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User Manual - Respondent Driven Sampling

1. 14250028 114250016 14250040 Sample Size 264 Number of Coupons per Recruit 7 jojo ojojolojojo Value for Missing Data 0 14256002 14250013 14250013 14250019 14250005 14250031 14250004 114250034 14250012 14250103 14250026 14250037 FIGURE 2 3 RDSAT Spreadsheet View 18 T RDS INCORPORATED Setting Options For Analysis Before conducting an analysis check the options that will be used Choose Options from the main window The window of figure 2 4 will appeat Number of Resamples for Bootstrap Psoo Confidence Interval tail alpha hos F9 cut Outliers of Network Sizes Minimum Net Size Maximum Net Size Algorithm type Aus Data Smoothing OEnhanced Data Smoothing FIGURE 2 4 RDSAT Options Window Adjust Average Network Sizes In a chain referral sample those with more connections and larger personal network sizes tend to be over represented in the sample This can potentially bias sample estimates The phenomenon can be corrected however and the RDS analysis tool does so by default To learn more about the methods used refer to Sampling and Estimation in Hidden Populations Using Respondent Driven Sampling by Douglas Heckathorn and Mathew Salganik If you do not wish to adjust the average network sizes for this sample bias uncheck the flag Note For NHBS i
2. occurrences of a period to the missing data value integer This can be done by clicking Edit gt Replace in the Excel menu bar In the window that appears type a period in the Find what textbox and the missing data value in the Replace with textbox see Figure 1 8 Then click Replace All 11 RDS INCORPORATED Preparing data from SAS If the data to be analyzed is in a SAS data file then the following steps will transform the data from a SAS data file to a data file that can be read by RDSAT First export the SAS data file using the following code fragment The portions highlighted in bold are specific to the dataset and must be altered data one set name of your main SAS data file gt file Target Directory RDSATdata txt gt put 1 SurveyID RDS_INJ Coupon submitted Coupon given 0 Coupon given 1 Coupon given 2 age sex race Run Note The lt gt brackets indicate that user fills in this information Age sex and race are examples of variables you might want to analyze There are two features of note in the above code First the output file must be a text file suffix txt or a data file suffix dat RDSAT only reads these file types Second the variables that comprise the main data set SurveyID RDS INJ Coupon submitted Coupon given 0 Coupon given 1 Coupon given 2 must be in the order shown above Then add variables you want to analyze such as age sex race RDSAT requires th
3. proportions This helps in determining how many waves of recruitment are necessary before the population is at equilibrium First click on Estimate Number of Waves Required in RDSAT s Analyze menu This will cause the window of Figure 6 2 to appear Then select a starting group for a hypothetical sample Next choose a convergence radius The smaller this number the higher the confidence intervals will be However the dataset will take longer to analyze The default is 02 which should setve as a good starting point A radius of 02 means that the population proportions will change by less than 02 with further recruitment In other words the sample population proportions are considered converged at 43 RDS INCORPORATED equilibrium when the change in population proportions in between waves is less than the convergence radius times of the population proportions Select analyze and this utility will use the Markov process implicit in the calculated transition probabilities to check how many waves are required for the sample population proportions to reach equilibrium The results of the analysis will be output to a new report page See Figure 6 3 NN Group with Initial Recruit Convergence Radius FIGURE 6 2 RDSAT Waves Estimation Window EEE Data File y mem EA Number Of Waves Required 4 NN bee istory of convergence of sample population proportions Wave number 1 Group Number 1 1 0 Group Num
4. refer to the documentation included with this distribution More help and resources are available on the web at http www respondentdrivensampling org FIGURE 3 1 RDSAT Analyze Partition Button 21 RDS INCORPORATED A partition is a user defined set of groups Everyone in the population belongs to a group in a partition The groups are defined by common traits For instance a simple partition would consist of just one trait such as gender Those with a gender of 1 in this case male would form one group those with gender of 2 female another A multi trait partition of race and gender can also be created A group would then be defined by both a gender and race value For example race gender 1 1 white male would be a separate group from race gender 2 1 black male although both groups have the same gender Analyze Partition Attributes Attributes to be analyzed Q9 Analyze FIGURE 3 2 RDSAT Analyze Partition Window The partition panel is divided into three parts see Figure 3 2 The top left contains a list of all traits that may be used for analysis The top right contains a list of all traits that will be used to make the partition The bottom contains options for parsing the trait data To include a trait in the partition select it and press the right arrow To remove it from the partition select it and press the left arrow For each of the traits included
5. Recruits age 51 or older Likewise a Step of 1 would produce 27 different categories one for recruits 25 or under one for a recruit of every age between 25 and 50 and one for recruits age 51 or older 25 RDS INCORPORATED Chapter Interpreting Analysis Results vatious size and proportion estimates are explained along with their T his chapter explains how to interpret the results of an RDSAT analysis The cotresponding graphs and diagrams Interpreting a Partition Analysis First create a simple partition with one variable and the complete option as shown in Figure 4 1 Click Analyze Attributes to be analyzed Mereakpoint e ZD gt FIGURE 4 1 RDSAT Single Variable Partition Analysis 26 RDS INCORPORATED After a moment the results of the analysis will be output to the pages in the main window To move between pages of the analysis click on its corresponding tab Recruitment Displays general statistics regarding the recruitment Estimation Network Sizes and Homophily Graphics and Histograms Data Smoothed Transition Probabilities Demographically Adjusted Recruitment Matrix Key of Group and Trait Correspondence FIGURE 4 2 RDSAT SINGLE VARIABLE PARTITION ANALYSIS RECRUITMENT Note Seeds are not included in the sample population sizes 27 RDS INCORPORATED Key of Group and Trait Correspondence The Key of Group and Trait Correspondence is used to interpret the d
6. homophily within 3 different groups Each group is shown as a sepatate bar This graph illustrates that Group 2 the middle bar has the highest homophily roughly 3 followed by Group 1 the leftmost bar and Group 3 rightmost Population Proportions Population Proportions 1 0 0 8 This graph displays the population proportions of each group The y axis is the population proportion and should be read as a percentage We see that Group 1 the leftmost bar comprises more than half the total population followed by group 2 and 3 33 RDS INCORPORATED Average Adjusted Network Sizes Avg Net Sizes 120 115 110 105 100 This graph displays the adjusted network sizes of each group Observe that group 3 the rightmost bar has the highest network size Transition Probabilities This is a 2 dimensional histogram of the transition probabilities A brighter color cotresponds to a higher value It is basically a way to better visualize the corresponding transition matrix Transition Probabilities 34 RDS INCORPORATED Degree List List of all network sizes reported in the sample The list is sorted from least to greatest fot easy view of the distribution Sorted Degree Sequence Degree 3900 800 700 600 500 400 300 200 100 0 150 Recruit In the graph above we see that there are a few respondents with networks as large as 900 but most respondents fall within a degree of 100 30
7. in the partition how to patse the data values must be selected Data Parsing Options Complete This option will find every distinct value in the data file associated with that trait and create new groups based on that value For example if the trait gender has two values in the data file 1 2 the complete option will make a new group associated with each of these values If the trait race has three values 1 2 3 then the complete 22 RDS INCORPORATED option will create 3 more groups corresponding to those trait values If both gender and race are included in the partition there will be 2 x 3 6 groups in all race gender 1 1 2 1 8 0 1 2 2 2 B 29 Breakpoint This will take every value below the specified breakpoint and create a new group based on it a 2nd group is created based on every value greater than or equal to the specified breakpoint This is different from a breakpoint analysis discussed in the next section in that only one breakpoint is chosen for the dataset rather than a range of breakpoints The analysis is identical to a complete partition analysis with the exception of creating exactly 2 groups from a partition in the dataset rather than one for every possible trait value For example the trait age has a range of values associated with it It would be impractical to create a group for every distinct age but by choosing breakpoint with a value of 40 the population can be
8. or choose the data type that best describes your data Original data type pe that best describes your data i Characters such as commas or tabs separate each field Fields are aligned in columns with spaces between each field Fixed width import at row fi File origin 437 OEM United States Preview of file C Tempirdsatinyjazz txt a 350 O 14250004 14250005 14250006 14256002 0 0 901 1 1 40 350 0 014250007 14250008 14250009 14256003 0 0 902 1 264 0 1 2 585 0 14250010 14250011 14250012 14256004 0 0 903 2 3 41 585 hd J Y Cancel lt Back u gt Finish FIGURE 1 9 Excel text import window To load data exported from the RDSCM v2 0 click File gt Open in Excel s menu bar and select the exported data The window of Figure 1 9 should appear Select Delimited in the file type section and click Next Note For NHBS variables such as network size gender race age etc will be found in the questionnaire data file and cannot be exported from RDSCM v2 0 14 RDS INCORPORATED Text Import Wizard Step 2 of 3 2 xl This screen lets you set the delimiters your data contains You can see how your text is affected in the preview below v Treat consecutive delimiters as one Text qualifier Semicolon Comma Other FIGURE 1 10 Excel text import window In the next wizard screen be sure to check the box entitled Space You should se
9. 0 Bootstrap Simulation Results Shows the histogram of Bootstrap estimates of Least Squares population proportions The horizontal axis depicts population estimates for the specified group The vertical axis shows the frequency of the Bootstrap estimate Frequency of Population Proportions from Bootstrap Procedure Frequency 0 06 0 05 0 04 0 03 0 02 0 01 0 00 0 50 0 55 Population Prop 35 RDS INCORPORATED Degree Distributions Distribution of network sizes for each group and for the population as a whole The diagram below happens to be of the entire population We see that most members of the population have network sizes close to 100 or 200 and the frequency of higher netwotk sizes decreases with the exception of an anomaly at 500 Complete Degree Distribution Frequency 0 20 300 400 500 600 700 Degree 36 RDS INCORPORATED Interpreting a Breakpoint Analysis A breakpoint analysis breaks a dataset into groups based on a single continuous variable A continuous vatiable of interest might be Age where one wouldn t examine each individual age as a separate group but rather a range of Ages As such there is no recruitment data for breakpoint analyses Rather there are interesting trends to notice in Homophily and population proportion as the breakpoint is shifted and respondents ate moved from the upper group of the lower group The Estimation tab shows a table of Least Squares population estimates
10. 03 Cancun Mexico Street and Network Sampling in Evaluation Studies of HIV Risk Reduction Interventions By Salaam Semaan Jennifer Lauby and Jon Liebman AIDS Review 2002 Oo Comparison and Evaluation of Alternate Methods for Sampling Hidden Populations Review of Sampling Hard to Reach and Hidden Populations for HIV Surveillance By Robert Magnani Keith Sabin Tobi Saidel and Douglas Heckathorn In AIDS 2005 51 RDS INCORPORATED Appendix Appendix 1 The RDS Data File Components of Core Data Files Note that all data outside of the first two lines must be integer valued Header on line 1 Every core data set must begin with the string RDS on the first line Parameters on line 2 From left to right the second line must contain the following integer valued information o o o Sample Size Maximum number of coupons received by a recruit in the sample Value for missing data This value will be used throughout the analysis to refer to missing data It will over ride all other values so it is important to choose an integer value that will not occur elsewhere in the data Main data set Subsequent lines contain the main recruitment information with each line corresponding to a recruit Arrange the columns from right to left as followed o o Survey Rectuit ID an integer code acting as the recruit s name Personal Network Size The serial number of the coupon the recruit rec
11. 09 14 3 585 0 14250010 14250011 14250012 14 4 400 0 14250025 14250026 14250027 14 9 150 0 14250022 14250023 14250023 14 6 100 0 14250028 14250029 14250030 14 T ann ni 1425nn1 amp R 14 amp 5n0417 1A9RNNAR 14 FIGURE 1 2 Sample RDS Data in an Excel Spreadsheet Note In this sample data set each recruiter is given 4 coupons to distribute and the coupon numbets are 8 digits and the coupon numbers are 4 digits For NHBS each recruiter is initially given 3 coupons RDS INCORPORATED G H J K Gender mt Race wBo Age Airplay 14256002 1 1 40 1 14256003 1 2 64 1 14256004 2 3 41 1 14256009 2 2 77 0 14256008 1 1 33 1 14256010 1 3 31 2 14256006 1 2 70 1 FIGURE 1 3 Excel Spreadsheet Custom Field Headers and Data Column headers must be entered for all fields other than the main data set Le respondent or survey ID network size coupon received from recruiter coupons given to respondents such as Gender Race Age etc If a data value corresponds to a specific group for example if a value of 1 corresponds to Male and 2 to Female you can indicate this in the data set Abbreviate the group with a single character for example m for Male and f for Female Add the abbreviations in order of increasing value to the gender header surrounded by parentheses In this example the resulting header would be Gender mf Similarly to indicate for the Race header that Whites correspo
12. 7 cormell edu Douglas Heckathorn douglas heckathorn cormell edu Department of Sociology Cornell University FIGURE 1 1 RDSAT Main Window RDS INCORPORATED Preparing Data from Excel RDSAT Accepts data in the form of a text file To load an existing excel spreadsheet into RDSAT the columns of the dataset must be in the following order Respondent ID Note for NHBS this will be the Survey ID Self Reported Network Size Coupon Received from Recruiter Coupons given to Respondent C1 to C4 Other variables then follow e g gender race age etc The first two rows of the spreadsheet make up the RDSAT header The first line must be RDS The second line is the sample size the maximum number of coupons given to each respondent the symbol for missing values In this sample dataset the number of respondents in 264 the maximum number of coupons distributed to each respondent is 4 and O entries ate treated as missing data napa For NHBS the data will not include the network information RDS_INJ because it comes from the questionnaire data file and not the coupon manager data file In this case the network information must be taken from the questionnaire data file and merged into the coupon manager data file before the data is exported F4 Microsoft Excel Book3 62 File Edit View Insert Format Tools Data Window Help Acrobat 264 a 0 1 350 0 14250004 14250005 14250006 14 2 0 0 14250007 14250008 142500
13. 7 voice messages may be left for any team member Note For urgent requests please call the phone number and identify the message as urgent 50 RDS INCORPORATED References Respondent Driven Sampling A New Approach to the Study of Hidden Populations By Douglas D Heckathorn Social Problems 44 174 199 Oo The original article in which RDS was introduced Respondent Driven Sampling II Deriving Valid Population Estimates from Chain Referral Samples of Hidden Populations By Douglas D Heckathorn Social Problems 2002 O Article extending the RDS method to include calculation of standard errors and post stratification to control for differences in network size and clustering across groups Salganik Matthew J and Douglas D Heckathorn In press December 2004 Sampling and Estimation in Hidden Populations Using Respondent Driven Sampling Sociological Methodology O Article showing through both analytic means and simulations that the RDS population estimator is statistically unbiased O Outstanding Article Publication Award of the Mathematical Sociology Section of the American Sociological Association Extensions of Respondent Driven Sampling A New Approach to the Study of Injection Drug Users Aged 18 25 By Douglas D Heckathorn Salaam Semaan Robert S Broadhead and James J Hughes AIDS and Behavior 2002 Oo Empirical evaluation of some of the assumptions underlying RDS and its use to study yo
14. RDS INCORPORATED RDS Analysis Tool v5 3 User Manual RDS INC RDSAT 5 3 User Manual O RDS Incorporated 45 Beckett Way Ithaca NY 14850 Phone 607 257 0787 Table of Contents FDSAT 5 3 B35l68 ie poo eR RI et tee uno obe ania seus 3 Installing the RDS Analysis Tool v5 3 sss 3 Basic Layout Information esses 4 Preparing Data from Excel esses 5 Preparing Data from SPSS utet exe iid ax utut 8 Preparing data from SAS uoces i Insee eaae eR RARE a REIR RN 12 Preparing Data from the RDS Coupon Manager 14 Loading Viewing and Editing Data in RDSAT 16 Loading Dat s an Rt a uod eS 16 Viewing Datas s M aM terea actes ded dose et bed eh ode 17 Setting Options For Analysis 19 Adjust Average Network SiZ S oooooccccccccccccoconcconcnoccnnnananannncnnnnnnnns 19 Number of He samples usos need 19 Confidente Interval oU D UA PUDE 20 CULOS ED SE Es 20 Analyzing a Dataset cere tete tp erica teen tdeetee 21 Parton An Sica so idad 21 Data Parsing ODIOFS i Luo eden e Ioan rias 22 Complete od RP TRENDS 22 A dah seit o ie A aahtents ad E 23 Gum HO 23 Breakpoint Analysis ttd t 24 Interpreting Analysis Results essesseessssss 26 Interpreting a Partition Analysis ssssusssss 26 BAADT EIEE EEA 27 Key of Group and Trait C
15. Re Analyze with Specified Missing Data This feature allows each trait to be chosen and to specify which value the missing data within that trait to have This option can also be used to give missing data a unique value to allow groups to form on the basis of whether they have missing data To re analyze a dataset simply load it into RDSAT and click Re analyze with specified missing data see Figure 5 1 40 RDS INCORPORATED ADS RDS Analysis Tool Analyze Analyze Partition Analyze Breakpoint Estimate Number Of Waves Required Re Analvze with Specified Missing Data E Impute Missing Data and Re Analyze Burm FIGURE 5 1 RDSAT Re Analyze with Specified Missing Data are Impute Missing Data and Re Analyze Sets missing data to their most probable value given the transition probabilities For instance if someone is recruited by Group 1 and the missing data prevents that person from being classified as Group 3 or Group 4 transition probabilities of Group 1 will be used to find the most probable trait value for the recruit and then assigns him or her to Group 3 or Group 4 In cases where missing data is not distributed randomly over trait values this option can help resolve a potential source of sample bias To re analyze a dataset simply load it into RDSAT and click Impute Missing Data and Re analyze see Figure 5 2 m5 RDS Analysis Tool Analyze Analyze Partition Analyze Break
16. alizations Enhanced Data Smoothing An RDSAT option that allows analysis to take place even in a dataset with no recruitment data for a particular group Homophily A measure of preference for connections to one s own group Vaties between 1 completely heterophilous and 1 completely homophilous Impute Missing Data and Re Analyze Sets missing data to their most probable value given the transition probabilities Initial Recruits Reports the number of seeds i e people recruited by the researcher in each group Least Squares Population Proportions Reports the estimated population proportions of each group using linear least squares to solve the population equations LLS Population Weights Multiplicative factors by which the Least Squares Estimates are different from the naive estimates Partition A user defined set of groups Everyone in the population belongs to a group in a partition The groups are defined by common traits Re Analyze with Specified Missing Data This feature allows each trait to be chosen and to specify which value the missing data within that trait to have It can also be used to give missing data a unique value to allow groups to form on the basis of whether they have missing data 48 RDS INCORPORATED Recruitment Matrix Matrix of recruitments to and from each group The vertical axis rows depicts the recruiters and the horizontal axis columns show rectuits Re samples This is the number o
17. at the data be placed in this order and doing so in the output step will save time For NHBS the data will not include the network information RDS_INJ because it comes from the questionnaire data file and not the coupon manager data file In this case the network information must be taken from the questionnaire data file and merged into the coupon manager data file before the data is exported This will be the same for any additional variables you want to analyze Once the data has been exported open the file using NOTEPAD or WORDPAD and add the two line header as described in the Section of this chapter entitled Preparing Data From Excel An example header is displayed highlighted in bold in the data file fragment below 12 RDS INCORPORATED The data file is ready to be read by RDSAT Note that SAS will export the data as a space delimited data file and not a tab delimited data file RDSAT is capable of reading both file types The completed data file will resemble the example below RDS 530 11 0 sex agecat race 3 3310000000000 25 2 0000000000 50 3 17 608 607 609 18 0 5 6 0 0 N N N 10 4 20 21 414 416 41 40 17 25 23 24 000 AJ OGY UI 45 OOON N OOON N POON N NO Oo Nor 13 RDS INCORPORATED Preparing Data from the RDS Coupon Manager RDSCM v2 0 Text Import Wizard Step 1 of 3 2 xl The Text Wizard has determined that your data is Fixed Width If this is correct choose Next
18. ata related to recruitment in the analysis It lists all of the various groups that were analyzed and relates them to the trait they have in common In this example Group 1 corresponds to Race 1 Looking at the Race variable we see that the races are listed in parentheses by their initials WBO W White B Black O Other So Group 1 corresponds to the first race in the list namely White Group 2 corresponds to Black in the same manner and Group 3 corresponds to Other Recruitments Matrix of recruitments to and from each group The vertical axis rows depicts the recruiters and the horizontal axis columns show recruits For example this matrix tells us that Group 1 recruited 94 other people in Group 1 from the same group Transition probabilities Normalizes recruitments by dividing by the total number of recruitments and gives the probability of one group recruiting another For example Group 1 recruited 94 from the same group and so the normalized transition probability is 94 94 32 18 652 where the denominator is the total number of recruits Group 1 made Demographically adjusted Recruitment Matrix Gives hypothetical recruitments if each group recruited with equal effectiveness Transition probabilities implied by this matrix are identical to those of the original Recruitment Matrix It is well known that some groups of respondents recruit more than others e g HIV positives often recru
19. ates of population proportions The term naive is used because the proportion is a simple ratio of how many of a particular group were recruited to the total number of recruits It is not adjusted for any statistical biases To learn more about the methods used refer to Sampling and Estimation in Hidden Populations Using Respondent Driven Sampling by Douglas Heckathorn and Mathew Salganik Equilibrium Sample Distribution The equilibrium sample population proportions indicate each group s population size after the proportions have converged to their equilibrium value This occurs when further recruitment waves do not change the population proportion by a significant amount Population Weights The population weights can either be calculated using the linear least squares algorithm or the data smoothing algorithm depending on how the options are set for the RDS analysis In the above diagram the data smoothing algorithm was used See the Algorithms section of Chapter 2 for more information on the difference between various estimation algorithms in RDSAT 1 LLS Population Weights Multiplicative factors by which the Least Squares Estimates are different from the naive estimates 2 Data Smoothed Population Weights Multiplicative factors by which the Data Smoothed Estimates are different from the naive estimates Confidence Intervals Are obtained by bootstrapping the original sample The confidence intervals only correspond to t
20. ber 2 0 0 FIGURE 6 3 RDSAT Waves Estimation 44 RDS INCORPORATED Figure 6 3 is ascreenshot of the waves estimation output The actual output is listed below for a partition analysis of the New York Jazz dataset See Chapter 2 for more information on this dataset Number Of Waves Required 4 History of convergence of sample population proportions Wave number 1 Group Number 1 1 0 Group Number 2 0 0 Wave number 2 Group Number 1 0 836 Group Number 2 0 164 Wave number 3 Group Number 1 0 79 Group Number 2 0 21 Wave number 4 Group Number 1 0 778 Group Number 2 0 222 What this information means is that it took a total of 4 recruitment waves before the population estimates changed by less than 02 times the population proportion Assuming a convergence radius of 02 As we can see the change in proportion estimates of Group 1 from wave 3 to 4 is 79 778 012 which is less than 02 79 0158 The same is true of Group 2 Save RDS Analysis in the File menu Allows the report pages from the analysis to be saved to a formatted html file The analysis can then be viewed at any time with any web browser and it can be cut and pasted onto most spreadsheets In the current version of RDSAT only saving to HTML is possible however copying and pasting should allow the data to be imported into many applications including plain text editors Export DL Network File in the File menu Allows a DL netwotk f
21. corresponding to each breakpoint value Similatly the Network Sizes and Homophily tables are arranged by breakpoint value see Figure 4 5 File Analyze Help Rds Data File z 78 open New RDS Analyze Partition CProgram Files r Data Included Add Data Analyze Breakpoint po Uf Edit Data NH Change Options Race vyBO s A mmm Recruitment Estimation Network Sizes and Homophily Graphics and Histograms Population Proportions Linear Least Squares and Data Smoothed 37 RDS INCORPORATED FIGURE 4 5 RDSAT Breakpoint Analysis Estimation Tab Viewing the data in the graphics tab will often make patterns very clear For example in the breakpoint analysis of Chapter 3 New York Jazz musicians were analyzed based on their age Try clicking on Homophily in the graphics tab of the RDSAT main window Homophily Homophily B Lower Group 1 0 2 Upper Group 15 Breakpoint There are several visible patterns Homophily tends to zero as the age variable increases This implies that differences in age become less important for choosing relationships the older the recruits are It is also notable that the older group is always more homophilous than the younger group Finally it is possible to see that homophily is strongest where age is the lowest 25 This implies that young jazz musicians show strong preference for relationships with other young jazz musicians 38 RDS INCORPORATED Po
22. divided into a group less than 40 years old and a group 40 years old and greater Custom This allows partitions to be created based on non overlapping ranges of values For instance selecting a trait such as age and using a custom partition with parameters 10 20 21 30 31 40 41 50 would create 5 groups based on 5 intervals of ages Each range must be enclosed in curly braces and delimited with commas Ranges should not overlap Upper and lower bounds may be the same however e g 30 30 if a group must be based on only one value Note It is very easy to create a partition with a large number of groups e g mote than 10 by selecting complete with a trait with many values e g age In general the amount of data is insufficient to handle partitions with such a large number of groups and the analysis will fail 23 RDS INCORPORATED Breakpoint Analysis Breakpoint analysis allows one trait to be analyzed over a range of possible breakpoints This is very useful for continuous vatiables such as age ADS RDS Analysis Tool EJES File Analyze Help Rds Data File z T E Open New RDS C Program Filesrdsatnyjazz txt i E Data Included Add Data Gender MF Race WBO Edit Data Recruitment Estimation Network Sizes and Homophily Graphics and Histograms Respondent Driven Sampling Analysis Tool v 5 0 1 If you are new to Respondent Driven Sampling refer to the docume
23. e the data line itself up properly at this point see Figure 1 10 Finally click Finish Ea Microsoft Excel FromRDSCM txt E File Edit View Insert Format Tools Data Window Help amp x Oe 4Y amp amp 2 l 7 113 E fe 1 154 2 1 3 1 1 1 1234 1111 4 2 1 1 1002 1 5 3 1 1002 1 1 B 4 1 2128 4563 453 5 1 2546 4563 4565 B B 1 5452 7456 2314 9 7 1 4566 4564 4564 FIGURE 1 11 Imported RDSCM Data Change the network sizes to their appropriate values by double clicking the appropriate cells and save the data as described in the section entitled Preparing Data from Excel Figure 1 11 shows fictitious NHBS data exported from RDSCM v2 0 15 RDS INCORPORATED Chapter Loading Viewing and Editing Data in RDSAT his chapter covers how to load data into RDSAT Topics covered include loading RDSAT format files setting options for analysis and viewing editing the data Loading Data RDS Analysis Tool File Analyze Help Rds Data File f y open New RDS Analyze Partition f C Program Filesrdsatnyjazz txt Data Included Analyze Breakpoint Gender MF Change Options Race WBO ge Ort Recruitment Estimation Network Sizes and Homaphily Graphics and Histograms Respondent Driven Sampling Analysis Tool v 5 0 1 If you are new to Respondent Driven Sampling refer to the documentation included with this distributi
24. eived NOTE if the recruit is a seed then this number must be set to the missing data value Serial numbers of the coupons given to the recruit This data will take up the number of columns specified by the max number of coupons given to a recruit parameter specified on line two If the recruit was given a number of coupons less than that set some of the values to the missing data value For example below are the first 7 lines of the core data set for Doug Heckathorn s New York jazz musicians RDS 264 7 0 1 3500 14250004 14250005 14250006 14256002 901 0 0 200 14250007 14250008 14250009 14256003 902 0 0 3 585 0 14250010 14250011 14250012 14256004 903 0 0 4 4000 14250025 14250026 14250027 14256009 904 0 0 5 150 0 14250022 14250023 14250023 14256008 905 0 0 52 RDS INCORPORATED Appendix Appendix 2 RDSAT Questions amp Answers Are seeds included in the RDSAT analyses calculations Yes because recruitments by seeds are treated like any other recruitments and all recruitments in combination ate used to calculate the transition probabilities In contrast the self reported network sizes of seeds are not used to calculate network size estimates because seeds were not recruited by a peer they were recruited by key informants or in some other manner If a participant reports that the person who gave them a coupon is a stranger are they included in the RDSAT analysis If so what are the implications for the recruitment chain
25. f the data you wish to analyze is in an SPSS spreadsheet see Figure 1 5 you may convert it to the RDS format by copying and pasting the data into an excel spreadsheet First organize the columns so that the main data set appears in the standard RDSAT format namely Respondent ID Survey ID for NHBS IDU Self Reported Netwotk Size Coupon Received from Rectuiter Coupons given to Respondent C1 to C3 in Figure 1 5 and finally other variables you want to analyze like gender race age etc RDS INCORPORATED Note In this sample data set the variable label for Respondent or Survey ID is rid for the network size is net for the coupon received from the recruiter is coupon for the coupons given to respondents is C1 C4 For NHBS these variable labels may look differently when exported from RDSCM v2 0 or from the questionnaire data file Variable Variable label Data Source Survey ID SurveyID RDSCM v2 0 Network Size RDS INJ questionnaire data file Coupon received from recruiter Coupon submitted RDSCM v2 0 Coupons given to respondent Coupon given 0 RDSCM v2 0 cc ee 55 Coupon given 1 Coupon given 2 For NHBS the data will not include the network information RDS_INJ because it comes from the questionnaire data file and not the coupon manager data file In this case the network information must be taken from the questionnaire data file and merged into the coupon ma
26. f times random subsets of the data are sampled to derive the bootstrap confidence intervals More re sampling will result in better confidence intetvals but will be more CPU intensive Respondent A participant in an RDS sampling study Respondent ID A unique integer representing a respondent in a given RDS dataset Sample Population Proportions The naive estimates of population proportions without correction of over sampling and othet biases Sample Population Sizes The total number of recruits in each group Self Reported Network Size The number of individuals a respondent reports he or she has in his her netwotk Transition Probabilities Normalizes recruitments by dividing by the total number of recruitments and gives the probability of one group recruiting another Unadjusted Network Sizes A straight forward arithmetic mean of the sample s network sizes Waves Estimation This feature allows hypothetical recruitment scenarios to be examined The sample population proportions ate considered converged when the change in population proportions in between waves is less than the convergence radius times of the population proportions 49 RDS INCORPORATED Help and Support In addition to this manual you may also contact the RDS Coordinating Center for technical assistance with any RDS Inc product RDS staff will respond to all requests for assistance within 24 hours Email RDS CDC gmail com Phone 607 257 078
27. g frame depends on the aims of the study Can RDSCM allow the user to override an individually expired coupon Are there anticipated implications for RDSAT as it relates to the expiration dates of coupons an or referral cards RDSCM will allow a coupon s void status to be overridden in the following way 1 Increase the validation timeframe so that coupons won t be automatically voided 2 Change the voided coupons status to UNPAID the auto void will not trigger anymore due to step one 3 Enter the records for the individuals that arrive with the coupons 4 Finally return the validation timeframe to normal The coupons in question should be in a PENDING state so they will no longer be auto voided How does RDSAT account for missing data For example one of out sites lost 2 interviews handheld computer malfunction one from a seed and the other from a non seed respondent Currently RDSAT will not process the entire recruitment chain linked to a record with missing data How does RDSAT adjust for differential coupon distribution For an in depth look at the methods used in RDS analysis please consult Sampling and Estimation in Hidden Populations Using Respondent Driven Sampling The citation for this paper can be found in the references section of this manual Please also consult the References section for more RDS related literature 54
28. he Least Squares population estimates and can be set in the options panel click options in the main window 31 RDS INCORPORATED Network Sizes and Homophily This tab displays Homophily Affiliation and Average Network Sizes f Recruitment Estimation Network Sizes and Homophily Graphics and Histograms Adjusted Unadjusted Average Net Average Net Key of Group and Trait Correspondence FIGURE 4 4 RDSAT Single Variable Partition Analysis Network Sizes Tab Adjusted Average Network Sizes Netwotk sizes are adjusted for sampling bias In a chain referral sample those with more connections and larger personal network sizes tend to be over represented in the sample This can potentially bias sample estimates To learn more about the methods used refer to Sampling and Estimation in Hidden Populations Using Respondent Driven Sampling by Douglas Heckathorn and Mathew Salganik Unadjusted Network Sizes Straight forward arithmetic mean of the sample s network sizes Homophily A measure of preference for connections to one s own group Vaties between 1 completely heterophilous and 1 completely homophilous Affiliation Matrix Displays the same preference measures as homophily but for all group paits 32 RDS INCORPORATED Graphics and Histograms This tab displays visual illustrations of data presented in the previous sections of this chapter Homophily Homophily 1 0 This graph displays
29. ile to be exported to the recruitment chain data DL format is recognized by numerous network analysis packages including UCI net and Pajek Pajek in particular can be used to create attractive social network visualizations as seen in Figure 6 4 45 RDS INCORPORATED FIGURE 6 4 Pajek Generated Social Network Visualization UCINET http www analytictech com ucinet 5 description htm PAJEK http vlado fmf uni j si pub networks pajek 46 RDS INCORPORATED RDS Glossary of Terms Adjust Average Network Size Option In a chain referral sample those with more connections and larger personal network sizes tend to be over represented in the sample This RDSAT option corrects this bias Adjusted Average Network Sizes Netwotk sizes that are adjusted for sampling bias Affiliation Matrix Displays preference measures for connections between all group pairs The diagonal of this matrix is Homophily within a group Bootstrap Simulation Results Shows the histogram of Bootstrap estimates of Least Squares population proportions The horizontal axis depicts population estimates for the specified group The vertical axis shows the frequency of the Bootstrap estimate Breakpoint Analysis A Breakpoint analysis allows one trait to be analyzed over a range of possible breakpoints This 1s very useful for continuous variables such as age Complete Variable Analysis This option will find every distinct value in the data file as
30. it substantially more than do negatives This is shown in the recruitment matrix if the number of recruitments by HIV positives 1 e the row sum in the matrix exceeds the number of recruitments of HIV positives 1 e the column sum in the matrix The demographically adjusted recruitment matrix shows what the recruitment matrix would have looked like if all groups had recruited equally 1 e so row and column sums are equal without any change in recruitment patterns 1 e no change in transition probabilities This type of adjusted matrix is useful for testing one of the assumptions of the statistical theory on which RDS is based which holds that if recruitment effectiveness is uniform across groups cross group recruitments will tend to be equal Therefore the cross group fecruitments in the adjusted matrix will differ only by amounts consistent with stochastic variation Thus if positives recruit more than negatives then in the original recruitment matrix all else equal the number of negatives recruited by positives will tend to be greater than the number of positives recruited by negatives However in the demographically adjusted matrix these will be if not equal at least strongly correlated 28 RDS INCORPORATED Sample population sizes Reports the total number of recruits in each group Initial Recruits Reports the number of seeds from each group i e people recruited by the reseatcher in each group Note Much of
31. ment Estimation Network Sizes and Homaphily ns ae eters and Histograms Respondent Driven Sampling Analysis Tool v 5 0 1 ESOpen New RDS Analyze Partition If you are new to Respondent Driven Sampling refer to the documentation included with this distribution More help and resources are available on the web at http www respondentdrivensampling org FIGURE 2 2 RDSAT Edit Data Button 17 RDS INCORPORATED View the data loaded by clicking on the Edit Data Button or select View Edit RDS from the file menu A new window will pop up displaying the contents of the data files you have loaded see Figure 2 3 Sample size 264 the value for missing data 0 and the number of coupons per respondent 7 are displayed on the left The table columns may be rearranged by clicking and dragging them Click on Save RDS Data to save the data loaded into one file with an rds extension The next time this file is loaded all data including the core and trait data will load automatically Trait data is any vatiable that is not core data Core data consists of the respondent id network size and coupons Trait data can be Race Age etc Notice that when a cell in the table is clicked on its contents may be changed The changes will be saved to any data file created with the Save RDS Data button Note Be careful not to delete data unintentionally 14250004 14250007 14250010 14250025 114250022
32. nager data file before the data is exported RDS INCORPORATED NYJazz SPSS Data Editor fx File Edit View Data Transform Analyze Graphs Utilities Window Help ag e B 2 2 53 m a Er Extat s v al 1 airplay fi 0 14 e M ves gt 36 al 23 al Ds 42 00 54 x Data view Variable View Jr SPSS Processor is ready FIGURE 1 6 RDS Data highlighted in SPSS Highlight all relevant columns in the dataset To do this first click on the left most column header this should highlight the entire first column Next hold down the Shift key and press the right arrow key until all the desired fields have been highlighted see Figure 1 6 Finally either press Ctrl C on the keyboard or click Edit gt Copy on the menu screen to copy the data to the clipboard Paste this data into the third line of a blank excel spreadsheet see Figure 1 7 and add the relevant header information described in the previous section entitled Preparing Data from Excel 10 RDS INCORPORATED CE Microsoft Excel Book3 i File Edit View Insert Format Tools Data Window He Search ey Rows Match case T Find entire cells only FIGURE 1 8 Excel replace dialog window Note Tf there are missing data entries in the SPSS dataset they will be denoted by a period However RDSAT only accepts integers in the dataset Before saving to the Tab Delimited Text Format you must replace all
33. nd to group 1 Blacks to group 2 and all other races to group 3 you may use Race WBO RDS INCORPORATED RAData90 E nygender E LMSM_SF3 RADatas0 J NvEth7 2 LMsM sF2 RADataS E nyeth4 J LMsM sF1 RAData30 E NYEth E LMSM_CH9 J nydata J LMsM CH8 Nybreak E LMSM_CHS YAirU J LMSM CH4 E nyair LMsM CH2 nyunion nyage junk nyrace LMSM_SF9 CUYear2 4 nynet E LMSM sF8 E CUYear nyjazz4 S LMSM_SF7 J cmtest2 NYJazz E LMSM_SF6 E cmtest p nygroup fz LMSM SF5 E abu2 LO NEO Fle names fwaz O A Bse w iiu Save as type Text Tab delimited z Sarel FIGURE 1 4 Excel Save As Dialog N N To save this data set to a file choose Save As and choose the Text Tab Delimited format RDS INCORPORATED Preparing Data from SPSS fy NYJazz SPSS Data Editor File Edit View Data Transform Analyze Graphs Utilities cg B aja El 6 e HE Ela v el Vid o m loa met coupon ci c2 e 14250004 14250005 14250006 14250007 14250008 14250009 14250010 14250011 14250012 14250025 14250026 14250027 14250022 14250023 14250023 14250028 14250029 14250030 14250016 14250017 14250018 14250040 14250041 14250042 14256002 14250013 14250014 14250015 14250013 14250019 14250020 14250021 14250005 14250031 14250032 14250033 14250004 14250034 14250035 14250036 FIGURE 1 5 Sample RDS Data in SPSS I
34. ntation included with this distribution More help and resources are available on the web at http www respondentdrivensampling org FIGURE 3 3 RDSAT Analyze Breakpoint Button To analyze a breakpoint click on Analyze Breakpoint in the main window see Figure 3 3 A Breakpoint analysis can be done on any trait but it is more effective to use traits with many values such as age in the data set of New York jazz musicians The bound fields allow the range of values to be chosen over which the breakpoint will be set For example from the NYC Jazz dataset located in the RDSCM distribution folder see Chapter 2 for details age is selected from the drop down list The step size is set to 1 and 25 and 50 are entered for the lower and upper bound see Figure 3 4 This will perform a breakpoint analysis for groups above and below 25 then above and below 26 and so on 24 RDS INCORPORATED Breakpoint Analysis alo Trait to amp nalyze Age Lower Bound Upper Bound Analyze INN Step FIGURE 3 4 RDSAT Breakpoint Analysis Window In the above window we are selecting Age as the variable to be analyzed and choosing where the breakpoints will lie A Step of 5 with lower and upper bounds of 25 and 50 will break the dataset into the following 7 categoties e Recruits age 25 or under e Recruits 26 30 e Recruits 31 35 e Recruits 36 40 e Recruits 41 45 e Recruits 46 50 e
35. on More help and resources are available on the web at http www respondentdrivensampling org FIGURE 2 1 RDSAT Open New RDS Button First open the core data set The core data set contains information about the sample size missing data values and number of coupons per respondent Start the RDS Analysis Tool and choose Open New RDS or select the file menu and click on 16 RDS INCORPORATED New RDS see Figure 2 1 When a file chooser dialog window appears select the RDS data file and choose Open The nyjazz txt file included in this distribution is a good sample file to work with if no real dataset is available If the default installation directory was used this sample file will be located at C Program Files rdsat nyjazz txt For more information on the core data set refer to Appendix 1 Data pertaining to other population features of interest can also be included in this file Analysis cannot be carried out until this data is loaded Note The sample RDS data set of New York jazz musicians was collected by Douglas Heckathorn and analyzed in Finding the Beat Using Respondent Driven Sampling to Study Jazz Musicians Douglas D Heckathorn and Joan Jeffri Poetics 2000 Viewing Data MEROS Anatysis roo OR File Analyze Help Rds Data File C Program Filesrdsatinyjazz txt Data Included Analyze Breakpoint Gender MF Y EdiDia O Data Change Options Race WBO Recruit
36. orrespondence 28 Recruitment cnt 28 Transition probabilities sisas 28 Demographically adjusted Recruitment Matrix 28 Sample population sizes 3 2 a aaa 29 Initial Recruits 2 2 0 ccc ceececececceeececceecceececeueccecceecaecceeceeaeeceeeaeeseeeaees 29 ESUMAHOTN us dns ee Du D E hte eds 30 Estimated Population Proportions usus 30 1 Least Squares Population Proportions 30 2 Data Smoothed Population Proportions 30 Sample Population Proportions eeseseeeeeeess 31 Equilibrium Sample Distribution eeeseeseeeeeessssssss 31 Population We lOnisi sai adi 31 1 LLS Population WOIglils iie D DAVE 31 2 Data Smoothed Population Weights ssss 31 Confidence Intervals sas oet aaa 31 Network Sizes and Homophily ssssssss 32 Adjusted Average Network SiZ8S oocooccnccoccccccnccccccnnnnnnanncnnnnncnns 32 Unadjusted Network Sizes oocoononcccccccnccccccncnonancnnnccnnnnnnnnnnnannnnnos 32 Homopbiily na id 32 Affiliation A A O 32 Graphics and FStOGraM Sicilia 33 Transition ProbabilitieS oocoooccocnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnos 34 Degree acoso 35 Bootstrap Simulation Results oooooocccccccccnnncccccnccanoccccnnnnncnnnnnnnnnnnnos 35 Degree Distrib
37. point C Estimate Number Of Waves Required Re Analyze with Specified Missing Data Impute Missing Data and Re Analyze L IRace BO gt FIGURE 5 2 RDSAT Impute Missing Data and Re Analyze Help 41 RDS INCORPORATED Note These options only allow trait data for traits which have already been used to analyze a partition set imputed Like version 4 of RDSAT a partition analysis must always be completed before using the data set or impute features Also once enabled these features cannot be turned off within RDSAT To analyze a dataset without specified values or imputation of missing values close and re open RDSAT or reload the dataset via the Open New RDS Button 42 RDS INCORPORATED Chapter Extra RDSAT Features T he RDS Analysis Tool has several extra features that will be discussed in this chapter Estimate Number of Waves Required ADS RDS Analysis Tool Duc Help Rd Analvze Partition A Analyze Breakpoint C Estimate Number Of Waves Required Da Re Analyze with Specified Missing Data c Impute Missing Data and Re Analyze we FIGURE 6 1 RDSAT Estimate Number of Waves Required Menu Item abe This feature allows hypothetical recruitment scenarios to be examined A group is selected to be the initial recruiters and they are allowed to recruit based on their transition probabilities until the population proportions converge to the actual sample
38. pulation Proportions Population Proportions A Lower Group Upper Group Breakpoirt Next click on LLS Population Proportions on the Graphics page to find the breakpoint where the population of the upper group equals that of the lower group From this it can be inferred that half of the musicians are less than 43 years old Note that although the graph s x axis ranges from 0 to 25 we ate conducting a breakpoint analysis on groups age 25 to 50 Therefore the above intersection corresponds to an age of 43 18 25 not 18 39 RDS INCORPORATED Chapter Handling Missing Data in the Dataset ost data sets contain missing data RDSAT offers two ways of setting missing data and re analyzing them Both of these options will be covered in this chapter RDSAT employs two data imputation features The first makes it possible to reassign another value to missing data In this way respondents for whom data is missing can be included in the analysis to see if missing data is random or associated with other variables For example in an analysis of HIV prevalence respondents would be divided into three categories positive negative or missing One could then run analyses to see if having missing data was correlated with other terms such as race ethnicity The other data imputation procedure uses a regression like logic to assign values to respondents with missing data based on the estimate regarding what the missing value might be
39. s that follow In RDS studies recruitment rights are both scarce and valuable so respondents tend not to waste them on strangers so recruitment by strangers tends to be rare generally 1 to 3 A reasonable research strategy is to check to see if the respondents recruited by strangers differ significantly from other respondents and if not then to treat these as valid recruitments A maximally conservative research strategy would be to delete from the data set the serial number linking the recruit to the stranger rectuiter The recruit would then be treated as a seed and the stranger recruiter would become the terminus of a recruitment chain Neither respondent would be deleted from the data set but the number of peer recruitments would be reduced Are there any other essential variables we should be analyzing in RDSAT Other than gender race and age The variables to be analyzed depend on the research questions being addressed RDS is a sampling method a method for drawing statistically valid samples so its role is to help ensure that the answers are statistically valid 53 RDS INCORPORATED How does restricting recruitment to specific races affect the legitimacy of the survey and or RDSAT analysis This restriction of the sampling frame narrows the scope of the study e g limiting recruitment to Latino IDU would mean that the study would yield no information about non Latino IDU or Latina IDU How to best choose the samplin
40. sociated with a variable trait and create new groups based on that value Confidence Interval The value of this parameter determines the level of confidence for the confidence intervals reported in the analysis The default 05 measures the normalized length of a tail of the distribution of population proportions In short it determines 90 confidence for the intervals reported in the analysis Cut Outliers An RDSAT option that eliminates extremely small and large outliers in netwotk sizes from the dataset Data Smoothed Population Proportions Reports estimated population proportions for the Data Smoothed population equations Data Smoothed Population Weights Multiplicative factors by which the Data Smoothed Estimates are different from the naive estimates 47 RDS INCORPORATED Degree Distributions Distribution of network sizes for each group and for the population as a whole Degree List List of all network sizes reported in the sample The list is sorted from least to greatest fot easy view of the distribution Demographically adjusted Recruitment Matrix Gives hypothetical recruitments if each group recruited with equal effectiveness Transition probabilities implied by this matrix are identical to those of the original Recruitment Matrix DL Network File DL format is recognized by numerous network analysis packages including UCI net and Pajek Pajek in particular can be used to create attractive social network visu
41. t is recommended to keep box checked Number of Re samples This is the number of times the data is re sampled to derive the bootstrap confidence intervals For accurate confidence intervals keep this option at least the default value of 2500 For optimal accuracy a number over 15 000 is recommended Be aware 19 RDS INCORPORATED howevet that the bootstrap is demanding of CPU time There may be a short wait if this value is set to a high number Note For most NHBS analysis 2500 is recommended Confidence Interval The value of this parameter determines the level of confidence for the confidence intervals reported in the analysis The default 05 measures the normalized length of a tail of the distribution of population proportions In short it determines 90 confidence for the intervals reported in the analysis Cut Outliers With this option you may eliminate extremely small and large outliers in network sizes Check the box and input the desired minimum and maximum network sizes to be used in the analysis If this option is selected when the program encounters an individual whose netwotk size is outside of the specified bounds their network size will be set to the value of the nearest lower or upper bound To view the changes use the View Edit utility The changes enacted by the Cut Outliers option may then be saved to a data file Note Check for outliers by running a univariate frequency in SAS SPSS Excel before impor
42. the data reported above also have corresponding data smoothed estimates Data Smoothing is a method for eliminating deviations in cross group recruitments that occur due to chance For more information about data smoothing refer to Douglas D Heckathorn 2002 Respondent Driven Sampling II Deriving Valid Population Estimates from Chain Referral Samples of Hidden Populations Social Problems v 49 No 1 pages 11 34 29 RDS INCORPORATED Estimation Displays estimates of population proportions Key of Group and Trait Correspondence FIGURE 4 3 RDSAT Single Variable Partition Analysis Estimation Tab Estimated Population Proportions The estimated population proportion can either be calculated using the linear least squares algorithm or the data smoothing algorithm depending on how the options are set for the RDS analysis In the above diagram the data smoothing algorithm was used See the Algorithms section of Chapter 2 for more information on the difference between various estimation algorithms in RDSAT 1 Least Squares Population Proportions Reports the estimated population proportions of each group using linear least squares to solve the population equations 2 Data Smoothed Population Proportions Reports estimated population proportions for the Data Smoothed population equations 30 RDS INCORPORATED Sample Population Proportions Report the sample population proportions also called the naive estim
43. ting data to RDSAT Algorithm Type Three different algorithms are available for analyzing an RDSAT dataset Linear Least Squares LLS Data Smoothing and Enhanced Data Smoothing The recommended algorithm is Data Smoothing which adjusts recruitments across groups providing tighter Confidence Intervals than the naive LLS method Enhanced Data Smoothing assigns tiny non zero number to all cells in recruitment matrix then uses Data Smoothing This allows for an analysis to include non recruiting groups which would normally fail using LLS or Data Smoothing 20 RDS INCORPORATED Chapter Analyzing a Dataset his chapter introduces the analysis features of RDSAT This is the heart of the softwate s functionality Topics include Partition Analysis Breakpoint Analysis and Custom Analysis Partition Analysis When an RDS dataset is successfully loaded click on Analyze Partition in the upper right of the main window see Figure 3 1 By clicking on this button the window of Figure 3 2 will appear RDS Analysis Tool File Analyze Help Rds Data File i openNew RDS Analyze Partition C Program Filesrdsatinyjazz txt Data Included Add Data e D eakpoi Gender MF Race WBO Edit Data Change Options Recruitment Estimation Network Sizes and Homophily Graphics and Histograms Respondent Driven Sampling Analysis Tool v 5 0 1 If you are new to Respondent Driven Sampling
44. unger drug injectors Group Solidarity as the Product of Collective Action Creation of Solidarity in a Population of Injection Drug Users By Douglas D Heckathorn and Judith E Rosenstein Advances in Group Processes 2002 Development of a Theory of Collective Action From the Emergence of Norms to AIDS Prevention and the Analysis of Social Structure By Douglas D Heckathorn In New Directions in Sociological Theory Growth of Contemporary Theories Joseph Berger and Morris Zelditch editors Rowman and Littlefield 2002 O History of RDS and the research project from which it emerged Heckathorn Douglas D and Joan Jeffri 2003 Social Networks of Jazz Musicians pp 48 61 in Changing the Beat A Study of the Worklife of Jazz Musicians Volume III Respondent Driven Sampling Survey Results by the Research Center for Arts and Culture National Endowment for the Arts Research Division Report 43 Washington DC 2003 o Use of RDS fo study a non stigmatized hidden population jazz musicians Finding the Beat Using Respondent Driven Sampling to Study Jazz Musicians By Douglas D Heckathorn and Joan Jeffri Poetics 2001 O Useof RDS to study a non stigmatized hidden population jazz musicians Making Unbiased Estimates from Hidden Populations Using Respondent Driven Sampling By Matthew J Salganik and Douglas D Heckathorn Paper presented at the International Social Network Conference February 20
45. utiONS ocooononnnnnccccnnnnnnnncnnnnnncccnnccnnnnnnnnnnnnncnnnos 36 Interpreting a Breakpoint Analysis sss 37 Handling Missing Data in the Dataset 40 Re Analyze with Specified Missing Data 40 Impute Missing Data and Re Analyze 41 Extra RDSAT Features ess otc etie bna a tuor Sato caera 43 Estimate Number of Waves Required ssusse 43 Save RDS Analysis in the File menu 45 Export DL Network File in the File menu 45 RDS Glossary of Terms consisti nie 47 Ese O 50 RETErENCOS reno les 51 Appendix 1 The RDS Data File sssss 52 Appendix 2 RDSAT Questions 8 Answers 53 RDS INCORPORATED Chapter RDSAT 5 3 Basics his chapter will introduce the basics of the RDS Analysis Tool version 5 3 Topics covered include installing the Analysis Tool and preparing data from SPSS Excel SAS and the RDS Coupon Manager Installing the RDS Analysis Tool v5 3 The RDS Analysis Tool RDSAT is installed using a standard windows installer application First download the installer to a temporary folder from the following web address URL http www respondentdrivensampling org Click on Downloads and select the download that matches
46. your particular operating system and java configuration If you are unsute about your java configuration and are running windows choose Option 2 which includes the Java Virtual Machine JVM Once the file is downloaded double click the newly downloaded application RDSAT windows 5 3 exe The installer program will guide you through the installation process Default installation options are recommended and assumed throughout this manual To open the program double click the RDSAT icon or select it from the Programs listing in the Start Menu RDS INCORPORATED Basic Layout Information All RDSAT features are located in the right hand side of the main screen as buttons or in the menu bar See Figure 1 1 The current dataset being analyzed is displayed in the selection menu entitled Rds Data File When a dataset has been analyzed all graphs and figures can be found in the set of tabbed windows at the bottom of the main scteen RDS Analysis Tool O x File Analyze Help Rds Data File v open New RDS Data Included Change Options precrulmenta Estimation Network Sizes and Homophily Graphics and Histograms Respondent Driven Sampling Analysis Tool v 5 0 1 If you are new to Respondent Driven Sampling refer to the documentation included with this distribution More help and resources are available on the web at htto Avww respondentdrivensampling org Erik Volz amp mv

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