are the same product i e maize has 192 cases For the tabulation of pla we see two values that
41. was stored in the STD00000000 tmp temporary file If you do not block any lines and move your mouse over this icon you will see Execute do Clicking on this icon Stata will run the complete do file showing all the commands and the output In the Review window you will see the command do session2 do telling Stata to run the do file completely There are key strokes you can use as well to run the do file while in the do file editor Click on Tools from the menu to see the different commands you can use to execute commands from the do file If you want to execute all the commands and not see any output in the Results window the command to use is run in place of do Your SESSION2 DO should look similar to lines below your documentation comments may not match exactly what has been included in this listing Comments start with at the beginning of each comment and ending each comment with a You can also just use an if the command is one line change to the working directory modify for your computer cd C Users beaverm Documenits StataTraining data define the log file to capture output name of log file is log_session2 capture log close log using log_session2 smcl append 86 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation STATA do file section 2 Cross sectional Stata Tutorial Purpose Calculate food production in calories p
42. years ago has the marketing of these products been more difficult or easier 1 more difficult gt question 66 2 easier gt question 67 H66 66 If more difficult why 1 fewer buyers 2 transportation problems 3 security problems 4 low prices 5 lack of consumer goods 6 other 125 Stata 13 Sample Session Annex I I Survey Instrument H67 67 If easier why 1 more buyers 2 better transportation 3 better security 4 attractive prices 5 more consumer goods 6 other H83 83 Does your family usually receive traditional gifts or participate in exchange relations l yes 2 no H84 84 If yes how often 1 only when there is a lack of food 2 only during feasts and rituals 3 frequently XI TYPICAL CONSUMPTION PATTERNS H86 86 How many meals did these people have yesterday Number of meals H89 89 Do you consider these meals adequate to maintain the health of all the household members l yes 2 no We would also like to ask you about your diet during the hungry period January to May H91 91 How meals do you customarily prepare daily during hungry period H92 92 In general are these hungry period meals adequate to maintain the health of all household members l yes 2 no H96 96 During the hungry period was there always food available to purchase from the market or from your neighbors l yes 2 no 126 Stata 13 Sample Session Annex I I Survey Instrument I HOUSEHOLD CHARACTERISTICS Table IA Household C
43. 10 Note you must use the ampersand symbol amp not the word and 43 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations The replace command 7 Click on the copy button switch to the do file editor paste and switch back to the dialog box and click on Ok The Stata command is generate byte age_gp 1 if ca3 gt 0 amp ca3 lt 10 Stata will indicate in the Results window how many missing cases were generated 949 missing values generated That means that from the total of 1524 cases 949 were not assigned a value Now that the new variable has been created another command is used to assign the codes for the cases with no values for this new variable The command is the replace command 8 From the Data menu select Create or change variables then Change contents of variable The replace Replace contents of existing variable dialog box opens 9 Inthe Variable box select the name of the variable that was just created age_gp 10 Type 2 in the New Contents box 11 Click on the if in tab 12 Inthe If expression box type in ca3 gt 10 amp ca3 lt 19 13 Click on the copy button switch and paste in the do file editor switch back and click on Submit The dialog box remains open and the command is run The Results window indicates how many changes were made 271 real changes made 14 Now make the changes to assign values to the other
44. 2 generate tax var var 10 Collapsing across variables 83 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation foreach qtr of numlist 1 4 local m3 qtr 3 local m2 qtr 3 1 local m1 qtr 3 2 generate incgtr qtr inc m1 inc m2 inc m3 This command computes the quarterly income variables incqtr1 incqtr4 using the foreach command Display the final output table We can now display a table showing the average caloric production in quartiles for each of the districts 1 From the menus click on Statistics then Summaries tables amp tests then Summary and Descriptive Statistics then Summary statistics The summarize Summary statistics dialog box opens 2 Inthe Variables box select cprod_ae Click on the by if in tab 4 Placea Vv in the box next to Repeat command by groups 5 Inthe box below Variables that define groups select district quartile 6 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK 7 Add a comment to explain what you have done 0S The Stata command is by district quartile sort summarize cprod_ae You should note that the mean for the 2nd quartile in Monapo is 2 539 364 The output from the summarize command gives you the numbers necessary for the table However the output is difficult to read The
45. 2220664756392 3 001349748747086 r skewness vr kurtosis r sum 32516 10000002384 r min 5 r max 81 r pl 1 E ps I r p10 3 r p25 7 r p50 16 r p75 32 r p90 48 r p95 57 r p99 69 You can use these values stored in memory to perform calculations For example to subtract the mean of ca3 from cas generate ca3_mean ca3 r mean Using the information in memory eliminates the need to type specific numbers and will give you more accurate values These scalars are only available until the next command is run Since the variables ca1 work on a farm or not ca2 relation to head ca4 sex cad level of schooling and 31 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations The tabl command ca6 marital status are categorical the values representing categories we will run a tabulate command To run a tabulation do the following 1 From the menus click on Statistics then Summaries tables amp tests then Frequency tables then Multiple one way tables The tab1 Multiple one way tables dialog box opens 2 Click on the drop down arrow for the box for Categorical variables to select the variables cal ca2 ca4 cad ca6 3 Click on the copy button switch to the Do File Editor and paste the command then switch back to the dialog box and click on the Submit button 4 The command will be executed You w
46. 529 2443 454 3134 742 12674 86 92 Stata 13 Sample Session Section 3 Tables and other Types of Analysis STATA 13 SAMPLE SESSION SECTION 3 Tables and Other Types of Analysis Tables Using the table command you can calculate various statistics and present them in a variety of ways that are completely under your control table allows you to choose how you want to assemble variables and statistics for display in rows columns and super columns or super rows A super column or super row has a variable nested below it Variables can be stacked one on top of the other Variables can be nested meaning that all of the values for one variable are displayed below the individual values of another variable With the table command you can manipulate table structure content and presentation format With this command there a few limitations a up to 4 variables can be specified in the by b up to 5 statistics can be displayed in each cell c the sum of the number of rows columns super columns and super rows is called the number of margins A table may contain up to 3000 margins e g a one way table may contain 3000 rows a two way table may contain 2998 rows and 2 columns or 2997 rows and 3 columns and so forth Commands that produce similar results are tabstat displays summary statistics for a series of numeric variables in a single table tabsum produces one and two way tables of means and standard deviations this
47. 7546 224 4898 1806 867 gt district monapo quarts 2 Variable Obs Mean Std Dev Min Max cret_ae 2T 2239 088 199 4202 1888 33 2554 892 gt district monapo quarts 3 Variable Obs Mean Std Dev Min Max cret_ae 27 3343 003 461 9159 2685 971 4303 122 gt district monapo quarts 4 Variable Obs Mean Std Dev Min Max cret_ae 27 7619 101 395 7 135 4359 737 20873 97 gt district ribaue quarts 1 Variable Obs Mean Std Dev Min Max cret_ae 30 1251 391 358 8783 429 2929 1790 432 gt district ribaue quarts 2 Variable Obs Mean Std Dev Min Max 91 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation cret_ae 30 2171 697 205 3644 1835 298 2566 006 gt district ribaue quarts 3 Variable Obs Mean Std Dev Min Max cret_ae 30 3165 192 330 2283 2578 604 3731 045 gt district ribaue quarts 4 Variable Obs Mean Std Dev Min Max cret_ae 29 5828 97 1632 9 3825 879 9464 901 gt district angoche quarts 1 Variable Obs Mean Std Dev in Max cret_ae 29 929 4182 388 3228 207 9077 1395 962 gt district angoche quarts 2 Variable Obs Mean Std Dev in Max cret_ae 29 1718 789 166 1601 1447 059 1984 059 gt district angoche quarts 3 Variable Obs Mean Std Dev in Max cret_ae 29 2442 247 347 8035 L997 11 3063 996 gt district angoche quarts 4 Variable Obs Mean Std Dev Min Max cret_ae 28 5022
48. Manage Value Labels 3 Hide All Value Labels wife husb 69 head 37 wife husb 26 son daugh 7 s son daugh 3 head 46 wife husb 32 son daugh 14 Y Filter Observations Sonan ani head 67 wife husb 76 head 34 wife husb 24 netia amp 2 ves enn dannh 1 Selecting this option will show the numeric values for the categorical variables rather than the labels If you want to hide variables or see only certain variables go to the Variables box on the right and remove the tick mark on the left to not show specific variables Look at the icons available from the data editor You can open a data file directly within the data editor rather than opening it first in Stata If you open a file while in the data editor the command will appear in the Results window as well as in the Review window The data editor can remain open while you are working with the data Within the data editor you can change from being able to edit the data to only browsing the data To switch to browse click on the icon lt 3 You can also open the data file from the main Stata window in browse mode rather than edit mode Unless you need to change values it is best to open the data in the browse mode to prevent accidental changes to the data All of the data manipulations that are available from the main window are also available within the data editor If you want to sort the file in a specific way you can click on Data Sort A 14 Stat
49. OK The Stata command is recode h64a h64b h64c h64d h 4e h64f h64g h64h 2 0 The values in the h64x variables are now 0 or 1 yes We are ready to count the number of crops that increased in sales 1 Select Create or change variables from the Data menu 2 Select Create new variable extended The egen Extensions to generate dialog box opens 3 Under the Main tab type the name of the new variable in the Generate Variable box ncrops 4 For the egen function box scroll down and highlight Row total 5 Inthe box for Generate variable as type select Integer 6 Click on the Egen function argument Variables box type h64 In Stata we can use the asterisk to indicate to Stata to use all variables that start with h64 6 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK The Stata command is egen int ncrops rowtotal h64 Now you can do a frequencies on the new variable tabulate ncrops This table tells you how many households increased salesl on the number of crops asked about We see that 136 101 Stata 13 Sample Session Section 3 Tables and other Types of Analysis The tabstat command 2 Multiple response households did not increase sales in any crop On the other extreme two households had increased sales on all crops asked about ncrops 8 You must ask yourself if the data are reasonable Can a househol
50. Part 3 Each part of the survey is entered into a different data file To combine Part and Part 3 where both sets of data are at the household level we would use the merge command Joinby datasets This type of merge combines datasets horizontally matching all pairwise combinations possible An example is a set of data on parents and a set of data on children Joinby would match the parents to every observation of the children within that family The key word unmatched is used and within parentheses the type of join is specified There 56 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation steps l are four types of joins none all unmatched observations are ignored this is the default i e if there is not a matching observation in both files the observation is dropped from the final dataset both unmatched observations from the master or file that is in memory and using file that is not in memory data are included master unmatched observations from the master data are included but not unmatched observations from the using file using unmatched observations from the using data are included but not unmatched observations from the master file Cross datasets In this type of merge the first observation in the first file is joined horizontally with every observation in the second data set The second observ
51. STATA 13 SAMPLE SESSION Cross Sectional Analysis Short Course Training Materials Designing Policy Relevant Research and Data Processing and Analysis with STATA 13 for Windows 1st Edition Margaret Beaver Department of Agricultural Food and Resource Economics Michigan State University East Lansing Michigan January 2014 StataCorp 2013 Stata Release 13 Statistical Software College Station TX StataCorp LP Stata 13 Sample Session Section 0 File structure and Basic Operations for Stata 13 Components of the Cross Sectional Training Materials Section 0 Introduction to the Window structures for STATA 13 Stata Review Results Command Variables and Properties Windows as well as the Do File Editor This section must be read before starting the sample session Section 1 Basic functions Section 2 Table Lookup amp Aggregation Section 3 Tables amp Multiple Response Questions and Other Useful Commands Section 4 Graphs tables publications and presentations how to bring them into word processor and use of Survey commands Annexes I Frequently used Stata commands II Several pages from the socio economic survey of the smallholder survey in the Province of Nampula Mozambique NDAE Working Paper 3 1992 References to papers discussions levels of data On the Food Security Group web site at MSU there are several survey research training materials which you might find helpful The website is htt
52. a 13 Sample Session Section 0 File structure and Basic Operations for Stata 13 Exercise dialog box opens where you can specify the variables you want to use to sort the data Below is a snapshot of the options available to manipulate the data within the data editor from the Data menu These options will be discussed later in the tutorial Edit Data Tools Describe data Create or change data Variables Manager Data utilities Nn VV A WN p If you want to change the definition of a variable that can be done in the Variables Manager A new dialog box opens Variables Manager erns Enter filter text here Drag a column header here to group by that column Variable Properties EE T Neme household member number does this person work relation to head age sex level of schooling marital status where entered Reset Apply If you want to change a value directly within the data editor you can just type the new value The command that changes it is written in the Results window as well as in the Review window Exercise Change the value to 1 in the hh column where hh 2 Switch back to the Stata window You will see a command that has been run that replaces the value What do you see 15 Stata 13 Sample Session variables Exiting the Data Editor Saving the Stata Data File The save replace command C The Brower Window The browse command Se
53. a single sampling unit 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK Once the survey design has been specified and the file saved it is no longer necessary to specify it again The specification is saved with the data file We can use the svytotal command to look at the total estimates 1 Click on Statistics Survey data analysis 2 Then click on Means proportions ratios totals then Totals 3 Inthe Variables box select maisea ricea milleta sunfa 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Submit maizea ricea milleta sunfa running total on estimation sample Survey Total estimation Number of strata Number of PSUs 69 394 Number of obs 6601 Population size 807414 Design df 825 Total Linearized Std Err 95 Conf Interval maizea ricea milleta sunfa 649230 9 14472 95 61770 91 24319 15 25105 89 599840 3 698621 5 2360 009 9830 125 19115 77 7346 125 47318 95 76222 87 3418 858 T7593 26 31045 04 118 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation svyset _n pweight hhwgt pweight VCE hhwgt linearized Single unit Strata 1 SU 1 FPC T svy linearized total missing lt one gt lt observations gt lt zero gt 10 11 12 Le
54. ae if district 1 nq 4 xtile q2 cprod_ae if district 2 nq 4 xtile q3 cprod_ae if district 3 nq 4 s BE REER EERE MASPRAP EEL ERE second method uses a loop creating 3 variables with one command HEBER EEE SASSER SENSE RAE for z in num 1 3 xtile quartz cprod_ae if district z nq 4 combine the 3 variables into one variable initialize variable gen quart replace values with information from the 3 variable into just one variable for z in num 1 3 replace quart quartz if district delete variables that are no longer needed quart1 quart2 quart3 for z in num 1 3 drop quartz check results should see equal number of cases in each category tabulate quart district BEARER RERA HAE E RRR ERE RS third method uses a loop where the user does not need to know how many values are in the loop FERSERA CRESS HERES ES EAE the following command stores the values of district into a local variable levelsof district local levels 89 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation foreach command loops through the values stored in the local levels variable creating new variables foreach z of local levels xtile quartile z cprod_ae if district z nquantiles 4 create a new variable to transfer the values from the quartile z variables into one variable generate quartile replace value
55. alog box and place a y mark in the box next to Treat missing values like other values 2 Click on the copy button switch to the Do File Editor and paste the command Write a comment and then switch back to the dialog box to click on the Ok button The command will be executed 40 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Summary statistics on a continuous variable for each value in a categorical variable The by sort summarize command tabulate ca2 ca4 column miss row Now we see the missing data included We wanted counts row percentages and column percentages Row percentages sum to 100 across all the cells in a row while column percentages sum to 100 down the cells ina column The table produced by this command tells you that there are 21 female heads of household and that 6 12 of the total heads of households are female row percent Of the total sample of females those who are heads represent 2 87 column percent For this analysis the same command is used as for general summary statistics with a slight modification This command will show how the mean and other statistics for a continuous variable differ by the values of one or more categorical variables Suppose we want to know how the age of the member varied by his her relationship to the head of household If we did this with tabulate we would get a table with dozens of cells for the differ
56. ame in the command This command is available using the menus From the Menu Select Data then Data utilities then Optimize variable storage The command is compress varlist Stata comes in different flavors Stata MP Stata SE Stata IC and Small Stata With Stata IC we are limited to the use of 2048 variables For the MP and SE flavors you can increase the number of variables that can be used Read the documentation to understand more Stata 13 Sample Session Types of files used by Stata and their extension names 1 Data files 2 Log files Section 0 File structure and Basic Operations for Stata 13 Data files have an extension of dta files containing data Extension dta The format of the data files used by Stata 13 has changed Stata 13 can read files created in earlier versions However since there is a new format for strings that allows very large strings greater than 244 characters if you want to save a data file so that it can be read into an older version you must use the saveold command To open a file From the Menu Select File then Open If you are not in the directory where your files are change to the appropriate directory Only files with an extension name of dta will be listed You can also select the icon on the GUI bar a Only one file can be open at a time If another file is in memory Stata will not permit a new file to be opened and will give an error messa
57. andard deviations Y Suppress frequencies Y Suppress number of obs 8 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok AUN In the Results window we see 96 Stata 13 Sample Session Section 3 Tables and other Types of Analysis Means of Calories per adult equivalent per day Calorie production quartile district 1 2 3 4 Total monapo 1248 7023 2539 3641 3997 4884 9150 0217 4206 5071 ribaue 1502 242 2554 488 4062 3014 7607 719 3900 7966 angoche 1297 9691 2465 509 3698 807 8495 49 3950 2608 Total 1352 5022 2519 7353 3919 3795 8399 3828 4014 5181 7 10 11 Notice that the number of decimals is not uniform We can fix that with the table command From the menus click on Statistics then Summaries tables amp tests then Other tables then Flexible table of summary statistics The table Flexible table of summary statistics dialog box opens Press the Reset button 9 to clear the boxes Under the Main tab select district in the Row variable box Click in the box Y next to Column variable and select quartile in the box below In the Statistics 1 select Mean from the drop down box In the box to the right specify the variable to use for the Mean statistic cprod_ae We would also like to see the minimum and maximum values Click on the drop down box next to 2 and scroll down to Maximum and select that statistic Fo
58. anual in the annex section for further information please contact Dr Michael Weber at webermi msu edu Four portions of the questionnaire are referenced each of which has a corresponding Stata data file Two other Stata data files are required for conversion of units of measure Questionnaire Section Stata Data File Main Household Section c hh dta Table IA Household Member Characteristics c qla dta Table IV Characteristics of Production c q4 dta Table V Sales of Farm Products c q5 dta Conversion factors for computing kilograms conver dta Conversion factors for computing calories calories dta This training consists of four sections each of which should take approximately two hours We recommend that you complete each section in a single sitting These tutorial materials make the following assumptions e You know how to use Windows with a mouse e The six data files listed above should be stored in a directory of your choosing on your hard disk Important Always remember to SAVE the changes to the data after each exercise and section using a new file name Open your Stata software If you have not read or 19 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Data files and the working file Working Directory The cd command completed Section 0 please do so now to clarify the concept of the Command Window the Review Window the Results Window t
59. apsed file We want the collapsed file to have one case per household so we use the variables that identify a household in our survey district vil and hh 4 Click on the Copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok 5 Add a comment to explain what you have done The Stata command is collapse sum cprod_tt by district vil hh In the Variables window you should see only 4 variables Look at the resulting file click on the Data Editor Browse tool You should see only one case per household The collapse command created a new variable cprod_tt which we calculated by summing cprod_tt total calories produced across all cases all the different food crops for each household The only variables which are contained in a collapsed file are the grouping variables and any new collapsed computed variables created e g cprod_tt Stata automatically added a variable label which is the function and variable used to create the resulting new variable 68 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation You can look at the variable definitions using the describe command The computed variable cprod_tt does not have a very descriptive label any more so we need to change the label to reflect what the variable is 1 Click on Data then Data utilities then Label utilities then Label variable 2 Inthe Variable b
60. ariable as type drop down box and change to int 6 For the Contents of variable box click on the Create button 7 Inthe Expression builder box under the Category section select Functions and then Programming 8 A list of available functions is displayed Scroll down to the recode function and highlight that function You will see a description of the function at the bottom of the dialog box 9 Double click on this function The function will be pasted in the window at the top of the dialog box so that you see recode x x1 x2 xn The first x is highlighted Replace the first x with the variable name ca3 so that the expression now looks like recode ca3 x1 x2 xn Replace the x1 with the value of the highest age that you want to recoded for the first group e g recode ca3 10 x2 xn Continue replacing the values with the next group 49 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations to be coded until all groups are defined e g recode ca3 10 19 60 100 Stata will use the value as the code assigned to all cases that fall within that group The value of 10 will be assigned to all observations with ages between 0 and 10 the value of 19 will be assigned to all observations that fall between ca3 gt 10 and lt 19 and so on 10 Click on Ok to exit the expression builder dialog box 11 Click on the copy button switch to the do file edito
61. ariable can have only one value The solution is to record each possible response in a different variable The responses can be analyzed separately using commands you have already seen tabulate but ideally we want to analyze these related variables jointly Multiple dichotomy yes no questions If a survey question asks the respondent to check all that apply from a set of ten choices a separate variable is required for each of the ten responses Each variable has a value to indicate whether the response was 99 Stata 13 Sample Session Section 3 Tables and other Types of Analysis The count command checked 1 or yes or not checked 2 orno An example of this type of question can be found in the household level survey questions see appendix Section V Agricultural Sales question 64 have you increased the quantities sold over the last five years All of the variable names associated with this question begin with H64 Open the file Select File then Open Select c hh dta Click on Open Copy the command and paste it in the do file editor Delete the folder reference eS In this survey 1 yes and 2 no Questions you might ask are A How many respondents increased sales quantities of maize To answer this question you can count the number of times the value of appears in the variable associated with maize To count the number of times a value appears in the variable The command is cou
62. ariables Such a file would be the wide form of the data The file as it is organized now is the long form of the data The following reshape command converts the file from long to wide form such that each animal code is now a variable and the file becomes a household level file reshape wide num i hh j animcode list nol nod noo hh num330 num331 num335 206 70 F 217 65 8 221 1200 200 When followed by this next command the file is re converted from wide to long But note that the file has become rectangularized that is the three animal codes now appear for each household reshape long num i hh j animcode list nol nod noo hh animcode num 206 330 206 331 70 206 335 217 330 217 331 65 217 335 8 221 330 1200 221 331 200 221 335 The command fillin would have also generated the same rectangularized file as in the preceding example Do file suggested commands to place at the beginning of a do file to set the parameters before starting to work 1 Commands in a do file may be delimited by a carriage return or a semi colon To set the semi colon as the delimiter the command is delimit This command will only work in a do file The delimiter cannot be changed from the console If you wish to revert back to the carriage return as the delimiter the command is delimit cr 2 The next command will clear the memory clear all 3 There are several set commands that are useful to put at
63. arize cprod_ae tabulate district quartile summarize cprod_ae nostandard nofreq noobs table district quartile contents mean cprod_ae max cprod_ae min cprod_ae row col format 1 1 Ofc TABLE 1 Food Production in calories per adult equivalent per day Mean Maximum and Minimum Calorie production quartile district 1 2 3 4 Total monapo 1 249 27 539 37997 9150 4 207 1 973 3 176 5 067 28 466 28 466 294 1 984 3 226 5 0 7 294 ribaue 1 502 2 554 4 063 7 608 3 901 2 030 371A 1 4 984 13 124 137124 429 2 082 3 190 5 152 429 angoche 1 298 2 466 3 699 8 495 35 950 2 024 2 996 4 692 20 485 20 485 354 2 037 3 009 5 022 354 Total 1 353 2 520 3 919 8 399 4 015 2 030 3 176 5 067 28 466 28 466 294 1 984 3 009 5 022 294 The table command permits you to specify more than one variable to summarize and also permits formatting of the contents of the table 98 Stata 13 Sample Session Section 3 Tables and other Types of Analysis Print a table from the Viewer Exercise 3 1 A simple way to print a table you have just created is to open the Viewer select the table and print 1 Open the Viewer Click on File then View A dialog box opens asking for the name of the file 2 Click on the Browse button and select the file session3 smcl and click on Ok Scroll down to the table you want to print and block it 4 Click on File then Print then Viewer The Print dialog box opens Under Page Range click on the
64. ation in the first file is then joined with every observation in the second data set and so on The result is a very large dataset This type of file combination is rarely used In this tutorial we will use the merge and the joinby commands With this information in hand we can now think about the specific steps we must take to create the file we need to produce the output we want Logically there are three We need to know how many calories each household produced for the year We can generate a file with this information using data we have stored in three files the production file c q4 dta and two table lookup files conver dta and calories dta We need to know how many adult equivalents are in each household We can generate a file with this information using data from the member file c q1a dta We need to combine the results from steps 1 and 2 into one file so we can compute calories produced per adult equivalent per day 57 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Step 1 Generate a household While executing this step we must keep three things level file containing the firmly in mind number of calories produced per household First all production is currently measured in non standard units Each unit can have a different weight for each of the products Thus we must first convert all production into kilograms Second we want to kn
65. bar Click on the more at the bottom of the Results window 35 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations If you wish to interrupt a Stata command you can click on the Break 3 button on the Tool bar or press lt Ctrl Break gt or type q the letter q for quit in the Command window To rerun the command you just ran click on the last command in the Review windows You see the command is now in the Command window Press lt Enter gt to run the command Copy the command to the Do file editor and add comments to explain what you have done The Stata command should look like list district vil hh mem ca1 ca2 ca3 ca4 ca5 ca6 in 1 10 clean If you wish to you can type the list command in the Command window If you are typing in the command window you can pick the variables from the Variable window and the names will be pasted into the Command window Note that to list a subset of observations Stata uses the key word in e g in 1 10 The key word in restricts the list to a range of observations Examples are list in 1 lists first observation list in 1 lists last observation list in 2 4 lists observations 2 through 4 list in 3 2 lists 2 observations starting with the 3 from the last observation To limit the listing to a specific criterion use the if key word This command is an excellent way to list data with problems Examples are
66. ble weight types 104 Stata 13 Sample Session Section 3 Tables and other Types of Analysis fweight or frequency frequency weights Number of replicated observations this value is always an integer If the fweight associated with an observation is 5 it means the observation represents 5 identical observations pweight Sampling weights inverse of the probability that this observation is included in the sample due to the sampling design A pweight of 100 indicates that this observation represents 100 subjects in the population There are qualifications to this weight when used with survey analysis commands aweight or cellsize analytic weights inversely proportional to the variance of an observation The observations typically represent averages and the weights are the number of elements that produced the average iweight Importance weights relative importance of the observation This weight is generally used by programmers who want to produce a specific computation To read more about weights look at the User manual weights If you use the generic weight sub command Stata will tell you which weight it assumes you want to use Not all commands will allow a weight to be included The format is type_of_weight variable_in_file Let s use one of the Stata s sample data files to explore this sub command 1 Click on File then Open A dialog box should open telling you that the data have changed and D
67. categories In the If expression box change the criteria to ca3 gt 19 amp ca3 lt 60 15 Click on the Main tab and type 3 in the New Contents box 16 Click on the copy button switch and paste in the do file editor switch back and click on Submit The dialog box remains open and the command is run 629 real changes made 17 Type 4 in the New Contents box 18 Click on the if in tab 19 In the If expression box change the criteria to ca3 gt 60 20 Click on the copy button switch and paste in the do file editor switch back and click on Ok 49 real changes made 44 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations The Stata commands created and run are generate byte age_gp 1 if ca3 gt 0 amp ca3 lt 10 949 missing values generated replace age_gp 2 if ca8 gt 10 amp ca3 lt 19 271 real changes made replace age_gp 3 if ca8 gt 19 amp ca3 lt 60 629 real changes made replace age_gp 4 if ca3 gt 60 49 real changes made Note that the Results window shows how many observations were modified after each command was run The next step is to verify that the changes were made correctly Run the tabulate command on the new variable 1 From the menus click on Statistics Summaries tables amp tests Frequency tables One way table The tabulate One way Tables dialog box opens 2 For the Categorical variable box select the variab
68. click on Statistics then Summaries tables amp tests then Tables then Two way tables with measures of association The tabulate2 Two way tables dialog box opens 2 In the Row Variable box select quartile 3 In the Column Variable box select district 82 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation quartile monapo 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK 5 Write a comment in the do file to explain what the commands are doing The number of cases in each cell should be almost the same counts plus or minus a case or two e g AT StrLct ribaue angoche 28 27 27 27 30 29 30 29 30 29 29 28 109 Examples of the foreach looping command 119 115 The new variable requires a label 1 Click on Data then Data utilities then Label utilities then Label variable 2 Inthe Variables box select the name of the first variable quartile 3 Inthe Attach label to variable up to 80 characters box type Calorie production quartile 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK The Stata command is label variable quartile Calorie production quartile Examples of the use of the foreach command are Computing new variables foreach var of varlist inc1 inc1
69. command can be found in the Programming manual and is called foreach We use a command called levelsof to store the values of the district in a temporary local variable called r levelsof The local variable is used to cycle through the values form the district variable 1 Type the following command in the Command window levelsof district The results should display the values of the districts e g 1 2 3 2 Now let s store that information in a local variable To make a temporary local level we include the word local which means the variable only exists with the do file We will work in the do file to create all the commands needed Switch to the do file editor and type levelsof district local levels 80 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation 3 We can now create variables containing the rank of the household within each district There are 3 lines we will type into do file exactly as shown below The right curly bracket must be on a line by itself Also a left quote C which can be found above the lt Tab gt key is used to indicate the the local macro name z along with a right quote e g Z The command will not work unless you use the left quote and right quote around the z in the xtile command not to be used in the foreach line foreach z of local levels xtile quartile z cprod_ae if district z nq 4
70. ction 0 File structure and Basic Operations for Stata 13 In the Results window you should see documentation of the Stata commands that were executed while you were in the Data Editor e g replace hh 2 in 1 1 real change made Now switch back to the data editor Another option from the Tools menu is to Manage Value Labels where a value label can be defined dropped or added We will discuss later value labels when we create new To exit the Data Editor click on the x in the upper right hand comer of the Data Editor The window closes You will often get a data file compute new variables make transformations and finally save the modified set of data to a new name to be used at another time For example you might retrieve a data file with land area per crop add to it production per crop from another file and then calculate yield If you want to use the new production and yield variables at a later time you must make sure that the data file is saved with the new variables in it Never save data that has been modified to the same file name unless these are permanent changes to be made to the original data set We do not want to save any changes that were made to this data file Below are instructions on how to save the file if you wanted save the data file You can close the Data Editor or you can also save the file within the Data Editor From the menus select File Save As enter a new name From the Command w
71. d have 8 cash crops that it is growing Which crops have had increased sales the most For this question we can use the summarize command but we could also use the tabstat command 1 From the menus click on Statistics then Summaries tables and tests then Other tables then Compact table of summary statistics The tabstat Compact table of summary statistics dialog box opens 2 Under the Main tab type h64 in the Variables box 3 Under Statistics to display place a tick in the first box and select from the dropdown list Sum as the statistic 4 Under the Options tab in the Use as Columns change to Statistics 5 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK The Stata command is tabstat h64 statistics sum columns statistics You see all the h64 variables with a count of the number of cases where yes was specified Manioc h64b was the most frequent crop for which households had increased sales 115 sorghum h64g was the least 14 Multiple Response The other type of multiple response question is where the survey question asks the respondent to list up to xx choices from a set of ten choices If four responses are requested four variables must be used to code the responses The set of possible responses are assigned sequential values and the same set of values are used for each of the 4 variables The respondent mus
72. d table Our key variables specify how to merge the lookup file using product and unit the grouping variables because we have a different conversion factor for each product unit combination If we had used only prod Stata would expect each product to have only a single conversion factor with the same value regardless of the unit of measurement used For example it would expect the 61 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Check the resulting data file bag or a 20 liter can This would be incorrect The new working file produced by the join contains the needed conversion factor variable conver For every if the join was successfully completed Stata provides a variable that tells us how the observations from the two files matched That was done In the Command window type From the Results window you should see there are the Click on the Data Editor Browse button to look at some of the cases to verify that the conversion factors match the products We could also use the list command to see if a 20 liter can filled with maize grain has a conversion value of 18 kilograms prod 47 unit 8 The Stata command is list prod unit conver if prod 47 amp unit Note Two equal signs are required The two equal signs distinguish relational equality from create a variable where you will be assigning values to that variable you will use an e
73. e hold Member questionnaire Variable ca2 relation to head is a categorical variable because its values are limited to 6 categories and the values by themselves have no meaning An indicator variable is a special type of categorical variable This type of variable denotes whether something is true or false e g yes no questions or whether a person is male or female This type of variable contains only 2 categories i e it divides the data into 2 groups Start by examining the data in the file Use the Data Editor window to scroll through your data file To do this perform the following steps Click on the Browse button on the Tool Bar or in the Command Window type browse and press lt Enter gt If you want to only look at the data this is the best choice If you think you want to change data directly in the data file not recommended you E could instead click on the Data Editor button Scroll through the data A period in a field indicates a missing value or system missing value In Stata you can specify up to 27 different missing values e g a or b These are called extended missing values Extended missing values are used to identify specific reasons why there are no data e g person refused to answer or a question was not asked Scrolling through the data will give you a feel for what is in file It might also help point out obvious errors e g a variable whose values are missing for all listed ca
74. e Do file editor by using the mouse lt Right Click gt and choose Send to Do file Editor Another method is to copy commands from the Command window and paste them into the Do file editor The lt Right Click gt using the mouse will show different menus depending on which window is active A data file must be loaded into memory before any analysis can be done Allocation of memory is now automatic and no longer a major concern to the user If you are interested in how much memory is available use the following command memory Stata 13 Sample Session memory Memory usage Compress Section 0 File structure and Basic Operations for Stata 13 allocated data strLs 33 554 432 0 data amp strl 33 554 432 data amp strLs var names fmts overhead Stata matrices ado files stored results Mata matrices Mata functions set maxvar usage other 0 33 554 432 2 24 600 1 064 964 1 065 360 0 0 0 0 0 0 1 350 400 1 350 400 779 7179 grand total 2 415 497 35 995 571 Since the data file that you are working with is loaded into memory it is good practice to run the compress command occasionally to reduce the amount of memory that is being used Stata will examine the variables and change the type to another type that uses less memory if it will not affect a loss of precision compress If you wish to compress specific variables just include the variable n
75. e Stata command is tabulate age_gp ca2 column row From the table you can see that 11 95 of heads of households are 61 years of age or older Also of the people 61 years or older 83 67 are heads of households Apply what you have learned about data transformations and descriptive statistics in the following exercise Using the Household Data and Questionnaire available in the annex find out the number of households in each district that have 1 4 5 7 and more than 7 persons per household 51 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Key frequency row percentage column percentage Household size monapo Hints a Use the file c hh dta b Recode h1 into hhsize using the following groups 1 thru 4 5 thru 7 8 thru Highest Add a variable label and value labels d Produce a cross tabulation table showing the districts in the columns and the new variable in the rows Ask for a row percent and a column percent G Looking at the results you can see 34 76 ofall 1 to 4 member households are found within Monapo and that 60 75 of all households in Monapo have 1 to 4 members in a household district ribaue angoche 1 4 members 65 34 76 60 75 48 74 67 3957 34 64 35 5 7 members 39 et 45 56 36 eae 48 06 30 8 12 members 3 04 80 15 3 lt 22 74 We have completed Section 1
76. e name of the first variable ae 3 Inthe New variable label may be up to 80 characters box type Adult equivalents per household 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok 5 Run the describe command again To verify that this variable was created summarize the variable ae 1 Statistics then Summaries tables and tests then Summary and descriptive statistics then Summary statistics 2 Inthe Variables box you should see ae 3 Don t forget to copy the command into the do file editor then click on the Ok button You should find that the average adult equivalent over all households is 3 494221 The Stata commands are 75 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Step 3 Merge the two files created in steps 1 amp 2 to compute calories produced per adult equivalent The merge command label variable ae Adult equivalents per household summarize ae This completes Step 2 Save this file as HH_FILE2 DTA 1 Click on File Save As 2 Filename is hh_file2 3 Click on the Save button 4 Copy the command from the Review window and paste it into the do file editor delete the reference to the folder and add a comment to explain what you have done The Stata command is save hh_file2 dta If you run the syntax again and try to save the hh_file2 dta
77. e nine individuals with missing age or sex codes are all average individuals and assign them the adult equivalent value of 79 Warning be very cautious about filling in missing data this way Careless use of this technique can give you misleading results We are using this example to illustrate the use of Stata commands and not recommending that you do this routinely to compensate for missing data We will use the Replace command to change the system missing values in the ae variable to the mean We want to use the scalar mean rather than entering a specific value because the data could change with further cleaning meaning the fixed value is no longer correct 1 Data then Create or change variables then Change contents of variable The replace Replace into same variable dialog box will appear 2 Under the Main tab select ae in the Variable box 3 Inthe New Contents box type r mean 4 Under the if in tab in the Restrict to observations if box type 73 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Calculate the adult equivalents for the household The collapse command ae The period represents system missing 5 Don t forget to copy the command into the do file editor then click on the Ok button 9 real changes made 6 Check the results of your replace command by rerunning the tabulate command You should see 9 cases in the frequenc
78. e observations in the using data nokeep that have no corresponding observation in the master 121 Stata 13 Sample Session Annex I I Survey Instrument assert assert verifies that an expression is true if it is the command produces no output if it is not assert informs you that the assertion is false append using append appends a STATA format dataset stored on disk to the end of the dataset in memory mvencode varlist mv changes all occurrences of missing to in the variable listing override specified override specifies the protection provided by mvencode is to be overridden without this option mvencode refuses to make the requested change if is already used in the data mvdecode varlist mv changes all occurrences of to missing in the variable list Teagan ee arguments in varlist constructs categorical dummy variables for variables omitting the first category this command can do is determined by the previous command probit probit estimates maximum likelihood probit models search searches the keyword database Use search when you are not certain of the command e g search string shows all commands associated with strings calculates and displays tables of statistics converts data from wide to long form and vice versa wide and long refer to how data are organized See reshape notes below fillin varlist adds observations with missing data so that all combinations of
79. e saved in the dataset label define lblname 1 assigns labels to integers and stores these in the value label1 label2 label bIname 2 label values varnamel 1biname 2 associates the value label Iblname to the variable varname1 e g label define gender 1 female 2 male label values sexhead gender label list lists all value labels modifies the value of a variable using rules specified set memory changes the amount of memory allocated to the data area Stata suggests setting the memory to at least one and half times the size of the file you want to load in the memory of the computer when used with if it counts the number of observations that meet the specified condition otherwise it counts the number of observations in the dataset collapse converts the data file in memory into another data set of means medians etc merge varlist using filename merge joins corresponding observations from the dataset currently in memory called the master dataset with those from the Stata format dataset stored as filename called the using dataset into single observations performs a match merge on varlist when these are specified the variable merge which gives information on the results of the merge command is added to the file _merge 1 obs from master data _merge 2 obs from using data _merge 3 obs from both master and using data merge varlist using filename nokeep causes merge to ignor
80. ed in almost all survey sampling rather than selecting an independent sample Further sub sampling may occur within a district or a village as well Units at the first level of sampling are called the primary sampling unit or PSU or cluster To summarize weights are used to obtain the correct point estimates Clustering and stratification are used to get the correct standard errors The svy commands also calculate the design effects of deff and deft Deff is equal to the design based variance estimate divided by an estimate of the variance that would have been obtained if the survey was carried out using simple random sampling Deft is approximately equal to the square root of deff Further explanation of these two terms can be found in the Survey Data manual under the command Svymean We will use a data set from Zambia from the Post harvest survey of the 2001 2002 agricultural season where the area planted for specific types of crops is tested 1 Click on File then Open 2 Select Zambia_PHS0102_crop_area dta and click on Open 3 Paste the command into the do file editor and delete the reference to the folder Use the Data Editor Browse icon or type in the browse command to look at the data or click on the browse icon In Zambia for surveys conducted in the 1990s and early 2000 a stratified random sampling method was used This method divided the districts into census supervisory areas CSA Within the CSA Standard
81. edit and press lt Enter gt If value labels have been assigned to the values in a variable you will see the value label rather than the actual value Below is an example of a data file with value labels displayed for some variables and values only for other variables File Edit View Data Tools BF Ga Bee a vilf7 1 E district vil hh gt 1 monapo netia 2 3 2 monapo netia 2 3 3 monapo netia 3 ian 4 monapo netia 3 5 monapo netia 3 6 monapo netia 3 7 monapo 3 8 monapo netia 4 9 monapo netia 4 10 monapo netia 4 11 monapo netia 4 12 monapo netia 5 13 monapo netia 5 14 monapo netia 6 15 monapo netia 6 16 monapo netia 6 17 monapo netia 6 18 monapo netia 6 ub WwW NBN BR BP WN BOW ob WN BON OB ca2 ca3 ca4 ca5 ca6 univ a Variables q head 72 m illiterat married arizoni Filter variables here wife husb 69 f illiterat married arizoni Variable Label a head 37 m 3 married arizoni F district district wife husb 26 f 3 married arizoni vil village son daugh 7 m 2 single arizoni F hh household son daugh 5 f illiterat single arizoni TA mem eT See Tee son daugh 3 m illiterat single arizoni cal does this person head 46 m 2 married arizoni M cad dlstion to head wife husb 32 f illiterat married arizoni E a3 age son daugh 14 f illiterat single arizoni E cad k son daugh 10 f illiterat single arizoni head 67 f illiterat married arizoni Properties i wife husb 76 m iliterat married
82. ent ages represented The table would not be usable Instead we will use summarize with the by key word 1 From the Statistics menu select Summaries tables amp tests Summary and descriptive statistics Summary statistics The summarize Summary statistics dialog box opens 2 Select ca3 from the drop down box for Variables 3 Under Options in this tab select Standard display 4 Click on the by if in tab 5 Place a in the box Repeat command for groups 6 In the box below this option labeled Variables that define groups select ca2 7 Click on the copy button switch to the do file editor and paste Switch back to click the Ok button The command will be executed This command calculates the means of the variable ca3 age separately for each different value of the variable Ca2 relation to head including the system missing value The Stata command is by ca2 sort summarize ca3 41 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Note that the command begins with by This command is first sorting the data by Ca2 before it runs the summarize command You could also sort the file by Ca2 first and then just use the by key word e g sort ca2 by ca2 summarize ca3 From this output you find that the average age of heads of households is 41 5277 years while the average age of their spouses is 33 1871 years Four observations
83. eods onto saa svass Eacahdedts a atbnsatschbacetandvees bot desevionedboveriveatde adh tue e Describing the contents of a data file he describe command aieia EET ck eset cesta ete ees TOIL B A ie Seiad tet EEA Data storage types eison dateieae eh a a e a a oh dealin as e e diets Display format cece eeteeeeteeees Labels vos hdd lias Documenting variables and labels The labelbook command 008 The codebook cOMMANG uci acnath ENEE ch enon aia itt Asiana n that N Generating descriptive statistics Descriptive statistics using one variable D SCNIPUVeS serio Atlee cence cee en haha T EE than baat teatate alah telat ct A THESUMMAMIZE COMM AN sais ie 5 cceszes cadet dus fadezts E EET tea tei hecek hdeth aeteeates Information returned by Stata COMMANAS 0 cece esses eesesessesesseseeeeseeecneeesnenecseseeecneeeeneneeteneeteneeees Frequencies of Categorical Variables oo ccccecccesssssesescssesesesescsnscesesesesescesseseseseecsessseseecsteneeneseaeeeeees The tabl COMMAN Gs cc1c fss55 vce ea a a Hee Se ae ee A a os Ss a oh The histogram COMMANG 6s seca Ave atiina AAA SEREA Sa vi g graph t a file 35 ha 58 sre nde n e raa haan sina a r a e aat The NiStCOMMAN Gs 5 34 ohio ted carer ne a a A a a alee atten sett a a E NAA Descriptive Statistics using two or more variables Two way Tables with Categorical Variables Cross tabulation The tabulate command 2ccnn deel
84. er to go back to the original questionnaires to determine the reason why these data are missing Since we can t do this we will use an alternative method If we leave these values missing the total adult equivalents of those households will appear to be slightly 72 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation smaller which may distort the results We could avoid this problem by eliminating the households with missing information from our analysis but then we can t use the information about the food production from those households Instead we will try to make a reasonable assumption about those nine missing members We know that the adult equivalent values range from a low of 6 for children to a high of 1 0 for adult males which is not a very wide range We can determine the mean average adult equivalent value for the whole sample and use that value to fill in the missing data To find out the average adult equivalent value for our sample click on 1 Statistics then Summaries tables and tests then Summary and descriptive statistics then Summary statistics 2 Inthe Variables box select the variable ae 3 Don t forget to copy the command into the do file editor then click on the Ok button The Stata command is summarize ae We can see that the mean average value of ae for all individuals is 79 with a standard deviation of only 17 We will assume that th
85. er adult equivalent per day by district M Beaver January 2014 Tasks 1 Compute total kgs produced compute value of production in calories for specific food crops and aggregate to the household level to obtain total food calories produced 2 Compute adult equivalents and aggregate to the household level 3 Merge the two files and calculate food production in calories per adult equivalent per day 4 Produce a table showing average food production in calories per adult equivalent in quartiles for each district Stata recommends you include the version that the do file was written in version 13 clear all macro drop _all turn off more set more off KKK KK KKK KKK k k k k k k k k kk kk kk kkk kk kkk kkk Step 1 Kk k k k k k k k k k k k k k k k k k k k k k kk kkk kk kkk kkk open production data file use c q4 dta clear sort variables to match by to merge in the conversion value to convert to kgs sort prod pla tab1 prod pla rename the pta variable to unit to match the conver data file rename p1a unit joinby prod unit using conver dta unmatched master _merge _merge check to be sure merge done correctly tab1 _merge check to see if got what was expected using list command list prod unit conver if prod 47 amp unit calculate kgs produced generate double qprod_tt p1b conver merge in the lookup conversion value for calories and calculate total calories
86. eriod Jan April 3 this year s harvest 4 various times Motive for sale at this time 1 needed money 2 buyers available 3 consumer goods available 4 attractive price 1 lojista 2 wholesaler 3 AGRICOM 4 ambulante 5 brigada 6 company Locale of sale 1 farmgate house 2 village 3 locality 4 district 5 province Distance from the farm enter the kms between farmer and point of sale Who in the household is responsible for the sale 1 husband 2 wife Why sold to this buyer Value of Sales meticais Unit 1 unit price 2 total value 1 the only one available 2 always sell to this one 3 best price 4 transportation provided 5 carries consumer goods gt N B Not all of the variables that appear in the printed table are in file c q5 dta Only variables VEN V2a V2b V9a and V9b were kept for this exercise 129
87. f we wanted to see more summary statistics on this variable we can ask for detail Switch back to the summarize Summary statistics dialog box you can see the icon on the task bar 4 Click on the radio button next to Display additional statistics Click on the to copy the command to the clipboard 5 Click on the button to run the command The dialog box will close The results are age Percentiles Smallest 1 I5 1 6 3 I Obs 1524 7 1T Sum of Wgt 1524 16 Mean 21 33602 Largest Std Dev 17 69252 32 75 48 76 Variance 313 0252 5 7 78 Skewness 9152221 69 81 Kurtosis 3 00135 The median age is 16 50 Percentile The Stata command is 30 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Information returned by Stata commands Frequencies of categorical variables summarize ca3 detail Switch to the Do File Editor and paste the command Insert comments to explain the commands you have pasted When you run a command Stata sends the information to the Results window as well as saves the information in memory in to scalars To see what has been saved you can use the return list command In the Command window type return list The information that is returned from the summarize command is displayed r N 1524 r sum_w 1524 r mean 21 33602362206289 r Var 313 0251689442948 r sd 17 69251731507687 915
88. fication of an individual household The Grouping variable s is used to specify the variables to be used for combining cases in the collapsed file Any cases from the original file that have identical values for all 3 of the grouping variables will be combined into a single case in 74 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation the collapsed file We want the collapsed file to have one case per household so we use the variables that identify a household in our survey district vil and hh 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok 5 Add a comment in the do file to explain what you have done The Stata command is collapse sum ae by district vil hh Collapse creates a new working file The new working data file is at the household level with one case per household The variable ae is the total adult equivalents for that household Look at the resulting file click on the Data Editor Browse icon You should see four variables with only one case per household You can also look at the variable definitions using the describe command The computed variable ae does not have a very descriptive label any more so we need to change the label to reflect what the variable is 1 Click on Data then Data utilities then Label utilities then Label variable 2 Inthe Variables box select th
89. file editor Log files contain statistical output data information and presentation generated by the Stata processor They do not contain data Log files are made accessible to Stata with a File View command The extension is smcl Data files contain data including original survey variables plus any new variables created through various Stata commands such as the generate command Data files are made accessible to Stata using a File Open command from the menus or typing the command in the Command window Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Stata 13 SAMPLE SESSION SECTION 1 Basic functions Stata files Descriptives and Data Transformations Introduction This is a self paced training aid designed to introduce the commands needed for some typical statistical survey analyses using Stata 13 This tutorial is intended to be a stand alone training tool To use it most effectively you should ask a knowledgeable STATA user to help you get started and to answer questions as you work independently through the session It can also be used as a guide for classroom training A copy of the questionnaire on which the data is based can be found in the Mozambique project 1992 NDAE Working Paper 3 A Socio economic survey of the smallholder survey in the province of Nampula Research Methods copies of the three tables which were made available and can be found at the end of the m
90. for example how many heads of household are older than 60 Since the age variable ca3 is continuous we cannot do this directly first we have to transform it Let s suppose we re interested in four categories 0 10 years old 11 19 years 20 60 years and over 60 years of age To categorize a variable we can use the generate command Categorizing a continuous variable makes detailed information more general To keep the detailed information as well as the new general information you must recode the variable into a new variable If you recode into the same variable the original values will be lost There are several methods that can be used to recode a continuous variable First method If you wish to see the category values of 1 2 3 and 4 where 1 0 10 2 11 19 3 20 60 and 4 over 60 you can do the following 1 From the Data menu select Create or change data then Create new variable The generate Create a new variable dialog box opens 2 Under the Main tab type the name of the new variable in the Variable name box age_gp 3 For the Contents of variable box we will specify a value or an expression Type in the box a value of 1 This is the value that you want the new variable to have 4 Inthe drop down box for the Variable type select byte 5 Click on the if in tab 6 We will restrict observations to a specific set of data using the If In the If expression box type in ca3 gt 0 amp ca3 lt
91. g calculate mean to determine average ae across the whole population To fill in the missing values summarize ae replace all system missing with the value of 79 replace ae r mean if ae tabulate ae missing need to sum the adult equivalents for each household collapse sum ae by district vil hh label variable ae Adult equivalents per household summarize ae save file for later use save hh_file2 dta replace KKK KKK KKK KK k k k k k k k k k k k k kk kkk kk kkk kkk Step 3 combine both the hh_file1 with hh_file2 match files by district vil hh kk kk k kk k k k k k k k k k k k k kk kk kk kkk kk kkk kkk 88 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation use hh_file2 dta clear matching the two files which are at the same level hh level merge 1 1 district vil hh using hh_file1 dta check to see which file the variables are coming from tab _merge 3 variables came from both files drop _merge calculate the calories per adult equivalent per day generate double cprod_ae cprod_tt ae 365 label variable cprod_ae Calories per adult equivalent per day sum cprod_ae rank the new variable by district into quartiles check for number of districts tabulate district there are 3 districts so we want to loop 3 times PEARARRERERARAR ES kkkt RE first method RELEEARERSERAERESESE NES xtile q1 cprod_
92. ge To open another data file the subcommand clear must be included in the command From the Command window if you are working in the correct directory you can type use name of file clear Stata can record a copy of the commands and the output from the commands in a log file If you wish to record this information in a file you must turn on the log There are two types of logs log files and cmdlog files The log files can have 3 different types of extension names SMCL log and txt as described below commands and output Extension SMCL Stata markup and control language commands and output Extension log ASCII text commands only Extension txt 1 Log The first type of log records everything that you submit for execution and all the output resulting from the commands You can specify one of two formats either SMCL or ASCII text log From the Menu Select File then Log then Begin You are prompted for a file name The default extension is SMCL The file is formatted in the Stata markup and control language Type a name for the file and click on OK If you prefer to record the information in ASCII text then you would need to type the file extension of log e g session1 log Stata 13 Sample Session The log using command The cmdlog using command The log close command Section 0 File structure and Basic Operations for Stata 13 To start a log file you can select the Menu opti
93. h and paste in the do file editor switch back and click on the Ok button The Stata command is label variable age_gp Age group To assign value labels to a variable we first have to define a label and then assign value labels to the values in that label Click on Data then Data utilities then Label utilities then Manage value labels Remember Stata assigns a name to a group of value labels Click on the top button the right Create Label Another dialog box opens In the Label name box where there is a prompt lt Enter new label name here gt type age_group In the Value box type 1 In the Label box type 0 to 10 Click on the Add button below the Label box The dialog box remains open Continue defining the labels for the values Type 2 in the Value box and in the Label box type 11 to 19 and click on the Add button Type 3 in the Value box and in the Label box type 20 to 60 and click on the Add button Type 4 in the Value box and in the Label box type 61 and older and click on the Add button All the values have been assigned a label To close the dialog box click on the Ok button to close the Create label dialog box Click on the Close button to close the Manage Value Labels dialog box As you can see in the Results window the Stata command 46 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Second method labe
94. haracteristics Filename c qla dta This person Relation to Head Level of Schooling Marital Status works on farm or off 1 head enter the last 1 monogamous farm 2 spouse completed year 2 polygamous 3 child 3 single 4 parent 0 illiterate 4 widowed 5 other kin 12 post high school 5 divorced 6 other 98 no formal 6 emigrant wife schooling but literate husband out longer than six months CA6 eM cal CA2 cA3_ Cad gt uw 127 Stata 13 Sample Session Annex I l Survey Instrument IV PRODUCTION Product Quantity harvested B cotton S peanuts 6 rice 1 cashew nut B0 beans B 1 manteiga bean 41 dry manioc 47 com 44 sorghum Table IV Characteristics of Production Filename c q4 dta Quantity Existing stocks Month in Amount to be How long Quantity reserved harvested ina at harvest time which last stored from this will this for seed normal year year s stock year s harvest for year s ran out consumption _ stocks last Unit enter the i i Qty 1 sack 100 2 sack 50 B kilo 4 liter 5 can 20 128 Stata 13 Sample Session Annex I I Survey Instrument V AGRICULTURAL SALES Table V Sales of Farm Products Filename c q5 dta 3 cotton 5 peanuts 6 rice 21 cashew nut 30 beans 31 manteiga bean 41 dry manioc 47 corn 44 sorghum Quantity sold Units 1 sack 100 2 sack 50 3 kilo 4 liter 5 can 20 Period of sale 1 planting Aug Dec 2 hungry p
95. have no labels 0 and 1 and note that there are only 1670 cases that contain a value for pla There are possible data problems We would expect to see a value in pla for every crop that was harvested How would you determine if there are missing data in the pla variable If it were possible corrections should be made before proceeding further The variable in the conver dta file that contains the code for the unit has a name of unit The variable in our active file that contains the code for the unit is named pla We cannot merge the two files unless the variables that we want to merge by have the same names We will rename p1a to unit 1 From the Data menu select Data utilities then Rename groups of variables The rename Rename groups of variables dialog box will come up 2 In the Existing Variable names box select pla In the New variable names box type unit 3 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK The Stata command is rename p1a unit Note in the Variables window that the name has changed 60 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation The joinby command The files are now ready to be merged We are doing a File Table merge where the second file is our Lookup Table We want to keep all records in the master file or the file in memory and keep
96. he command that created the graph to see the graph again You cannot have more than one graph window open at a time Let s look at another graph We will use the file created in the last session hh_file4 dta We can plot adult equivalents per household with total calories produced 1 Click on File then Open 2 Select hh_file4 dta and click on Open Copy the command to open the file to the do file editor 3 Select Graphics Two way graphs scatter lines etc 4 The dialog box opens for the twoway graph Click on the Create button to define the graph The default type of plot is scatter You could pick Line Connected Area Bar Spike or Dropline 5 For the Y variable select ae from the dropdown arrow for the X variable select cprod_tt from the dropdown arrow 6 Click on the Accept button You now see that Plot 1 has been defined and is highlighted 7 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Submit You can now view the graphic 8 Close the graph and return to the dialog box We want to see the distribution by district Click on the By tab v check the box next to Draw subgraphs for unique values of variables 9 Inthe Variables box select district 10 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK button to view the graphic What are these graphs telling yo
97. he Do file Editor and the Viewer Data from questionnaires that has been entered into Stata are stored in what are called data files If we want to work with a set of data we must open the corresponding data file so that it is available to the program The working directory is the directory where your data files are stored You can set the working directory through the menus From the menus select File Change Working Directory A dialog box opens in Browse mode Find the directory that you want to work in and then click on Ok You can instead use the cd command to change to the directory where you have placed the data files you want to use by typing the command in the Command Window Type cd name of working directory Changing to the directory where the files are located eliminates the need to include the directory name in the do file that we will be creating If the directory you are changing to has spaces the directory must be enclosed in quotes Example of a directory name with spaces C Users Documents My data In the Results window the cd command has been executed We can copy that command to place it in our do file so that if we share our do file with another person the command can be modified to fit the directory structure that person is using Example cd C Users Documents Stata Training data When a data file is opened it is loaded from the disk into memory the computer s RAM making it the working
98. hese user written procedures are given an extension ado and included in the installation of the program For analysis of the multiple response type of question we can go to the Help menu and search to see what has been written 1 From the menus click on Help then Search The Keyword Search dialog box opens 2 Inthe Keywords box type multiple response 3 Click on OK 4 Under the Options tab in the Use as Columns change to Statistics 5 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK Several listings appear in the Viewer window One item labeled FAQ gives us an article to read that discusses multiple response You can click on that link to read further Go back to the Help then Search dialog box and this time select Search net resources with the Keywords multiple response A different listing appears in the Viewer window stating that 11 packages were found either in the Stata Journal or the SB listings The first listing looks promising st0082 from http www stata journal com software sj5 1 SJ5 1 st0082 Tabulation of multiple responses Tabulation of multiple responses by Ben Jann ETH Zurich Support jann soz gess ethz ch After installation type help mrtab mrgraph and _mrsvmat here is a user written ado command called tabw Peter Sasieni STB 25 Stata 3 1 103 Stata 13 Sample Session Section 3 Tables and other T
99. hiataseate lassie Beas Aaah ene ean Summary statistics on a continuous variable for each value in a categorical variable 0 0 0 0 41 The by sort summarize command oF e a A ETa aE T aE EE EE AE A E E ETR Converting continuous variables to categorical variables The generate COMMANG scien cies teen nae eet a a a eai Ihetreplace command tetcxte ae A Ale hee tol hohe evoke ke sak hd 2 tik at Thelabel variable commandi iras inia he le es Saad Se SE Aa hee eles The label define command The label values command The recode fUNCE ON 22208 04 e ann a eta a aah ei cai adh anand aise Stata 13 Sample Session Section 0 File structure and Basic Operations for Stata 13 SECTION 2 Restructuring Data Files Table Lookup amp Aggregation 0 0 cc cccesceccnseseeccnseneeeeeeeneeeeeeceecseeececeetaeeaees 54 Restructuring Data Files sn sci sccs a a Beate aE ise toe eas anra 54 Step 1 Generate a household level file containing the number of calories produced per household 58 Rename any key variables in both files to the Same name oo cece cece esesesneseeeeseeeeseeecneneseeseeesereeneaeeneaeeteneeees The joinby COMMANG as eenia e R Ar A aiadinauaas actions unataautiueusnnins Compute total kilograms produced cnnan E E E A AT EEE RE N AA The generate command cece The drop COMMANA cece eeeeteeeees Calculate the total calories produced Select only staple food products
100. his window the Review window on the left commands submitted to the processor appear in this window the Variables window on the right the list of variable names in the data set that has been opened appear here the Properties window on the bottom right where the properties of the selected variable and the data file can be viewed and the Command window where commands can be typed This is the active window at startup The cursor is located in this window File Edit Data Graphics Statistics User Window Help EA amp 3 2 gt ula 4 is sie x Review Tigis Command c Variables Tax Variable Label There are no items to show Copyright 1585 2023 StataCorp LP There are no items to show Statistics Data Analysis StataCorp 4190S Lakeway Drive College Station Texas 77845 USA 800 STATA PC htto www stata con 979 696 460C stateG steta com 979 696 4601 Single user Stata perpetual license Serial number 301306235355 Licensed to Margaret Beaver Michigan State University 4 Properties t gt IE Variables Name Label Type Format Value Label p E Filename Notes Jariables 0 CAP NUM G Users beaverm Documents Stata 13 Sample Session How Stata uses memory Section 0 File structure and Basic Operations for Stata 13 Other windows are available but are not opened at startup These windows are Viewer used to view help files and log files SMCL
101. household product level file where there are multiple cases per household Stata uses the command collapse to aggregate the number of cases at one level to a new level 67 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Create a new file which is a household level file rather than a household product level file The collapse command We will sum all the cases for each household to create just one case for household To create the new household level file we use the command collapse Stata always uses the working data file as the file to be collapsed 1 From the Data menu select Create or change variables then select Other variable transformation commands then select Make dataset of means medians etc The collapse Make dataset of summary statistics dialog box will appear 5 On the Main tab in the Statistics box for 1 change Mean to Sum by clicking on the drop down arrow In the Variables box select cprod tt 6 Click on the Options tab and in the Grouping variables box select district vil hh in that order because those variables represent the identifi cation of an individual household The Grouping variable s is used to specify the variables to be used for combining cases in the collapsed file Any cases from the original file that have identical values for all 3 of the grouping variables will be combined into a single case in the coll
102. ick on the browse button You will see listed a file called session1 smcl 3 Select that file click on Open then click on Ok The Viewer opens and displays the log file that has saved all the commands and output from Section 1 of the tutorial To close the Viewer click on the x in the upper right hand corner of the Viewer 53 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation STATA 13 SAMPLE SESSION SECTION 2 Restructuring Data Files Table Lookup amp Aggregation Restructuring Data Files For some types of analysis the data files may need to be restructured to a different level The data from the four sections of the questionnaire household member production and sales are in four separate data files because the data are at different levels The household data is at the most general or highest level one case per household The other three files contain more detailed data which is usually thought of as being at a lower level there are multiple cases per household If you are not familiar with the concept of levels of data read Computer Analysis of Survey Data File Organization for Multi Level Data by Chris Wolf before continuing on with this section This paper is available at http fsg afre msu edu survey index htm The analysis we did in Section 1 was done using the variables in a single file However other types of analysis require c
103. if Stata knows the file is sorted is to use the Describe command In the Results window you can see at the end of the list of variables the words sorted by and the list of variables that the file is sorted by Because we created hh_file1 dta by collapsing the file it is already sorted by district vil and hh hh_file2 dta was also created by collapsing the file so it is also sorted by district vil and hh We are ready to merge the two files 1 Select Data then Combine datasets then Merge two datasets The merge Merge dataset in memory with dataset on disk dialog box will appear The default type of merge is one to one on key variables this is the merge we want to do 2 We are doing a one to one on key variables type of merge the default selection In the Key variables match variables box select district vil hh These are the Key Variables 3 For the Filename of dataset on disk box click on the Browse button Select the file hh_file1 dta and click on Open 4 Click on the Options tab Under this tab you see the box labeled Name of new variable to mark merge results The default name is merge This variable received a code of or 2 or 3 to describe what type of merge occurred The code definition is 1 observation is from file in memory 2 observation is from file on disk 3 observations are from both files It is very important to look at the values in this variable after you have run the merge 5 Click
104. if district 1 nq 4 wher e qi is the new variable that is created cprod_ae is the variable used to rank the data district is the controlling variable nq 4 is short for nquantiles number which specifies the number of quantiles to use 79 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation The for z in num 1 3 looping command The foreach looping command The levelsof command This command has to be repeated for the other two districts so that 3 variables are created where the observations for that district are divided equally into 4 groups Using the if expression works where you have only a few codes within the variable We have 3 districts so it would not be a problem to use this command but what if we had 20 districts This method would be a bit cumbersome Another method is to use a counter Add the command for z in num 1 3 in front of the xtile command where z is a temporary variable that loops through the values specified with the num 1 3 The value of 3 would be replaced with the number of values in the district variable Note that the z is added to the quart variable and that z is used instead of the actual numeric value for the value to use for the value for district Stata provides another looping command that we can use to compute the new ranking variable It is not available through the menus The looping
105. ill see in the Stata Results window that for ca 70 67 of the household members work on a farm There are 1524 cases for this tabulation The results for ca4 show that 51 53 are males and 48 47 are females How many cases have been included Only 1508 cases were tabulated Why 1 Go back to the dialog box tab1 Multiple one way Tables 2 There is an option Treat missing values like other values Place a tick mark WY n the box to the left of this option 3 Copy the command switch to the do file paste the command write a comment to explain the difference between the first command and this one then return to the dialog box 4 Click on Ok Looking at the table for ca4 there are 16 observatiaons that are missing values Stata s default is to not show the missing values The Stata commands are tab1 cal ca2 ca4 ca5 ca6 tab1 cai ca2 ca4 cad ca6 missing Note to produce a tabulation frequency of just one variable you can use the tabulate command However if you want to list several variables in the frequency command you must use the tabl command Below you will see that if you use the tabulate command and list 2 variables a cross tabulation is produced 32 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations The histogram command Saving a graph to a file Another useful way to examine a continuous variable is to Graph the variable to view the dist
106. in 4 bytes default storage type unless another type is specified double larger real numbers stored in 8 bytes 24 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Display format Labels Documenting variables and labels The labelbook command strX string The X is replaced by the maximum number of characters allowed for the variable e g it is a fixed length up to 2045 characters strL string Can contain from 0 to 2 billion characters new to Stata 13 Since Stata stores the data from the file in memory when you define a variable you want to define it with an appropriate storage type to maximize the amount of data that be opened in the program Display format The display format is the third column which describes how the data are to be displayed Stata will make an assumption with new variables so it is not always necessary to specify the format Format information always begins with a percent sign to indicate the start of the format information Refer to the PDF documentation Section 12 5 for more details In this example the 9 describes the width of the variable After the decimal the 0 indicates no fixed number of decimals will be displayed If you wished to see only 2 decimals the example would be 9 2g The letter following indicates what type of format e scientific notation e g 1 00e 03 f fixed format e g 1000 03 g general
107. indow you can also type save newfilename or if you want to use the same name type save replace The same name as the file you opened will be used The second method to look at the data is to use the Browse mode You cannot modify the dataset if you use this method This method will prevent you from accidentally modifying the data Click on the browse button In this window you can sort the data and also hide columns if you wish To exit the Browser click on the x in the upper right hand corner of the Browser 16 Stata 13 Sample Session D The Stata Results Window E The Command Window F The Viewer G Stata Graph window Section 0 File structure and Basic Operations for Stata 13 Stata automatically writes all messages and output to the Results Window from the execution of your commands For example if you run a tabulate command then the frequency table will be written to the Results window If you wish to save the information in the Results window you must remember to turn on a log file See the explanation above on Log files The Command window is used to type commands directly If you use the menus the command is run immediately The command is placed in the Review window If you want to rerun a command that is in the Review window click on the command The command is placed in the Command window To execute the command press lt Enter gt Useful keystrokes within thi
108. information in the dialog box click on the Reset icon to clear the contents 3 Under the Main tab type the name of the new variable in the Generate variable box cprod_ae 4 Change the Variable type to double 5 For the Contents of new variable box we can use the Create button to help us build the expression Click on Create Under Category select Variables Double click on the variable cprod_tt It will appear in the box above the Category On the right hand side click on the Now click on ae Click again on the Now click on the numbers 365 The expression should look like cprod_tt ae 365 Click on Ok to close the Expression builder dialog box 6 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK 7 Add a comment in the do file to explain what you have done The new variable does not yet have a variable label To assign a variable label 1 Click on Data then Data utilities then Label utilities then Label variable 2 Inthe Variables box select cprod_ae 78 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation 3 Inthe New variable label variable may be up to 80 characters box type Calories produced per adult equivalent per day 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK 5 Adda
109. k on the Reset icon in the Lower Left corner of the dialog box 3 Under the Main tab change the Variable type to double 4 Type the name of the new variable in the Variable name box cprod_tt 5 For the Contents of variable box type in qprod_tt calories 6 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok 7 Add a comment in the do file to explain what you have done The Stata command is generate double cprod_tt qprod_tt calories Note that missing values were generated for 131 cases The two new variables do not yet have variable labels To assign a variable label 1 Click on Data then Data utilities then Label utilities then Label variable 2 Inthe Variable box select the name of the first 65 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Select only staple food products variable qprod_tt 3 In the New variable label may be up to 80 characters box type Total production in kgs 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on the Submit button Clicking on the submit button leaves the dialog box open so we can then define the label for the cprod_tt variable without having to select it again from the menus 5 In the Variable box select the name of the second variable cprod_t
110. k to select it To close the drop down box click in another area of the dialog box In the Options section below the variable box note that Standard display is the default selection for output E Don t forget to click on the icon to copy the command to the clipboard switch to the do file editor paste the command and switch back to the dialog box 3 Click on the button to run the command The dialog box will remain open The output appears in the Stata Results window You will see that the mean for age Ca3 is 21 33602 years The Stata command shown in the Review window is summarize ca3 The Results window displays Variable Obs Mean Std Dev ca3 1524 21 33602 17 69252 Return to the dialog box On the task bar you can see 29 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations summarize Summary click on this task Click on the MM in the lower left corner to see more detail about the Summarize command In this help window you can see that the first two letters of the command are underlined e g summarize In Stata only the letters that are underlined are absolutely required for the command to be recognized The following command also works su ca3 Scroll down through the Viewer to see the options available with this command and examples To close the Viewer click on the x in the upper right hand corner of the dialog box I
111. l define age_group 1 0 to 10 2 11 to 19 3 20 to 60 4 61 and older Copy this command to your do file The command creates a label name age_group and defines the labels for the four values Now that the label has been defined we can assign this label to the categorical variable we created 8 Click on Data then Data utilities then Label utilities then Assign value label to variables The label values Assign value label to a variable dialog box opens The default choice is to attach a value label to variables 9 Inthe Variables box select age_gp This is the variable that we want to attach a label to 10 In the Value label box select age_group 11 Click on the copy button switch to the do file editor paste the command switch back and click on the Ok button The Stata command is label values age_gp age_group Run a tabulate on this variable to check to see there are labels for the values tab age_gp Age group Freq Percent Cum 0 to 10 575 Sla TI 3773 11 to 19 271 17 78 55 51 20 to 60 629 41 27 96 78 61 and older 49 BinZ2 100 00 Total 1 524 100 00 Another method we can use create a new variable assign the new values and assign the labels for the values in one step 1 Select Create or change data from the Data menu 2 Select Other variable transformation commands 3 Select Recode categorical variable 47 Stata 13 Sample Session Section I Basic functions Files Descripti
112. lding all the commands again Documenting the do file with comments can save you much time trying to remember what analysis you did and why Let s see how you would retrieve the do file you just created To exit Stata 1 Click on File then Exit Stata will prompt you if you have not saved the data file and will give you an opportunity to return to the program to save the data file If you do not want to save your data file click on yes to exit Start Stata again To open our do file 1 Click on Window then Do file editor then New Do file or press lt CTRL 8 gt or you can click on the 85 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Do file editor icon on the tool bar The Do file editor window will open 2 Click on the yellow file folder tool and select the file session2 do 3 Click on Open You can then re execute these same commands or edit them as you wish There is one icon on the tool bar to the far right that allows you to run all the commands in the do file or just commands that you have blocked If you block lines in the do file and move your mouse over this icon you will see Execute Selection do Clicking will run the commands you ve blocked In the Results window you will see the commands and any output from analysis In the Review window you will not see the commands only the one command do STD00000000 tmp where the blocked command
113. le age_gp from the drop down box 3 Place a Vv in the box next to Treat missing values like other values 4 Click on the copy button switch and paste in the do file editor switch back and click on the Ok button The Stata command is tabulate age_gp missing There should be 4 codes in the frequency table 1 2 3 and 4 with no missing data We can use the Data Browser to check to see what changes were made Click on the Data Editor Browse button Compare the value in ca3 with the The label variable command new variable do the values in age_gp fit the criteria we used to assign the values Close the browser window when you are finished The values do not have any value labels to define what the values of 1 2 3 and 4 mean A categorical variable is not useful unless labels are assigned We want to add both a variable label and give labels to the values in this variable 45 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations The label define command The label values commands 1S l 2 l To assign a variable label Click on Data then Data utilities then Label utilities then Label variable We want the default selection to Attach a label to a variable In the Variable box select the name of the variable age_gp In the New variable label box type Age group Note Label may be up to 80 characters Click on the copy button switc
114. lookup file and or the production file and run your procedure again easy to do since you have a do file with all the commands you need We can now calculate total kilograms produced by multiplying the quantity of production i e 25 of whatever unit was specified in pla by the conversion factor The variable with the quantity is p1b 1 Select Create or change variables from the Data menu 2 Select Create new variable The generate Create a new variable dialog box opens 3 Under the Main tab change the Variable type to double 4 Type the name of the new variable in the Variable name box qprod_tt 5 For the Contents of variable box type in p1b conver 6 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok 7 Add a comment in the do file to explain what you have done The Stata command is generate double qprod_tt p1b conver Note that there were 49 cases where no value was generated We had 27 cases with no conversion value Why do the rest of the cases 22 cases not have values Look at the data in the browser where there are no values if you are not sure Are there issues you might want to try to fix You should rename the variable that had to be changed for the joinby back to the original name if you plan to save the data file to a new name Otherwise you might forget what the variable originally was The command is the same as above
115. marize tabulate and table based on an example from section 2 1 Click on File then Open 2 Select hh_file3 dta Click on Open 3 Copy the command from the Review window and paste it into the do file editor First we will use the Summarize command 1 From the Statistics menu select Summaries tables and tests then Summary and descriptive statistics then Summary statistics The summarize Summary statistics dialog box opens 2 Select cprod_ae in the Variables box Be sure that under Options in this tab Standard Display has been selected Click on the by if in tab Click in the box Repeat command by groups In the box below this option select district quartile Click on the Copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok ies DAUA For each combination of district and quartile we see the summary statistics This output is difficult to read Next we will use the tabulate command 1 From the menus click on Statistics then Summaries tables and tests then Other tables then Table of means std dev and frequencies The tabsum Table of means std dev and frequencies dialog box opens In the Variable 1 box select district In the Variable 2 optional box select quartile In the Summarize variable box select cprod_ae For output we are only interested in the mean so tick the boxes next to Y Suppress st
116. me can be assigned to multiple variables You can create a label name for 1 yes 2 no and assign that label name to several different variables 26 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations The codebook command Generating descriptive statistics The summarize and tabulate commands In the Command window you can also type label list district vil To document the data and all the variables including those that do not have value labels another command is available 1 From the Data menu select Describe data Select Describe data contents codebook Click on to copy the command and switch to the do file to paste the command Switch back to the dialog box and click on Ok In the Command window you can also type In this output every variable is listed The type of variable is given the range of values in the variable number of unique values how many cases have a missing value and it also includes descriptive statistics for variables The output for the descriptives is based on whether Stata thinks the variables are continuous or categorical Stata cannot always tell if the variable is categorical so it does not always display a frequency table for a categorical variable After examining the variables we will begin to examine the data by running descriptive statistics e g frequencies averages maximum minimum and standard deviations for all variables Thi
117. mp ca3 gt 10 amp ca3 lt 19 14 Click on Submit The dialog box remains open and the command is run 143 real changes made 15 In the Restrict to observations if box change the criteria to ca4 2 amp Ca3 gt 20 16 Click on the Main tab 17 Type 72 in the Contents box 18 Click on Submit The dialog box remains open and the command is run 331 real changes made 19 Type 6 in the Contents box 20 Click on the if in tab 21 In the Restrict to observations if box change the criteria to ca3 lt 10 22 Click on Ok 520 real changes made The statements we need are detailed in the table below Numeric value If statement 1 ca4 1 amp ca3 gt 10 84 ca4 2 amp ca3 gt 10 amp ca3 lt 19 72 ca4 2 amp ca3 gt 20 6 ca3 lt 10 23 Copy the 4 commands from the Review window and paste them into the do file editor and add a comment to explain what you have done The new variable does not yet have a variable label To assign a variable label 1 Click on Data then Data utilities then Label utilities then Label variable 2 In the Variables box select the name of the variable name ae 3 In the Attach label to variable up to 80 characters box type 71 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Replace missing values with a mean value Adult equivalents 4 Click on the copy butto
118. n switch to the do file editor paste the command switch back to the dialog box and click on the Ok button Add a comment to explain what you have done in the do file The Stata commands are generate ae 1 if ca4 1 amp ca3 gt 10 replace ae 84 if ca4 2 amp ca3 gt 10 amp ca3 lt 19 replace ae 72 if ca4 2 amp ca3 gt 20 replace ae 6 if ca3 lt 10 label variable ae Adult equivalents To verify that the new adult equivalent variable ae has been calculated display a frequency table for it 1 From the menus click on Statistics then Summaries tables amp tests then Frequency tables then One way tables The tabulate1 One way tables dialog box opens 2 Select the variable name ae in the Categorical Variable box which is found under the tab labeled Main 3 Check the box Y next to Treat missing values like other values 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on the Ok button The Stata command is tabulate ae missing You should see there are 1524 total cases Ideally there should be four values represented in the table 1 72 84 and 60 and no missing cases You can see we have nine missing cases This tells us that our data file is missing either the age or the sex for nine people This problem should have been identified during the cleaning process At this point it would be ideal for the research
119. n Open 106 Stata 13 Sample Session Section 3 Tables and other Types of Analysis 10 Select c_q1a dta and click on Open Copy the command to the do file editor Delete the reference to the folder Check to see if there are any missing values in the age variable ca3 Use the list command list if ca3 gt We are checking to see if there are missing values because Stata considers missing values to be greater than any number Select Create or change variables from the Data menu Select Create new variable The generat Generate a new variable dialog box opens Under the Main tab type the name of the new variable in the Generate Variable box age18p For the Contents box type in ca3 gt 18 Click on the Generate variable as type drop down box and change to byte Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok Run a tabulate to look at the results Note if there had been a missing value for an observation that observation would have been assigned a value of 1 It would have been better to put a qualifier on the command to assign the values to cases where ca3 was not missing e g ca3 gt 18 amp ca3 lt The command would be generate byte age18p ca3 gt 18 amp ca3 lt Then any missing values in Ca3 would also be missing in the new variable age18p 107 Stata 13 Sample Session Section 3 Tables and other Type
120. neceaneneaes TAE foreach OOpPINGscOMMANG aces sasan a a Te aa Bish a etter a e E ae THe levelsof command isc scant ennaa e ena a e Na OA Examples of the foreach looping COMMANA 0 ecececccsesesessssesesescsnsssesescsessesesesesescesssenssesescsueseseseecaescesssesesescaeeceteneneaes SECTION 3 Tables and Other Types of Analysis eeeseeeeseeeeesesesesesssestsesssrstsestststsestsrsssttesrsestetssstretesssttetssrereesssrneseseets Comparison of the commands summarize tabulate and table ooo ccc cs csesseseseecsesceseseecseseeseseseseeeeesnenens Print a table from the Viewer Multiple Response Que tions ninini 06 side e beater thas eases aes eaten Leen ate tase an aaa ethos Bakes 1 Multiple dichotomy yes no questions 202 ccc cceesesesesneseseesesesucessuseeseeseeesnceesuseeseeneaeeseaesusesaeeesaeeneetaneeees The count command isi 382s 4 ache sein wed beeen deals leek ibid hie Gel a eee uel asada The recode command c cece The egen command eeceeteeeteteeteteeeeees The tabstat command 2 Multiple response 0 0 0 cee Other Types of Analyses Weights tinretes tees nactdabelasteh Mel Minha pade halal alate bee tobe e a lattes a Meh baa tanh a e aa Indicator variables 0 0 ecce sein A edie de Converting continuous variables to indicator variables Converting categorical variables to indicator variables SECTION 4 Tables and Graphs copying to a word processor Overlaid graphs
121. nerate the table from section 2 and adjust the commands as necessary to calculate the calories 90 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation retained Changes must be made for file names and variables e Computing the calories sold involves the same basic steps as computing the calories produced Step 1 Average calories sold should be 1 407 493 f Merge this newly created file the file containing calories sold with the file containing calories produced hh_file3 dta Check the merge variable tab merge and explain why you see a value of 2 and a value of 3 g Keep in mind that only 257 households sold products but all 343 households produced and retained calories If the calories sold variable is missing it means the household did not sell food so it should be recoded to zero h Compute calories retained calories produced calories sold The average calories retained per adult equivalent for the whole population should be 3044 261 Rank the calories retained variable into quartiles Use the Tabulate command to show calories retained by district and quartile k Save the data file to the name hh_file4 dta l Save the contents of the do file editor a vt Below is an example of the output you should produce gt district monapo quarts 1 Variable Obs Mean Std Dev Min Max cret_ae 28 1171 714 420
122. nt 1 From the menus click on Data then Data utilities then Count observations satisfying condition The count Count observations satisfying condition dialog box opens 2 If the box below If expression type h64 4 Copy the command and paste it in the do file editor switch back to the dialog box and click on OK The command is count if h64a In the Results window you see the value of 86 You could also run a frequencies tabulate h64a The tabulate shows that 147 did not increase sales of maize as well as 86 households who did Now we change the question B How many crops increased in sales within the household For this question we can sum the number of 1 s in the variables associated with this question using the egen command We need to recode the value of 2 no to 0 100 Stata 13 Sample Session Section 3 Tables and other Types of Analysis The recode command The egen command Recode 1 Select Create or change variables from the Data menu 2 Select Other variable transformation commands then Recode categorical variable The recode Recode categorical variable dialog box opens 3 Under the Main tab click in the Variables box and select all the variables that start with h64 e g h64a h64b h64c h64d h64e h64f h64g h64h 4 Inthe box for Required type 2 0 5 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on
123. o you want to continue and lose unsaved data We don t want to save any changes to the data file click on Yes to continue 2 Select census dta 3 Click on OK Remember to copy the command and paste it into the do file editor Use the Browse button to look at the data There is one observation for each state The variable called pop is the total population for the state The variable called medage is the median age of the population First let s get the population weighted mean 1 From the Statistics menu select Summaries tables and tests then Summary and descriptive statistics then Summary statistics The Summarize Summary Statistics dialog box opens 2 Select medage in the Variables box 3 Click on the Weights tab Note that only 3 types of weights are available 105 Stata 13 Sample Session Section 3 Tables and other Types of Analysis Indicator variables Converting continuous variables to indicator variables to choose from There is also a help button on weights 4 Select Analytic weights In the Analytic weight box select pop 5 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Submit yi Look at the output The sum of the weight is 225 907 472 This is the population of the U S in the 1980 census The weighted mean is 30 11 Now return to the dialog box 6 Click on None under the Weight tab
124. oard using the option E provided in the dialog box where commands are built and then switch to the Do file Editor to paste the command d You can copy commands from the Results windows into the Do file Editor using lt Ctrl C gt to copy what you have blocked in the Results window and then switching to the Do file Editor and pressing lt Ctrl V gt to paste the command that was copied from the Results window e In the Review window if you lt Right Click gt there is an option to Send to Do file Editor This option is only good if you have not been building a do file You can select all the commands in the Review window and choose this option A new do file opens and the commands are copied to that do file Every time you Send to Do file Editor a new do file is created This option will not send the commands to a do file that is already open Comments can be placed in the do file as you copy and paste commands Comments in a do file can start with an asterisk if the comment is one line If the comment covers several lines use before the comment and end with so that STATA will not think the comments are commands You can also use a double slash This option is useful if you want to add a comment after a command Example of the various styles of comments are 10 Stata 13 Sample Session The doedit command Discussion of the Windows used in STATA A The Do file Editor Section 0 File structure and Basic Ope
125. ombining data from more than one file Let s look at an example Suppose we want to create a table of calories per adult equivalent produced per day from the principal food crops Furthermore we want to see how this varies by district and calorie production quartile TABLE 1 Food Production in calories per adult equivalent per day Districts Monapo Ribaue Angoche Calorie Production Quartile 2 3 4 The data in their current form cannot produce this table Many transformations are required to restructure the data to be able to provide the results for this table The above table is an example of the complications you will encounter in real world data analysis This entire section will be devoted toward the goal of creating this table To begin let s look at the files we have and at the variables we need to use from each of these 54 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation e c q1a dta This file contains data on household roster characteristics It is at the household member level We need to use the variables ca3 age and ca4 sex in this exercise to compute the number of adult equivalents per household e c q4 dta This file contains data on crops produced by the household The variables we need to calculate the total production of the household are a prod contains the codes for the agricultural crop produced b pia contains the codes for the
126. on the copy button switch to the do file editor paste the command delete the folder reference switch back to the dialog box and click on Ok 6 In the do file insert comments to remind you what you ve done The Stata command is merge 1 1 district vil hh using hh_file1 dta In the results window you see a summary of the number 77 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Calculate the total calories produced per adult equivalent per household for the year of observations not matched and matched Result of obs not matched 0 matched 343 _merge 3 Now that you have run the merge run a tabulate on the _merge variable You can abbreviate the name to _m e g tab _m You should see only the value of 3 for 343 observations That means that there was an observation for each district vil hh combination in each of the two files Merge files created a new working data file The two variables you need to compute calories produced per adult equivalent are now in the working file Total calories produced cprod_tt per household for the year divided by total adult equivalents per household ae divided by 365 days per year gives us calories produced per adult equivalent per day cprod_ae 1 Select Data then Create or change variables then from Create new variable The generat Create a new variable dialog box opens 2 Ifyou see
127. only those records in the using file that match the data in the master file 1 From the Data menu select Combine datasets then select Form all pairwise combinations within groups The joinby Form all pairwise combinations within groups dialog box will open 2 To fill in the box labeled Filename of dataset on disk click on the Browse button Select the filename conver dta and click on Open 3 In the box labeled Join observations by groups formed from specific variables optional select prod unit 4 Click on the Options tab 5 Under Unmatched observations select Include from data in memory This option will keep cases in the master data set in memory that do not have a match in the lookup data set 6 Click on the copy button switch to the do file editor paste the command delete the directory reference that appears in the part where the data file is selected switch back to the dialog box and click on Ok The Stata command is joinby prod unit using conver dta unmatched master _merge _merge The above command tells Stata to merge the working data file or master the file in memory with the conver dta file or using data file using conver dta as a table lookup We had renamed p1a to unit The variable conver will be added to the working data file at the end of the variables Key variables are required in any procedure to merge two files when one of the files is being used as a keye
128. ons From the Menu Select File then Log then Begin Stata asks for the name of the log file You can change the folder where you want the log file by navigating to the folder you wish to use then entering the name of the log file in the box next to File Name Note that the default extension is smcl There is a GUI button on the tool bar 4 icon from the left You can click on that icon to start a log You can also open a log by typing the command in the Command window log using session1 append The SMCL log file can only be opened in the Stata Viewer If you wish to create a log file that can be opened in any word processing program then you must specify the extension name of log In the example below a log file will be started with the extension name of log log using session1 append text The above command opens a file to record the session and uses ASCII format This file can be opened in any text editor or word processor Stata has provided a translate command to convert smcl log files to plain text With this provision you can always share your log file with others who might not have Stata The command is translate session1 smcl session1 log The other type of log file records only the commands not the output from the commands The command is This command creates a file that records only the commands In the Stata Command window type cmdlog using session1 append A file is opened which
129. ow many calories are produced by each household not kilograms Thus after converting all production to kilograms we must convert kilograms to calories Third an examination of the file shows that we have data for each product produced by the household But we want to know the total calories produced by the household for specific food products not the total calories from each separate product After we convert all production to calories we need to select only specific food products and then sum the calories within each household to create a household level file that contains the total calories produced Let s begin by creating a new do file Open the Do File Editor Start by including comments about the purpose of the do file your name as the creator of the do file and the date Other items to include are the Stata version the cd command to switch to the directory where you want to work the log command to record the session Example change to directory where files are stored cd C Users Documents StataTraining data define the log file to capture output capture log close log using log_session2 append Purpose of do file Author and date Tasks to be done in this do file program setup version 13 clear all macro drop all We are now ready to open c q4 dta the production file 1 Select File Open 2 Select the file name c q4 dta 3 Click on Open to run the command
130. ox cprod_tt should be selected 3 In the New variable label may be up to 80 characters box type Calories produced in staple foods 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on the Ok button 5 Run the describe command again The Stata commands are describe label variable cprod_tt Calories produced in staple foods describe The new working data file now contains what we need total number of calories from staple foods produced per household We can also look at this variable by doing a descriptives Use the Summarize command to run a mean on the new variable cprod_tt You should find that the average number of calories produced per household per year is 4 483 965 Save this data file using the Save As command Use Save As from the File menu Name the file hh_file1 Click on Save Copy the command from the Review window and paste it into the do file editor delete the reference to the folder and add a comment to explain what you have done eee Remember to save your do file regularly You must be in the Do file editor to save the do file Step 2 Generate a household The data needed to calculate adult equivalents per level file containing the household is in the member file C Q1A DTA number of adult equivalents per household 1 Click on the Open icon on the Stata Toolbar 2 Select the file name c qla dta and
131. p fsg afre msu edu index htm The Survey Research Training Materials link can be found by scrolling down to the end of the page There are two papers that discuss levels of data which is an important concept to understand when working with survey data to handle the data properly 1 Computer analysis of survey data File organization for multi level data by Chris Wolf MSU Department of Agricultural Economics This document can be downloaded as a separate document in English or French 2 Data Preparation and Analysis by Margaret Beaver and Rick Bernsten June 2009 CDIE reference number pending Another article of interest which contains guidelines to manage the data data verification techniques and preparation of data for analysis is Survey Data Cleaning Guidelines SPSS and Stata 1st Edition Margaret Beaver MSU International Development Working Paper 123 April 2012 Acknowledgments Funding for this research was provided by the Food Security III Cooperative Agreement between the Department of Agricultural Food and Resource Economics at Michigan State University and the United States Agency for International Development Global Bureau Office of Agriculture and Food Security Stata 13 Sample Session Section 0 File structure and Basic Operations for Stata 13 SECTION 0 File structure and Basic Operations for Stata 13 How Stata uses Memory ssoi iruna Mise aeaa a n tna easier a COMPrESS sioa e aT OANA RR Ra i A A A
132. processor The table will be now in a Table in the word processor where you can easily manipulate the widths and other formatting as required Below is an example of output from the Results window converted to columns in Excel and pasted into Word Relation to head Oto 10 11to19 20to60 61 and older Total head 0 6 296 41 343 0 1 75 86 3 11 95 100 0 2 22 47 13 83 67 22 57 wife husband 0 25 280 5 310 0 8 06 90 32 1 61 100 0 9 26 44 59 10 2 20 39 son daugher 503 184 31 0 718 70 06 25 63 4 32 0 100 87 78 68 15 4 94 0 47 24 mother father 0 0 5 1 6 0 0 83 33 16 67 100 0 0 0 8 2 04 0 39 other relative 70 55 16 2 143 48 95 38 46 11 19 1 4 100 12 22 20 37 2 55 4 08 9 41 Total 573 270 628 49 1 520 37 7 17 76 41 32 3 22 100 100 100 100 100 100 Exercise 4 1 Select another table from your Session3 SMCL file Use all of these methods to copy another table from your log file into a word processor Graphs The process to copy output to a word processor is basically the same for Graphics such as pie charts and histograms but there is more flexibility in the ways to save the file along with more difficulties in getting just the look you want As an example 111 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation we will look at the distribution of cashew tree ownership across households in the Mozambique data using a histogram Open a new do file and place the requisite information at the top e g
133. prod 30 measured in a 20 liter can weighs 17 kg beans prod 30 measured in a 50 kg bag weighs 47 kg 55 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation 7 l 7 30 30 prod unit conver Product unit conversion factor calories dta This also is a table lookup file created for convert kilograms of food into calories of food It contains two variables a prod the product crop b calories number of calories per kilogram of each of the crops To create a data files that will produce the output table described above we need to combine the data from different files There are different methods that can be used to combine files depending on what is desired In Stata we can Append datasets Appending data sets means that the data in different files have the same variables and the desire is to add one data set of observations to the end of another data set or append one file to the end of another file An example would be that you entered data for harvest in one file for one district and entered data for harvest for another district into another file We want the data to be in the same file To do that we would use the append command Merge datasets Merge combines datasets horizontally matching corresponding observations An example is a survey asking questions about the household in Part 1 and another set of questions about the household in
134. r paste the command and switch back Click on Ok to run the command The Stata command is generate int agecat recode ca3 10 19 60 100 Run a tabulate on the new variable agecat and compare the number of cases in each category between the new variable and the age_gp variable The numbers will be the same You would need to add a variable label and create a value label with labels associated with the values to complete the variable These new variables are not yet part of the data file stored on disk We must save the data file for these variables to be included permanently in the data file It is a good practice to save the file under a different name in case we want to go back to a previous version of a file For this reason we will use the Save As command from the File menu The new file name will be Q1A AGE DTA 1 From the File menu select Save As The cursor should be in the box under File name above the Save as type Stata data DTA drop down box Since dta in the File name area is blocked you can immediately start typing the new file name 2 Type qla age The DTA extension will be added automatically 3 Click on Save to run the command The Stata command is save qla age dta Copy this command from the Results window to the do file editor We do not want to include the specific directory so delete the part of the command that references the specific directory If we want to share the do file
135. r the variable select cprod_ae Click on the drop down box next to 3 and scroll down to Minimum and select that statistic For the variable select cprod_ae Under the Options tab check Y Add row totals and also check Add column totals To format the numbers check Y next Override display format for numbers in cells Click on the Create button to the right of this box In the Create a display format dialog box check v Customize format and change the contents to read 11 2fc The Help format button shows different formats that can be specified This format says to use a width of 11 with 2 decimals fc means fixed format with a comma Click on Ok 97 Stata 13 Sample Session Section 3 Tables and other Types of Analysis 12 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Submit 13 Looking at the Results we decide we don t like the 2 decimals Go back to the dialog box Click on the Create button to the right of this box and changes the decimals to 0 Click on OK Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK 14 Return to the do file to write the comments to explain the difference For each district the first row is the mean the second row is the maximum and the third row is the minimum The three Stata commands are by district quartile sort summ
136. radio button next to Selection Then click on Print 5 Another dialog box opens labeled Output Settings In this box you can specify a Header a Name and a Project If you do not want line numbers and the Stata logo to print you should remove the ticks next to the boxes labeled Print Line s and Print Logo 6 Click on OK to print the selection iv Produce a similarly formatted table using calories retained using the data file that was created in Exercise 2 1 Include totals by retained quartile Your table should look similar to the table below Calories retained quartile district 1 2 3 4 Total monapo Lr ETL 2 239 09 3 343 00 7 619 10 3 571 01 1 806 87 2 554 89 4 303 12 20 873 97 20 873 97 224 49 1 888 33 2 685 97 4 359 74 224 49 ribaue 1y 2915 39 2 171 70 3 165 37 5 828 97 3 081 46 1 790 43 2 566 01 3 734 49 9 464 90 9 464 90 429 29 1 835 30 2 578 60 3 825 88 429 29 angoche 929 42 1 718 79 2 442 25 5 022 29 2 506 50 17395796 1 984 06 3 064 00 12 674 86 12 674 86 207 91 1 447 06 1 997 71 3 134 74 207 91 Total 1 118 42 2 040 13 2 971 30 6 135 48 3 044 26 1 806 87 2 566 01 4 303 12 20 873 97 2 0 4 87397 207 91 1 447 06 Ls GOT td 3 134 74 207 91 Multiple Response Questions 1 Multiple dichotomy yes no questions One of the types of question used in survey research asks the respondent to select multiple answers A single variable cannot record the answers to this type of question adequately because a v
137. rations for Stata 13 your name here and the date the file was created do file to examine variables using the methods of Tabulate and tab1 describe describing the variables in the file Within the Do file Editor you can submit several commands at once In the Command window only one command at a time can be submitted for execution You cannot send commands directly from the Stata Command window to the Do file Editor The command must be copied There are 2 ways to open the Do File editor 1 From the Button Bar you can click on the Do file Editor button a Another window opens which is the Do file editor 2 From the Command window you can type It is important to recognize the significance of the different types of files and to understand the various commands you use to create and access the files The Do file Editor is the window where commands can be typed before they are submitted to the STATA processor Commands can be typed directly into the Do file Editor You can copy the command to the clipboard using the option provided in the dialog box where commands are built s and then switch to the Do file Editor to paste the command or you can copy the commands from the Results window and paste the commands into the Editor There are four main uses of the Do file Editor To type commands directly into the Do file Editor to be processed later by STATA To send these commands to Stata 13 for proce
138. re is another command which can also be used to produce the final table We will discuss this command in Section 3 of the tutorial Before you save the file you should sort the file by the key variables then save this file as hh_file3 dta 1 Sort the file by the key variables Type in the Command window sort district vil hh 2 We no longer need the variable merge so it should be dropped Type in the Command 84 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Document the do file with comments window drop _merge Click on File Save As Filename is hh_file3 Click on Save Copy the three commands and paste them into the do file 7 Close the log file Type in the Command window log close 8 Copy this command into the do file DN ee oe Remember to save the contents of the Do file Editor to a permanent file so you can use it another time 1 The Do File Editor should be the active window 2 Click on File Save As 3 Use the filename session2 The do extension will be added automatically This file now contains all the commands that you pasted either from the Command window or from the Review window or from dialog boxes Note Whenever you do any substantial amount of work you should always copy the commands to a do file and save the file so that you have documentation on what analysis you have done and so you can repeat the analysis without bui
139. ribution of the values 1 From the menus select Graphics Histogram The dialog box opens labeled histogram Histograms for continuous and categorical variables 2 Click on the drop down arrow for the Variable box and select Ca3 The default for Stata is to assume the data are continuous 3 Tick the box v for Width of bins and type in 5 in the box next to this option The ages will be grouped into 5 year ranges 4 For the Y axis click on the radio button next to Frequency We will see the number of cases in the different age groups 5 Click the copy icon and then click on Ok to run the command A new window opens labeled Graph Graph All graphs produced by Stata open in a separate window Only one graph can be displayed at a time The Stata command is histogram ca3 width 5 frequency See a copy of the graph below If you want to save this graph to a word processing document you can lt right click gt on the graph select Copy then switch to your word processor and paste it into the document lt ctrl v gt If you want to save the graph to disk lt right click gt and choose Save As Note Only one graph appears in the graph window at a time If you run multiple graph commands at one time from a do file only the last graph will be visible You must run one command save or copy the graph then run the next graph command save or copy that graph For a more detailed description of the sub
140. ring a normal year is your farm production sufficient to feed your entire family l yes 2 no 124 Stata 13 Sample Session Annex I I Survey Instrument We would like to ask you about the cash crops you grow on your farm H34 34 Do your grow any crops that are principally destined for the market l yes 2 no 35 Which crops are grow principally to be sold List the three most important H35A 1 cotton 4 sunflower H35B 2 peanuts 5 rice H35C 3 sesame 6 other H36 36 Over the last five years have you changed the area grown in these cash crops 1 increased 2 decreased 3 no change H39 39 Do you normally grow cotton l yes 2 no H52 52 Since your involvement with the cotton companies have you reduced your area dedicated to food crops such as maize and manioc l yes 2 no IV PRODUCTION H56 56 Do you have cashew trees l yes 2 no H57 57 How many trees do you presently have number H57A 57A Of these trees from how many did you harvest during the last year number vV AGRICULTURAL SALES We would like to ask about the marketing of your agricultural products since August of 1990 64 Over the last five years have you increased the quantities marketed of the following crops H64A a maize l yes 2 no H64B b manioc l yes 2 no H64C c rice l yes 2 no H64D d cotton l yes 2 no H64E e peanuts l yes 2 no H64F f beans l yes 2 no H64G g sorghum l yes 2 no H64H h cashew nuts l yes 2 no H65 65 Compared with five
141. rod only 4 Click on the Options tab 5 Under Unmatched Observations select Include from data in memory This option will keep cases in the original data set that do not have a match in the lookup data set 6 Click on the copy button switch to the do file editor paste the command delete the folder reference switch back to the dialog box and click on Ok 7 Add comments to the do file The Stata command is joinby prod using calories dta unmatched master _merge _merge The new working data file produced by the merge now contains the needed calorie variable calories but check 64 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Calculate the total calories produced Assign variable labels to make sure Maize grain PROD 47 should have the value of 3590 in the calories variable We can browse the data and or we can use the list command again The Stata command is list prod calories if prod 47 Also check the _merge variable to see how the merge was done Note that there are 87 cases with no value in the calorie variable How would you check to see which products have no calorie value We can now compute total calories produced 1 Select Create or change variables from the Data menu 2 Select Create new variable The generate Create a new variable dialog box opens We have used this dialog box earlier To clear the contents clic
142. s as though they are commands if the comment begins and ends with these symbols To create a two way table do the following 1 From the menus click on Statistics Summaries tables amp tests Frequency tables Two way tables with measures of association The tabulate2 Two way tables dialog box opens 2 In the Row variable box choose ca2 from the drop down choices 3 In the Column variable box choose ca4 from the drop down choices We would like to see row percentages and column percentages 4 Under Cell Contents click in the box next to Within column relative frequencies to put a Vv 5 Click in the box V next to Within row relative frequencies 6 Click on the copy button switch to the Do File Editor and paste the command Write a comment and then switch back to the dialog box to click on the Submit button The command will be executed The Stata command is tabulate ca2 ca4 column row 39 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Key frequency row percentage column percentage relation to head The Key box in the Review window specifies which statistics appears on each row in the cells head wife husband son daughter mother father other relative How many total cases are included Only 1 504 cases are included in this table What about cases with missing values 1 Return to the di
143. s from quartile1 quartile2 quartile3 into quartile levelsof district local levels foreach z of local levels replace quartile quartile z if district z delete the 3 extra variables levelsof district local levels foreach z of local levels drop quartile z tabulate quartile district label variable quartile Calorie production quartile produce the table by district quartile sort summarize cprod_ae sort file by key variables sort district vil hh save hh_file3 dta replace close log file log close Exercise 2 1 Produce similar output using calories retained production minus sales instead of calories produced The final output should show calories retained per adult equivalent per day from the total of the same seven food crops The output should be broken down by district and calories retained quartile Hints a The procedure is very similar to the work that we just completed You can continue with the same do file that we used for the Session2 or you can create a new do file Sales data can be found in the file c q5 dta Be sure you understand which variable contains the quantity of the product that is sold Note that the product codes are the same as for c q4 dta Also check for the variables by which to sort You can start from a blank file and build all the commands necessary to produce the calories retained or you can copy the commands used to ge
144. s of Analysis Converting categorical variables to indicator variables Suppose that you want to do regression analysis and control for effects of the different geographic regions We have a variable called district which has 3 categories We want to create indicator variables for the three districts These types of variables are also called dummy variables First let s run the describe command to look at the contents of the file describe Next let s look at the values and labels for the variable district label list district To make 3 indicator variables we can type tabulate district generate district Now run the describe command again describe Three new variables have been created called district district2 and district3 We can examine the variables using the tab1 command tab1 district The variables district1 district2 and district3 can now be used for regression analysis as dummy variables They contain either a 0 ora 1 108 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation Stata 13 SAMPLE SESSION SECTION 4 Tables and Graphs copying to a word processor Overlaid graphs Survey estimation to account for design effects How to move Stata results The objective of this section is to give you the tools necessary to prepare reports i e to learn how to move Stata results into into other appl ications other applications The method is simple once a graph
145. s survey are Males 10 years and older 1 0 Females 10 to 19 years old 0 84 Females 20 years and older 0 72 Children under 10 years old 0 60 The generate if command is used to compute the adult equivalents for each member We will name the adult equivalent variable that we create as ae 1 Select Create or change variables from the Data menu 2 Select Create new variable The generate Generate a new variable dialog box opens 3 Under the Main tab type the name of the new variable in the Generate Variable box ae For the Contents box type the value of 1 Click on the if in tab Type the statement ca4 1 amp ca3 gt 10 Click on OK Soy From the results window you should see that 1003 70 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation missing values were generated from the command Now that the new variable has been created another command is used to assign the codes for the other adult equivalent groups that have not yet received a value We use the replace command 8 Select Create or change variables from Data 9 Select Change contents of variable The replace Replace contents of variables dialog box opens 10 In the Variables box select the name of the variable that was just created ae 11 Type 84 in the New Contents box 12 Click on the if in tab 13 In the Restrict observations if expression box type in ca4 2 a
146. s type of analysis helps you to find data entry errors It also gives you a feel for what kind of data are in the file and to see that missing values have been defined correctly It may be tempting to skip this step for some data sets or for some variables but this is an important step that will almost always save time later and improve analysis For example finding out the average age of all respondents may not be something you are interested in knowing but if the average age turns out to be 91 3 years you would be alerted that that something is probably wrong with the data Basic descriptive statistics can be obtained from two commands Summarize and Tabulate Summarize is used for continuous variables while Tabulate is used for categorical variables 27 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Continuous variable Categorical variable Indicator variable Descriptive statistics using one variable l There are three types of variables A continuous variable is a variable that does not have a fixed number of values It measures something e g age weight population The variable ca3 age is a continuous variable because age can take on many different values A categorical variable is a variable that has a limited number of values that form categories or groups e g geographic location or relation to head For example look at the Annex Table IA Hous
147. s window lt PageUp gt recalls the last command run and places it in the Command window If you continue to press lt PgUp gt the next command above will be placed in the window lt PageDn gt moves back down through the commands that appear in the Review window lt Esc gt clears the contents of the Command window The Viewer in Stata is used to view help files and log files and to print these files To enter the Viewer click on File View The Choose File to View dialog window opens You can type the name of the file or click on the Browse button By default the file type extension name is SMCL Files smcl Select the file you want and click on The file name is pasted into the dialog box where you can then click on If you decide to use Help from the menus the Help files are opened in the Viewer A graph is opened in its own window and is not stored in the Results window If you wish to keep a graph you can copy the graph to a word processing document or you can save the graph to a file Right click on the graph to see these options A graph file has the extension gph 17 Stata 13 Sample Session Summary of the Basic File Types Do file files Section 0 File structure and Basic Operations for Stata 13 Do file files or command files contain commands saved in the Do file Editor They do not contain output or data only commands Do files are made accessible to Stata if you open the Do
148. scriptive statistics by doing the following exercise Run descriptive statistics on another of the provided sample files Use the production questionnaire Table IV The data are in file C Q4 DTA Hints a make C Q4 DTA your working data file b Use the summarize command for continuous variables and tab1 for categorical variables prod is a categorical variable Quantities p1b p2b are continuous variables Units pla p2a are categorical variables p4 month in which stocks ran out last year amp p6 month in which stocks will run out this year are categorical variables ho ad A small sampling of what you should find from running these frequencies and descriptive statistics follows Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Tabulate product Percent cotton peanuts rough rice bananas sweet potato cashew liquor sugar cane liquor dried cashew sugar cane cashew nut coconut beans manteiga beans sunflower oranges cashew fruit manioc sorghum maize ossura tobacco tomato 90 vol hi 95 71 42 65 12 escla 68 66 48 41 30 wt 60 96 ede 34 30 24 TT fe SPOCOFPNONDOODANINDCOCFPONWOOASE Total Summarize Variable 00 Mean Std Dev plb p2b p3b p5b p7b Descriptive Statistics using two or more variables Two way Tables with Categorical Variables Cross tabulation
149. ses Decide which of the variables in this file are continuous and which are categorical normally you would refer to the questionnaire to make this decision You need to know this in order to select the correct command to use for each variable If you mistakenly perform a tabulate on a continuous variable you will probably get more output 28 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations than you really want with possibly hundreds of different categories one for each different value found If you perform a summarize on a categorical variable you will usually get meaningless results since the average value of a variable that uses the numeric value to describe categories has no real significance By examining the data you should have found that variable Ca3 age is continuous and the remaining variables are categorical To run descriptives on ca3 do the following Descriptives 1 From the Statistics menu select Summaries tables amp tests then Summary and descriptive The summarize command statistics then Summary statistics This will open the summarize Summary statistics dialog box This command is also available from Data Describe data Summary Statistics 2 The cursor should be in the Variables box There is a dropdown arrow at the end of the variables box x Click on the drop down arrow to select the variables you want Highlight ca3 and clic
150. ssing To save these commands to a file to be run again in the future and To retrieve files of commands that you have saved previously so that you can run them again without the need to rebuild the commands It is important to understand that the commands you put in the Do file Editor will not be executed no output will be produced until you send the commands to the processor The Do file Editor is simply an area that helps you prepare the commands To send the commands to the processor you use the Execute Do icon in the Do file Editor window toolbar This command runs the 11 Stata 13 Sample Session B The Data Editor Window Section 0 File structure and Basic Operations for Stata 13 commands in the current do file and shows the output in the Results window Another icon to the left of this icon called Execute quietly Run E also executes the commands in the current do file but does not show any output in the Results window Choosing either one sends all the command s to the processor which reads the commands written in the Do file Editor and executes them To send only specific commands block the commands you want to send and select the Do icon When you have successfully completed each step in your analysis or when you are ready to end a STATA 13 session even if it was not completely successful you should save the commands to a file for future use To save the commands make the
151. t 6 In the New variable label map be up to 80 characters box type Total calories produced 7 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on the Ok button 8 Add a comment to the do file to explain what you have done The Stata commands are label variable qprod_tt Total production in kgs label variable cprod_tt Total calories produced This gives us a new working data file with total calories produced per product for each household The final output table asks only for information about the staple food crops These are defined as peanuts prod 5 rice prod 6 nhemba bean prod 30 manteiga bean prod 31 manioc prod 41 sorghum prod 44 maize prod 47 We can find the product code by looking at prod in the questionnaire Since we are only interested in those products we need to exclude the rest of the cases about other crops Stata uses the keep command Once you run this command you will no longer have the complete data set available You must remember that you should never save a file to the same name after you have selected out a set of data You will overwrite the original data and 66 Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation no longer have the complete set The keep if command To select just a subset of cases 1 Click on Data then Create or change variables
152. t s run the same analysis with only the weight specified to see the difference Click on the tab labeled SE Cluster then click on the button labeled Survey settings Click on the button labeled Clear settings Click on the Weights tab Click on the radio button next to Sampling weight variable Click on the drop down arrow for the Sampling weight variable box and select hhwgt Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK Click on the task svy total on the Windows task bar Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on OK Note we have gotten the same point estimate as the design based estimate but the standard errors are much smaller and the 95 confidence interval is also different The second table does not account for the sampling design but assumes the sample is random rather than stratified random vce linearized singleunit missing maizea ricea milleta sunfa running total on estimation sample Survey Total estimation Number of strata Number of PSUs Number of obs 6601 Population size 807414 Design df 6600 Total Linearized Std Ber 95 Conf Interval maizea ricea milleta sunfa 649230 9 14472 95 61770 91 24319 15 14013 13 621760 6 676701 2 1327 559 T8705 17075 39 3942 684 54041 97 69499 84 1907 919 20579
153. t record a different value in each of the 4 variables These types of variables are called multiple response variables Question 35 in the household questionnaire is an example of a multiple response question It asks about crops grown principally to be sold Each household is asked to specify up to three main crops which are coded into variables h35a h35b and h35c Codes are provided for five of the 102 Stata 13 Sample Session Section 3 Tables and other Types of Analysis most common crops The question is left open ended however since a code of 6 is allowed for a crop not on the list The name of the crop is written down on the questionnaire and later assigned a code Because the question was open ended more categories were added to these variables than what appears in the annex After the data are collected the researcher assigns a code to each of the crops specified for 6 other this procedure is called post coding Codes and value labels are entered into the data file and the data changed from the value of 6 to the appropriate code As you will see using the tab1 command eleven different crops were specified for question 35 Stata does not have a command included in the application that will tabulate data collected in this format We can do frequencies of each variable or develop commands to pull out specific information Since Stata is open source anyone can write a procedure to do different analyses T
154. t district 10 Click on the OK button to view the graphic What are these graphs telling you The Stata commands are twoway scatter ae cprod_tt twoway scatter ae cprod_tt by district twoway scatter ae cprod_tt Ifit cprod_tt twoway scatter ae cprod_tt Ifit ae cprod_tt by district twoway scatter ae cprod_tt qfitci ae cprod_tt twoway scatter ae cprod_tt qfitci ae cprod_tt by district Stata provides statistical commands that have been developed specifically for survey analyses The Stata User s Guide discusses these commands as well as the manual called Survey Data Most of these commands begin with the letters svy There are a few of the survey commands that do not begin with these letters Survey data generally have three importance characteristics 1 The weights applied to survey data are sampling weights also called probability weights 2 The sample is clustered 3 Stratification is used in selecting the sample If data meets any one of the above characteristics the survey 115 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation commands can be used for analysis Briefly sampling weights are used in analysis to give estimators that are approximately unbiased for whatever is being estimated for the whole population i e one observation represents many elements in the population from which the sample is drawn Clustering by districts or villages is us
155. t just above the use command We also want to add comments to define what the purpose of the do file Above the command to open the data file you can type what the purpose of the do file is your name and the date you created the do file as well as any other comments that will help you remember what the do file is for Example session basic functions descriptives your name here the current date here example beaver 6 Jan 2014 member level file Other commands that are important and should be included are commands to close any log file that may be open clear the memory work space and drop all macro variables Below isi an example of the important commands and comments that should be added to any do file that you create change to directory where files are stored cd C Users Documents StataTraining data define the log file to capture output name of log file is log_sessionl 21 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Describing the contents of a data file The describe command capture log close log using log_sessionl replace Purpose of do file Author and date Tasks to be done in this do file program setup version 13 clear all macro drop _all 6 Save the do file From the File menu in Do File Editor select Save As 7 Enter the filename session1 The
156. then Keep or drop observations You should see the drop Drop or keep observations dialog box 2 Under the Main tab select the round radio button next to Keep observations 3 In the Observations to keep if expression box type prod 5 prod 6 prod 30 prod 31 prod 41 prod 44 prod 47 The is a symbol for the word OR We are telling Stata to select all cases with prod equal to 47 double equal or prod equal 30 or prod equal 31 and so on 4 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Ok 5 Add a comment to explain what you have done The Stata command is keep if prod 5 prod 6 prod 30 prod 31 prod 41 prod 44 prod 47 Only cases with these product codes will now be used for analysis Note that 464 observations were dropped You can use the tabulate command to verify that you now have only 7 crops in the file In the Command window you can easily type There should be 1 239 cases remaining for the 7 crops that are considered staple foods Now we need to know how many calories were produced per household for all these 7 staple food products combined To do this we need to sum for each household the values of cprod_tt for all of the food crops the household produced In other words we need to create a new household level file where there is only one case per household from the current
157. thre et e ia 8 SAT a ah tS Ut Bats Al et ess A eS tel at head 6 296 4 343 wife husband 25 280 5 310 son daugher 503 184 31 718 mother father 5 1 6 other relative 70 55 16 2 143 Total 573 270 628 49 1 520 109 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation Below is the same table after the font is changed to a fixed pitch and the font size is adjusted so that the table will fit on the page relation to Age group head 0 to 10 Ti to 9 20 to 60 61 and older Total head 6 296 41 343 wife husband 25 280 5 310 son daughter 503 184 31 718 mother father 5 J 6 other relative 70 55 16 2 143 Total 573 270 628 49 1 520 Copying tables from the Results window relation to head 0 told 11 to 19 head 0 6 0 00 1 75 0 00 2 22 wife husband 0 0 00 8 06 0 00 9 26 son daughter 503 70 06 25 63 87 78 68 15 You can also copy the information from the Results window into your word processor Stata provides three choices from the Edit menu for copying tables Click on Edit to look at the choices 1 Copy text Ctrl C copies the table as straight text and uses plain text for the lines 2 Copy table Shift Ctrl C copies the table and includes tabs where it thinks there should be tabs no lines are included You can try to convert this into a table within Word It will require a bit of editing 3 Copy table as HTML Shift Ctrl Alt C copies the table into HTML format
158. tion 2 Restructuring Data Files Table Lookup amp Aggregation with system missing generate quartile 5 We now replace the data in quartile with the data from the 3 variables created with the xtile command Remember we must rerun the levelsof command as well since that information is just temporarily stored in memory for the previous command Type the following lines block and run them replace values in quartile with information from the 3 quartileZ variables created above levelsof district local levels foreach z of local levels replace quartile quartile z if district z This commands cycle through the values for z and replaces the contents of quartile with the contents of quartile if district is equal to 1 in the first loop then replaces the contents of quartile with the contents of quartile2 if district is equal to 2 in the second loop then replaces the contents of quartile with the contents of quartile3 if district is equal to 3 for the final loop 4 The next step is to delete the temporary variables Type the following block and run the commands delete temporary variables levelsof district local levels foreach z of local levels drop quartile z Always check the new variables that are created to see if the values are what you expect to see We can use the tabulate command with 2 variables district and quartile to check the variables 1 From the menus
159. tton Switch to the do file and press lt Ctrl V gt or right click and choose Paste to paste the command Switch back to the dialog box and click on Ok In the Results window you will see the description of the variables The command to produce the results was describe as you can see in the Review window To obtain the same results without using the menus from the Command window you can type The output shows the file name the number of observations the number of variables the size and then information about each of the variables the storage type the display format the value label and variable label Contains data from c qla dta obs 1 524 vars AL size 73 152 93 0 of memory free storage display value variable name type format label variable label ole district district vil village household member number does this person work relation to head age sex level of schooling marital status where entered district Float vil Float hh Float mem Float cal Float ca2 Float ca3 Float ca4 Float cad Float ca6 Float univ Float AP al o oe AP o o oe ole O LO LO WO OO WOO WO OO WO ole Sorted by An explanation of each of the columns follows Data storage types Storage type Stata has 6 storage types byte integer between 127 and 100 int integer between 32 767 and 32 740 long integer between 2 147 483 647 to 2 147 483 620 float smaller real numbers stored
160. u Close the graph Note you could have also added a title and other options as well Graphs can also be overlaid 1 Select Graphics Two way graphs scatter lines etc You will see Plot 1 already defined 2 Click on the Create button to define a second plot Click on the radio button next to Fit plots Highlight Linear prediction in the box labeled Fit plots select one 3 For the Y axis select the variable ae For the X axis select the variable cprod_tt 114 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation Survey Estimation Accounting for Design Effects 4 Click on the Accept button You now see that Plot 2 has been defined and is highlighted 5 Click on the By tab Remove the mark from the box next to Draw subgraphs for unique values of variables 6 Click on the copy button switch to the do file editor paste the command switch back to the dialog box and click on Submit to view the graphic What are these graphs telling you 7 Close the graph Return to the dialog box highlight Plot 2 and click on Edit 8 Change the type of plot to quadratic prediction plot w Cl Click on the Accept button 9 Click on the Submit button to view the graphic What are these graphs telling you 10 If we want to see the distribution by district click on the By tab check the box next to Draw subgraphs for unique values of variables In the Variables box selec
161. unit in which the production was measured 100 kg sack 50 kg sack etc c pib contains the number of units produced for the year Note that the unit of production is not a standard unit for each crop For example a 100 kg sack as the term is used in Mozambique weighs 100 kg only when the sack is filled with maize When it is filled with manioc root it weighs much less than 100 kg Thus we need conversion factors to be able to convert each of the units in which production was actually measured to our standard unit which is the kilogram e conver dta This is a table lookup file This file was created specifically to handle the problem of converting non standard units to a standard unit For each product unit combination there is a conversion factor to convert the measurement to equal the weight in kilograms In other words there is a different conversion factor for each product unit combination For example the conversion factor for a 50 kg sack of rough rice is 39 44 for a 50 kg sack of cotton it is 17 5 while a 50 kg sack of peanuts is 41 67 The variables in this file are a prod product crop code Unit unit of measure c conver conversion factor equal to the number of actual kilograms for the combination of prod and unit Below a sample of data from CONVER DTA shows that rice prod 7 measured in a 20 liter can unit 8 weighs 19 kg rice prod 7 measured in a 50 kg bag unit 24 weighs 53 kg beans
162. ves Data Transformations 4 Inthe Main tab select ca3 in the Variables box 5 Inthe Required box specify the range you want and the new value to be assigned as well as the label for that new value using the exact format below which includes brackets 0 10 1 0 to 10 6 Inthe Optional boxes continue to specify the ranges and value to be assigned 10 001 19 2 11 to 19 19 001 60 3 20 to 60 60 001 max 4 61 and older Note examples on how to specify the value can be see if you click on the Examples button 7 Click on the Options tab Click on the radio button next to Generate new variables 8 Inthe box type the name of the new variable age _gp1 9 Wecan also specify a name for the value labels Click in the box next to Specify a name for the value label defined by the transformation rules 10 In the box type age_label 11 Click on the copy button switch to the do file editor paste the command switch back and click on Ok Note that the command for this process is on one line in the do file To make the command more readable we can make multiple lines However Stata must know that the command continues to the next line To do this you must add three 3 forward slashes at the end of the line Reformatting the command your do file should look like below recode ca3 0 10 1 0 to 10 Hf 10 001 19 2 11 to 19 I 19 001 60 3 20 to 60
163. which is used on the web 4 Copy as picture copies the text into a picture format which can be converted in Word to be able to edit it as a picture You may encounter problems with the second and third options if you use these The fourth option is difficult to edit Stata determines if there should be tabs and may not make the correct decision You might need to increase the width of the columns in the output to make sure that tabs are included Below is an example of the same table using the Shift Ctrl C Age group 20 to 60 61 andol Total 296 41 343 86 30 11 95 100 00 47 13 83 67 22 57 25 280 5 310 90 32 1 61 100 00 44 59 10 20 20 39 184 31 0 718 4 32 0 00 100 00 4 94 0 00 47 24 110 Stata 13 Sample Session Section 4 Tables and Graphs Survey estimation mother father 0 0 5 1 6 0 00 0 00 83 33 16 67 100 00 0 00 0 00 0 80 2 04 0 39 Quite a bit of editing is required to make the above table presentable Using Excel to create columns Another method is to copy the table from the Results window from the table into Excel Then use the method to convert text to columns that is provided in Excel The left most cells in each column will contain the text for the entire row Click on Data then Text to Columns The Text to Columns Wizard will start Follow the instructions in the wizard to divide the text into columns Upon completion of the wizard block the columns copy and paste into your word
164. with another colleague that person 50 Stata 13 Sample Session Section I Basic functions Files Descriptives Data Transformations Exercise 1 2 will only have to change the initial cd change directory command at the beginning of the do file to be able to run the do file To be able to rerun the do file successfully an error will be generated when the processor reaches this save command Stata will not save a file if the file already exists on disk To make sure the command will run you must add further instructions to replace the file if it already exists Add replace save qla age dta replace Now each time the data file Q1A AGE DTA is opened the age_gp variable as well as the other two age_gp1 and agecat will be included You might want to analyze this new categorical variable using the tabulate command to determine how many people in each age group are heads of households spouses or children 1 From the menus click on Statistics Summaries tables amp tests Frequency tables Two way tables with measures of association The Tabulate2 two way tables dialog box opens 2 Use age_gp for Row variable and Ca2 relation to head for Column variable 3 Check the proper selections in the Cell contents choices for we want both Row and Column percentages 4 Click on the copy button switch to the do file editor paste the command and switch back Click on OK to run the command Th
165. xpression exp and need only 1 equal sign example gen newvar oldvar 2 5 In the above example prod already has values and we 2 equal signs e g show me only records where prod 47 and unit 8 If you wanted to look at the 27 observations where there command to look at these 27 cases 62 same conversion factor for rice whether it was in a 100 kg product unit combination conver is equal to the number of kilograms in that unit It is always important to verify information is stored in a variable called _merge Let s run a tabulation on this variable to look at how the merge same number of records in the file as there was before the merge i e 1 693 There are 27 cases where there was not a match for the prod unit combination in the look up file the exp assignment phrase For example if you want to want to see only records where prod has the value of 47 Therefore it is a relational equality and we must use two is no conversion factor how would you specify the list Stata 13 Sample Session Section 2 Restructuring Data Files Table Lookup amp Aggregation Compute total kilograms produced The generate command As part of data verification and to make sure you are using a good set of data you will want to investigate further to see if the records without a conversion look up value are crops that you want to have included in the analysis you are doing If they are correct the
166. y 5 spaces and shows the variables in display format Below is an example district vil ca2 angoche monari son daugh cad univ illiterat arizona 1 Select the variables using the drop down arrow district vil hh mem ca1 ca2 ca3 ca4 ca5 ca6 Note if you wished to include all variables leave the box empty 2 Click on the tab labeled by if in In this tab we can limit the number of cases that are displayed 3 Check the box V next to Use a range of obser vations Specify the range to be from 1 to 10 4 Click on the Options tab Under Table options check the box V next to Force a clean table Note value labels will be displayed To see the numeric values place a next to the box Display numeric codes rather than label values 5 Click on the copy button and then click on Ok to run the command 6 Inthe Results window you see a list of the observations If the information for each observation is wrapping to the next line you can resize the Results windows so that it is wider Place your mouse pointer on the right border of the window and when you see a double arrow click the Left Mouse Button hold it and drag the right side out to make the window wider If you see the More at the bottom of the Results window there are several methods you can use to continue Press lt Enter gt Press any key Click on the More button on the tool
167. y with a value of 786429 Remember you can only use the scalar mean immediately after running the summarize command The Stata commands are replace ae r mean if ae tabulate ae missing Now we need to calculate the number of adult equivalents for each household The current file is at the member level but we need the value at the household level Again we use Collapse to go from the member level to the household level The new variable ae will be calculated by summing ae across all members of a household Reminder The Grouping variable s specify the variables to be used for combining cases in the collapsed file Any cases from the original file that have identical values for all of the grouping variables will be combined into a single case in the collapsed file We want the collapsed file to have one case per household so we use the variables that identify a household in our survey district vil and hh 1 From the Data menu select Create or change variables then Other variable transformation commands then Make dataset of means medians etc The collapse Make dataset of means medians etc dialog box will appear 2 On the Main tab in the Statistics box for 1 change mean to sum by clicking on the drop down arrow In the Variables box select ae 3 Click on the Options tab and in the Grouping variables box select district vil hh in that order because those variables represent the identi
168. ypes of Analysis lt mrtab h35a h35b h35c Click on the link for st0082 from 1 In this screen click on click here to install 2 The program will be installed in the folder C ado plus m 3 To read the help screen in the Command window type help mrtab To use the program in the Command window type mrtab h35a h35b h35c poly response 1 9 poly response 1 9 Percent of Percent Frequency responses of cases cotton peanuts sesame sunflower rice maize beans banana manioc sugar cane 1 2 3 4 5 6 8 9 89 2k 42 84 26 40 3 0 Ta 1 0 0 85 26 40 L2 T 2 1 Total Valid cases Missing cases Other Types of Analyses Weights 100 The table shows cotton was the most frequent primary cash crop 89 households grew this crop peanuts and rice were the next most often grown for cash You could have also used the tab1 command tab1 h35 Using the tab1 you have to add the number of observations yourself If you add the number of observations for cotton in h35a to the number of observations for cotton in hh35b what do you get Can you explain why the tab1 totals are different from the mrtab command output Stata provides for a method to analyze data using different types of weights The type of weight that is to be used with a set of data will depend on the type of sampling that has been used See the table below for an explanation of the availa

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