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Microdata User Guide HOUSEHOLD INTERNET USE SURVEY

1. KKK KK KK KK KK KK KK KK KK KK KR KR KK KK KK KK KK KR KR KR KR KK KK KK KK KK RK KK KK KK KKK k k k k k k k k k k k k k k k k k k k Ckckckckckokckokckokckokckckckckckckckokckokckokckokckokckokckokckckckckckokckckckckckokckckckckckckckokckckckckckckckckckckckckckckckckckck k k k k 40 0 2357 16 8 13 47 11 8 10 6 9 37 9 0 8 4 7 59 735 EXE 6 8 6 6 6 3 6 1 5x9 BET 56 5 4 SAS 522 KKK KK KK KK KK RK KK KK RK KK KR KK KK KK KR KK KR KK KK KK KK KR k KKK KK KK KK KK KK KK KK KK KR KK KR KK KK KK KR KR KK KR KK RR KR RK RK KK KR KK RK EK KK KK KK KK KK k k k k k k k k k k k k k k k k k k Ckckckckckckckokckokckckckckckckckokckokckokckokckckckckckokckokckokckokckokckokckokckokckokckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckok HK HK e e k KH HH KK HH KH KK k k KK k k k k k k k k k k k k k k k k k k k k k k k 50 0 4 E E H KK RK KK RK KK KK KK KK HK e KK KK KK KK k HK HK HK HK e e e k HK k k k e k KK e k KK KR KR KK KK KH k k k k KK KR k k k k k k k k k k k k k k k k k k k k k 2 2 OA gt gt g
2. 3 k k k k k k k k k k k k Ckckckckckckckckckckckokckckckckckckckckckckckck k k k k k k k k KKK KKK e e e e KKK e e KEKE k k k k k k k k k k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckck k k k k k k k k KKK KK RK KKK KKK KER KERR RRR ERE k k k k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckckckckckckckckckok KEKE KKK k k k Ckokckckckckckckckckckckckckckckckckckckckckckck ck k k k KKK KKK KKK KR KKK RRR KEKE KEKE k k k 10 0 5 OY O OO OO O O O O O O UJ OO 0 KKK KK KK KK KK KK KR KEK KK KEK EKER EKER k k k k k k k k k k k KK KK KK KK KK KK KK KK KR KER KKK KK KK KK KK KK KEK KK EKER ESTIMATED PERCENTAGE 15 0 46 32 26 23 20 4 to i0 O NN dH OY O0 O UJ OY XO 4 XO UJ iO AN O UJ OY ds 1o Ul O OO UJ 1o OH gt gt OY ON iO lOO OOr BUD AI 2 KKK KK KK KK KK KK KK KR KR KK KR KER KR EKER KR EKER k k k k k k k k k 20 0 45 31 26 22 20 34
3. 100 125 150 200 250 300 350 400 450 500 750 NOTE 64 0 1 1 0 2 0 ok kk k k k k 50 2 50 0 kk kk k k k k 35 5 35 3 kk k k k k k k 29 0 28 9 kk kk k k k k 25d 25 0 ok k k k k k k OU XE 22 4 ok kk k k k k 20 5 20 4 kk kk k k k k 19 0 8 9 kk kk k k k k 17 8 7 27 kk kk k k k k 16 7 627 K k k k k k k k k k k k k k k k 5 8 Ckokckckckckckckckckckckckckokok 5 1 K k k k k k k k k k k k k k k k 4 4 K k k k k k k k k k k k k k k k 3 9 K k k k k k k k k k k k k k k k 3 4 k k k k k k k k k k k k k k k 24 9 K k k k k k k k k k k k k k k k 2 5 K k k k k k k k k k k k k k k k DR K k k k k k k k k k k k k k k k 1 8 KR KKK KEKE KEKE KEKE KEK Ckokckckckckckckckckckckckckckckckck k k k k k k Ckckckckckckckckckckckckckckckckckckckckckckckok Ckokckckckckckckckckckckckckckckckckckckckckckok EEEE EEE EEEE EEEE EEEE EE Ckokckckckckckckckckckckckckckckckck k k k k k k Ckokckckckckckckckckckckckckckck k k k k Ckokckckckckckckckckckckckckckckckck k k k KR KKK EEEE EEEE EEEE EE EE EE Ckokckckckckckckckckckckckckckckckck k k k k k k Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for Atlantic Ts o 0
4. 16 8 16 3 4 ee 2222 14 5 14 1 5 FOR RR 322222222222 2522222 13 0 12 6 6 222222222222 11 5 7 ARA RARA RARA RARA RRA RARA koe ek ek k k k k k k 10 7 8 FOR RR KK KR RK KR RK IK RR RR KR KK k k k k k k k k 10 0 9 FOR RK KKK KR KR RK KR KK IK RR KK KKK k k KR KR IK IK k k kk k k k k k KR k k k 0 FOR RR 252222222222 222222222222 j FOR RR KKK KK KK KK IK ek ek KKK IK IR KK KR ke ke ek RK KK KK KR KR RK k k k k ek k k k 2 FOR RK KKK KK KK KK KK RR RK KKK RK IK KK IK KR RK KK KK kk RK k k ek eek k k k 3 FOR RR KKK KK KK KK IK IK RK KK KK KK KK IK IK RR k k k k k KR RK k k k k k k k k k k 4 FOR RK KKK KK IK KK KK IK RR KKK KOK IK KK KK IK RR KKK KK OK KR KKK IK k k k k k k KK k k k k k 5 FOR RR KKK KK KR KKK IK IK RK KKK KK IK KK KK IK ROKR KKK IK IK KR k k k IKK KR KR RK k k KR ke 6 FOR RR KKK KK KR KK IK ek RR KKK KK IK RAR RRA I KR k k KR k k k k k k k k k k k kkk k k k k k 7 FOR RR KKK KK KK KK IK OK RK KKK KK 222222222222 222222222222 k k k k k k k k k 8 FOR RK KKK KK IKK KK KK ek RK KKK OK IK KKK IK KR RK KKK IK KR KKK IK KR RR k k k KR KR RR KICK k k k k RR 9 20 21 22 23 24 25 30 35 40 45 NOTE FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION Special Surveys Division 35 0 24 7 17 4 14 2 1223 11 0 TOS 9 3 837 8 2 7 8 7 4 6 8 6 6 6 4 652 6 0 5 8
5. 10 W 4 Ckokckckckckckckckckckckckckckckckck Ckokckckckckckckckckckckckckckckckckckckckckckok Ckokckckckckckckckckckckckckckckckck KR KKK KEKE KEKE KEKE KKK ck Ckokckckckckckckckckckckckckckckckckckckckckckok Ckckckckckckckckckckckckckckckckckckckckckckckok Ckokckckckckckckckckckckckckckckckck Ckokckckckckckckckckckckckckckckckck KERR RK KR KKK KEKE KEKE KEKE KEK k k k k k KR KKK KK ERE RE KEKE KKK Household Internet Use Survey 2002 User Guide Approximate Sampling Variability Tables for Quebec u 102 4S A RAR 8 BUAN o WVA gt gt amp yu OO 2 01 O 3 KKK KKK KKK EKER RRR KEKE KEKE KA k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckckckckckckckckckok KEKE ARA KA KARA KA k k k k k k k HOUSEHOLD INTERNET USE SURVEY 10 0 DO NO NO Yu Y dg de UO i0 N d O O O HP U Ul O amp I0 O 5 4 IB iO UD JN AAN O
6. RR NEPEAN Be A PN CA HUN Bm gt gt gt gt O O LVO OO HH NN UU amp Ul O I 7 KKK KK KK KK KR KK KK KK KK KK KK KER KR KR 25 0 43 30 255 215 o gt gt gt HS Hs OY OY O OO OO iO OO OO OP UU PRUAN k k k OY 4 O iO d OY iO IO lO O iO OY iO 00 1 E 2 1 30 0 NNN og 2 4 O OQ O iO O Ul O IP O N Oy i0 0 JN HI 10 IN dH lo Y gt gt Pr HS Hs ULULULULOT OY 0 OO iO iO gt BOA 7 KR KR KK RK KK KK KK KK KK KK KK KK KK KK KK KR KR KK KK KK KR KR KK KK KK EK KK k k k k k k k k k k k k k k k k k k k 35 0 NNN og 2 OY XO OY IB NVU d OY iO O UJ OO HP 4d OY iO INN 100 HS 4 OY I0 HS UV I 0 4d gd ds gt uS Hs 4S OON 0 OO OO OO OO BRUA 4 k k k k RK KK KR KR KK KK KK KK KK KK KK KK KK KR KK RK KR KK
7. OH oU 00 P NON I0 NN RS IN 3 2002 30 0 DN WW gt Ul O OO O J PU Ul i0 0 O A 34 os FB i000 O iO UU 9 00 IP IOO P0 l000 00 W 4 gt 4S OY OO iO iO ID N WW d J oo 2 35 0 NO Y dg ds OO i0 N UJ amp O gt IN O0 10 d F2 0 O NM iO UJ Xo Oy d XO d VO Oy d d IO N Oy dH lo d IO Oy d JP 0 ONA gt gt 4 4S OY 200 OO OO O iO I 0 Py 40 0 d OO JP 2 OSJONDALOVWNIHOAHRNWONUVPUNONSHWAOHRH www gt HS 4 OO O iO N NVU RAR OY OY Oy J NND vu w Ov 6 KKK KK KK KK KK KK KK KK KK KK KR KR KK KK KK KK KK KR KK KK KK KR k RK RK RK KK KK KK KK KK HH KK KK RK KK KK KK KR KR KK KK KK KK KK KR KR k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k PERCENTAGE 000 0 1 1 104 7 2 74 0 3
8. IP 0000 0 OY Wo gt gt iO iO iO 40 0 50 0 70 0 42 39 30 3 30 27 21 4 24 22 17 55 21 9 45 52 9 13 6 12 4 11 5 0527 10 9 6 8 4 8 6 4 w 4 O UJ dH N OY OO 10 OY HP A 2 dom gt gt gt OY 2 OO OO O iO O LOCO OH NIU PUAN oN gt d gt gt Bs HS Hs OY y OO NU O0 OO O Special Surveys Division 90 0 I H H H H HB B N N N N N N N UU U UU U 0 0 iS iS iS UL 14 O0 O OHnptu 00 00 NU N Ul O O HB NM amp Ul U OD O P 0 OO HP Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for New Brunswick NUMERATOR OF ESTIMATED PERCENTAGE PERCENTAGE 1000 0 1 1 0 2 0 5 0
9. 7 0 6 9 6 6 6 4 6 2 6 0 5 8 DES 4 1 2 3 75 IR RRR RRR 75 0 6 8 6 6 6 4 6 2 6 0 5 8 546 bu 3 59 253 80 EK EAS 6 8 6 6 6 4 6 2 6 0 5 8 5 6 5 4 4 9 3 8 2 2 85 HEU ee RET 6 6 6 4 6 2 6 0 5 8 546 5 4 52 4 8 So 251 90 EERE AEA ES ERIK ERA 6 4 6 2 6 0 59 5 5 533 5 1 4 6 3 6 2l 95 AAA 6 2 6 1 55 9 ST 545 54 3 Sl 4 9 4 5 345 2 0 100 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 5 9 5 7 5 6 5 4 5 2 5 0 4 8 4 4 3 4 2 0 125 ck KR KKK KR KKK KKK KR a a 5 3 5 1 5 0 4 8 4 7 4 5 4 3 3 9 350 1 8 150 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 4 8 4 7 4 5 4 4 4 2 4 1 3 9 3 6 2 8 1 6 200 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk AoA 3 9 3 8 au 3 5 3 4 3 1 2 4 1 4 250 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 3 6 3 5 3 4 33 3 2 3 0 2 8 2 2 1 2 300 222222222222 I 2222223 3 2 3 1 3 0 2 9 2 8 2 5 2 0 T 350 FRR RR KR I RR RRR RK 3 0 2 9 2 8 2 7 2 6 233 158 ixl 400 RR KR RK KK KI RARA RARA ko RARA ko ke ke kk e ke ke ke 2 27 2 6 2 5 2 4 2 2 167 1 0 450 RARA RARA RARA RARA RARA RARA RARA RR ke ke kk e ke ke ke 2 5 2 5 2 4 253 2 1 1 6 0 9 500 222222222222 kk RARA 22222222222 222222222222 2 3 2 2 2 39 2 0 1 5 0 9 750 222222222222 222222222222 222222222222 e ke ke ke 1 8 T6 qus 0 7 1000 EI ke RARA 222222222222 222222222222 222222222222 222222223 i1 0 6 1500 222222222222 ek kk ke he kk echec ke kk ke ck kh
10. IND IN gt iS IS dS S UL 1 2 iO Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for Prairies NUMERATOR OF ESTIMATED PERCENTAGE PERCENTAGE 1000 0 1 1 0 2 0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 40 0 50 0 70 0 90 0 1 62 1 61 9 61 5 60 6 590 57 3 55 6 53 8 52 0 50 1 48 2 44 0 34 1 T9 2 43 7 43 5 42 8 41 7 40 5 39 3 38 1 36 8 35 4 34 1 3151 24 1 13 9 3 355 3545 35 0 34 1 3351 32 1 31 1 30 0 28 9 27 8 25 4 19427 11 4 4 30 9 30 8 30 3 2945 28 7 27 8 26 9 26 0 2D 24 1 22 0 170 9 28 5 ERRKERER 24s 27 5 27 523 26 4 25 6 24 9 24 1 2358 22 4 21 5 Lat 15 2 8 8 6 EXETER 25 3 25 1 24 7 24 1 23 4 2257 22 0 23 2 2055 Dat 159 13 9 8 0 7 GO RO RUE CR 23 4 23 3 22 9 2293 21 7 21 0 20 3 8 9 8 2 6 6 12 9 7 4 8 EXER 21 9 21 8 21 4 20 9 20 3 9 0 8 4 TEST 7 0 55b 12 0 7 0 9 EXTRA TER 20 6 20 5 20 2 TF 8 5 749 7 33 6 7 6 1 4 7 11 4 6 6 0 ER ERROR 9 6 9 55 952 8 7 8 1 7 6 7 0 6 4 5 8 55 2 359 10 8 6 2 1 ER IRE E 8 7 8 6 8 3 7 8 7 3 6 8 6 2 Dl 4 5 353 10 3 5 49 2 FREER 79 7 8 9 7 0 6 5 61 535 5 0 4 5 3 29 25 9 8 SET 3 ERR RE 7 2 JI 6 8 6 4 559 5 4 4 9 4 4 329 3 4 2 2 9 4 5
11. 250 300 350 400 450 500 750 1000 1500 2000 13 4 HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for Quebec ESTIMATED PERCENTAGE kkkkkk kkkkkk kkkkkk kkkkkk kkkkkk 10 0 OO Of NO kkkkkk 15 0 96 6 68 3 55 8 48 3 43 2 ak a EN oOoOO DD PROD i00 tn oo fo CO OH kkkkkk kkkkkk 30 0 Ad now MO kkkkkk 35 0 Aa 5 5 5 5 O O N C0 CO O CO 7 40 0 50 0 70 0 90 0 81 1 74 57 4 33 1 57 4 52 4
12. 2 0 125 0 040 0 125 2 0 125 0 040 0 125 0 010 0 125 0 010 0 115 0 135 With 95 confidence it can be said that between 11 5 and 13 5 of households which have never used the Internet reported that they have a computer at home 10 3 How to Use the Coefficient of Variation Tables to Do a T test Standard errors may also be used to perform hypothesis testing a procedure for distinguishing between population parameters using sample estimates The sample estimates can be numbers averages percentages ratios etc Tests may be performed at various levels of significance where a level of significance is the probability of concluding that the characteristics are different when in fact they are identical Let X and X be sample estimates for two characteristics of interest Let the standard error on the difference x 5 X beo Special Surveys Division Household Internet Use Survey 2002 User Guide X f t 2 9 is between 2 and 2 then no conclusion about the difference between the characteristics is justified at the 5 level of significance If however this ratio is smaller than 2 or larger than 2 the observed difference is significant at the 0 05 level That is to say that the difference between the estimates is significant 10 3 1 Example of Using the Coefficient of Variation Tables to Do a T test Let us suppose that the user wishes to test at a 5 level of significance
13. 2 calculate the AVERAGE weight for these records by summing the original household weights from the microdata file for these records and then dividing by the number of households who reported PROVINCE 24 3 for each of these records calculate a RESCALED weight equal to the original household weight divided by the AVERAGE weight 4 perform the analysis for these records using the RESCALED weight However because the stratification and clustering of the sample s design are still not taken into account the variance estimates calculated in this way are likely to be under estimates The calculation of more precise variance estimates requires detailed knowledge of the design of the survey Such detail cannot be given in this microdata file because Special Surveys Division Household Internet Use Survey 2002 User Guide of confidentiality Variances that take the complete sample design into account can be calculated for many statistics by Statistics Canada on a cost recovery basis 9 5 Coefficient of Variation Release Guidelines Before releasing and or publishing any estimate from the Household Internet Use Survey users should first determine the quality level of the estimate The quality levels are acceptable marginal and unacceptable Data quality is affected by both sampling and non sampling errors as discussed in Chapter 8 0 However for this purpose the quality level of an estimate will be determined only on the basis of
14. 4 OY 10 2 OY O N O O I HS Bs HS OV 1 1 0 iO UO d H OU d HATO gt 0 640000 HB Special Surveys Division 90 0 ON ON IND WW S iS IS dS S UL OY OY O i0 O N U O N d O UJ Ul 4 BUN 0 9 Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for Saskatchewan NUMERATOR OF ESTIMATED PERCENTAGE PERCENTAGE 1000 0 1 1 0 2 0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 40 0 50 0 70 0 90 0 T RRR EEE 44 1 43 9 43 2 42 0 40 8 39 6 38 4 3 4 3 35 7 34 3 3143 24 3 14 0 2 ERRE 31 2 31 0 30 5 29 7 28 9 28 0 21 26 2 25 3 24 3 22 2 17 2 9 29 3 RES 2 DAD 2543 24 9 24 3 23 6 22 9 22 2 21 4 20 6 19 8 18 1 14 0 8 1 4 21 9 21 6 21 0 20 4 9 8 9 2 83D 57 59 1752 LB T2 7 50 5 19 6 93 8 8 8 3 TER TIA 6 6 16 0 15 3 14 0 50 29 6 3 6 EXETER ES 17 9 736 7 22 6 7 6 2 547 14 6 14 0 12 8 9 59 bis 4 7 EEE EERE EEE EN 16 6 6 3 579 5 4 5 0 4 5
15. 81 80 77 75 68 66 62 58 45 150 73 71 6 9 62 60 58 53 41 200 63 61 5 9 54 52 50 46 35 250 56 55 53 48 47 45 41 32 300 Se 5 0 49 44 42 41 37 29 300 46 45 41 39 38 34 27 400 4 38 37 35 32 2 5 2 PERF 4 0 36 35 33 30 24 500 or 40 39 38 34 33 32 29 22 750 E A 28 27 26 24 18 1000 5 7 527 24 23 22 20 16 1500 de ee 20 19 18 17 43 2000 kkkk 1 7 1 6 1 6 1 4 1 4 3000 kkkk 1 A 1 3 1 3 1 2 0 9 4000 ke e x 1 2 1 4 1 0 0 8 5000 kkkk kk ve xXx kkkk kkkkkk 0 9 0 7 6000 k kkkkkk 0 8 0 6 7000 kkkk kkkk k kkkk kkkkkk 0 6 8000 kkkk kkkk kkkkkk 0 6 9000 KKK kkkkkk CO CC COO DOO rcr rr E TOCOCO CO CO C9 CXCO CO 5 t0 O 1 0000 kkkkk kkkkkk 4 So the approximate coefficient of va
16. Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for Ontario NUMERATOR OF ESTIMATED PERCENTAGE PERCENTAGE 1000 0 1 1 0 2 0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 40 0 50 0 70 0 90 0 1 100 5 100 0 99 5 98 0 95 4 92 27 89 9 8721 84 1 ell 77529 TIG 31 8 2 712 70 7 70 4 69 3 67 4 65 5 63 6 61 6 59555 57 3 55d 50 3 38 9 22 55 3 58 0 57 8 57 5 56 6 5521 5355 5 T9 50 3 48 6 46 8 45 0 41 0 31 8 18 4 4 50 2 50 0 49 8 49 0 47 7 46 3 45 0 43 5 42 1 40 5 38 9 3535 2755 159 5 EER ERE 44 7 44 5 43 8 42 7 41 5 40 2 38 9 37 6 36 3 34 8 31 8 24 6 14 2 6 40 8 40 6 40 0 38 9 37 8 36 7 35 5 34 3 33441 31 8 29 0 225 13 0 7 VOCE KO 37 8 37 6 37 0 36 1 35 0 34 0 32 9 31 8 30 6 29 4 26 9 20 8 12 0 8 EEE 35 4 3552 34 6 33 32 8 31 8 30 8 29 7 28 7 27 5 2553 9ub5 11 2 9 EXTRA TES 33 3 33 2 32 57 31 8 30 9 30 0 29 0 28 0 27 0 26 0 2347 8 4 10 6 0 31 6 31 5 31 0 30 2 29 3 28 4 23 5 26 6 25 6 24 6 22 25 7 4 10 1 1 ER IRE Re 30 2 30 0 2945 28 8 27 9 27 1 26 3 25 4 24 4 23 5 21 4 6 6 946 2 FAR 28 9 28 7 28 3 2755 26 8 26 0 2 24 3 23 4 22 5 20 5 5559 9 2 3 ZEIT 27 6 27 2 26 5 25 7 24 9 24 1 299 22 5 21 6 9x 53 8 8 4 26 7 26 6 26 2 2565 24 8 24 0 23 23 225 2147 20 8 9 0 4 7 8 5 5 RC AEE 25 8 25 7 2553 24 6 23 9 23 2 22 5 21 7 20 9 20 1 8 4 4 2 8 2 6 FAAS 25 0 24 9 2
17. Microdata User Guide HOUSEHOLD INTERNET USE SURVEY 2002 CE ene Canad Household Internet Use Survey 2002 User Guide Table of Contents 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 Huge 5 rien ii ia RI RIA ATAN Rda 7 iS Jd ai a p aaa aaa re aa aa pa aaaea a Aa aaa a a a aa aaaea a aa aa aa aaa Ta aaa aa r Sas ade da naden Ed siaina 9 Concepts and Definitions 2 denia esce daoias aiita 11 4 1 Labour Force Survey Concepts and Definitions esee 11 4 2 Household Internet Use Survey Concepts and Definitions 12 4 3 Labour Force Survey Variable Definitions pt 14 Survey Methodology Sa 19 5 1 Population Coverage si ae Erb a ar ie c eX Peg Perd 19 5 2 Sample Design repete E t Sev 19 5 2 1 Primary Stratification tiroir rte rore ghe re M 19 5 2 2 TO OL Ra 19 5 2 3 Secondary StratifiCatiOn coe rre o erect er ent rt prt vi p dee exte E eet ded 20 5 2 4 Cluster Delineation and Selection 20 5 2 5 Dwelling Selection ctn rrt i re exei arte i Pet ghe er i ce 21 5 2 6 Person Selection uicti a e E e bei rita EE P EE e eed e M red 21 5 3 Sample iZ ita a a ee A 21 5 4 Sample ROtatlon mn en ed ea aea Adad aa oaia ndanda 21 5 5 Modifications to the Labour Force Survey Design for the Household
18. 11 Household Internet Use Survey 2002 User Guide Not in the Labour Force Persons not in the labour force are those who during the reference week were unwilling or unable to offer or supply labour services under conditions existing in their labour markets that is they were neither employed nor unemployed Industry and Occupation The Labour Force Survey provides information about the occupation and industry attachment of employed and unemployed persons and of persons not in the labour force who have held a job in the past 12 months Since 1997 these statistics have been based on the North American Industry Classification System NAICS and the Standard Occupational Classification SOC 91 Prior to 1997 the 1980 Standard Industrial Classification and the 1980 Standard Occupational Classification were used Reference Week The entire calendar week from Sunday to Saturday covered by the Labour Force Survey each month It is usually the week containing the 15th day of the month The interviews are conducted during the following week called the Survey Week and the labour force status determined is that of the reference week Full time Employment Full time employment consists of persons who usually work 30 hours or more per week at their main or only job Part time Employment Part time employment consists of persons who usually work less than 30 hours per week at their main or only job 4 2 Household Internet Use Surve
19. JAY HD NU BUD 0 7 KKK KK KK KK KR KK KK KK KR KER KKK KK AREA RA k k k k k k ESTIMATED PERCENTAGE 15 0 96 68 55 48 43 39 36 34 325 30 29 2 26 25 24 24 234 225 22 21 2 20 20 4 o O lO OO lO 24 00000 00 HN lO Qo 4 IB i000 HS 00 OO WO PN NU dH O Jl 6 k k 20 0 93 66 54 46 41 38 35 33 31 29 28 2d 264 25 24 23 22 225 21 20 20 20 4 to amp OD OH NIHUIOPL LUHNIPDNDOOONANHPNLDOHMN UI O0 O iO O O O HP HP NM d KG 2 Ckokckckckckckckckckckckckckckokckckckokckokckokckokckckckckckckckckckckckckckckckckckckckckckck k k k k k k k 25 0 90 64 52 45 40 37 34 32 30 28 2 26 25 24 23 22 225 24 20 20 o gt gt gt UI OY J OO O H P NM N UJ WO 34 KK HK HK HK HK HK HK HK HK HK HK KH KH KR KK k k KK k k k k k k k k k k k k k k k k k k k 1
20. The coefficient of variation depends only on the size of the estimate itself On the Approximate Sampling Variability Table for the appropriate geographic area locate the estimated number in the left most column of the table headed Numerator of Percentage and follow the asterisks if any across to the first figure encountered This figure is the approximate coefficient of variation Rule 2 Estimates of Proportions or Percentages of Households Possessing a Characteristic The coefficient of variation of an estimated proportion or percentage depends on both the size of the proportion or percentage and the size of the total upon which the proportion or percentage is based Estimated proportions or percentages are relatively more reliable than the corresponding estimates of the numerator of the proportion or percentage when the proportion or percentage is based upon a sub group of the population For example the proportion of households which have never used computer communications is more reliable than the estimated number of households which have never used computer communications Note that in the tables the coefficients of variation decline in value reading from left to right When the proportion or percentage is based upon the total population of the geographic area covered by the table the CV of the proportion or percentage is the same as the CV of the numerator of the proportion or percentage In this case Rule 1 can be used When
21. and are the coefficients of variation of X and respectively That is the standard error of the difference d 0 383 0 556 0 173 is Special Surveys Division 49 50 Household Internet Use Survey 2002 User Guide 4 0 383 0 027 0 556 0 010 0 0001069 0 0000309 0 0117 3 The coefficient of variation of d is given by 0 0117 0 173 0 068 4 Sothe approximate coefficient of variation of the difference between the estimates is 6 896 This estimate is publishable with no qualifications Example 4 Estimates of Ratios Suppose that the user estimates that 1 192 540 households in Quebec reported that one or more members of their household use a computer at home for E mail in a typical month HUQ11 1 Yes while 2 523 213 households in Ontario reported that one or more members of their household use a computer at home for E mail in a typical month HUQ11 1 Yes The user is interested in comparing the estimate of Quebec households versus that of Ontario households in the form of a ratio How does the user determine the coefficient of variation of this estimate 1 First of all this estimate is a ratio estimate where the numerator of the estimate X is the number of households in Quebec which reported that one or more members of their household use a computer at home for E mail in a typical month The denominator of the estimate X is the number of househol
22. regularly but did use it regularly in the past Never user A household responding no to the question GUQ02 Has anyone in your household ever used the Internet E mail or world wide web from home work school or any other location In other words a household that has never used the Internet Typical month A typical month refers to a month that is not out of the ordinary for the household A typical month is always in relation to a certain period of time usually in the past year The period of time to be used for defining a typical month was left for the respondent to determine Penetration rate The proportion or percentage of a population adopting a particular activity A penetration rate answers the question to what extent has an activity permeated a specified population Any location Includes Internet use from home work school a public library or some other location and designates a household as only using once irrespective of use from multiple locations Internet The Internet connects computers to the global network of networks for electronic mail services file transfers and information search and retrieval Influence and window shopping Refers to the effect that the Internet may or may not have had on the purchase of products and services by the household Electronic transaction The sale or purchase of goods or services whether between businesses households individuals governments and other public or priva
23. the hypothesis that there is no difference between the proportion of households in Quebec which reported that one or more members of their household use a computer at home for E mail in a typical month and the proportion of households in Ontario which reported that one or more members of their household use a computer at home for E mail in a typical month From Example 3 Section 10 1 1 the standard error of the difference between these two estimates was found to be 0 0117 X X _ 0383 0556 0173 _ t 0 0117 0 0117 Since t 14 8 is less than 2 it must be concluded that there is significant difference between the two estimates at the 0 05 level of significance 10 4 Coefficients of Variation for Quantitative Estimates For quantitative estimates special tables would have to be produced to determine their sampling error Since most of the variables for the Household Internet Use Survey are primarily categorical in nature this has not been done As a general rule however the coefficient of variation of a quantitative total will be larger than the coefficient of variation of the corresponding category estimate i e the estimate of the number of households contributing to the quantitative estimate If the corresponding category estimate is not releasable the quantitative estimate will not be either For example the coefficient of variation of the total number of orders for products or services would be greater than t
24. x k X k where k is determined depending upon the level of confidence desired and the sampling error of the estimate Confidence intervals for an estimate can be calculated directly from the Approximate Sampling Variability Tables by first determining from the appropriate table the coefficient of variation of the estimate X and then using the following formula to convert to a confidence interval CI 4 CI Xa io Special Surveys Division 51 52 Household Internet Use Survey 2002 User Guide where Os is the determined coefficient of variation of X and t 1 if a 68 confidence interval is desired t 1 6 if a 9096 confidence interval is desired t 2 if a 9596 confidence interval is desired t 2 6 if a 9996 confidence interval is desired Note Release guidelines which apply to the estimate also apply to the confidence interval For example if the estimate is not releasable then the confidence interval is not releasable either 10 2 1 Example of Using the Coefficient of Variation Tables to Obtain Confidence Limits A 95 confidence interval for the estimated proportion of households which have never used the Internet and have a computer at home from Example 2 Section 10 1 1 would be calculated as follows X 125 or expressed as a proportion 0 125 t 2 Qi 4 0 0 040 expressed as a proportion is the coefficient of variation of this estimate as determined from the tables 0 125
25. 10 0 15 0 20 0 25 0 30 0 35 0 40 0 50 0 70 0 90 0 T REE EE 47 5 47 2 46 5 45 3 44 0 42 7 41 3 39 9 38 5 37 0 33 7 26 1 15 1 2 UXOR KHOE 33 6 33 4 32 9 32 0 31 1 30 2 29 2 28 2 2152 26 235 9 18 5 1057 3 NOR REE ES 2743 26 9 26 25 4 24 6 2 9 23 1 22 2 21 3 195 151 8 7 4 EERE ne 23 6 2353 22 6 22 0 2153 20 7 20 0 9 2 8 5 16 9 134 7 55 5 21 51 20 8 20 2 9 9 21 8 55 749 7 2 6 5 15 1 LLA 6 7 6 EA REE EIR EAE RI ER RES 19 0 85 8 0 7 4 6 9 6 3 547 Sa 13 8 10 7 6 2 7 EERE KORR 17 6 Ta 6 6 6 1 5 6 4 5 4 0 12 8 9 9 Did 8 RRA A 16 4 6 0 5 6 Sa 4 6 4 1 346 37 11 9 952 543 9 1525 Dis 4 7 4 2 359 33 2 8 2 3 11 2 8 7 50 0 RAE OE EUR SOR RRR ICR EE 14 7 4 3 359 35 3l 2 6 2 2 2 4 7 10 7 853 4 8 1 EERE RAE REE 14 0 3 37 3 3 249 2 5 2 0 1 6 1 10 2 7 9 4 6 2 ROR OR RR RR KARA KARA k k k k k k k 13 4 3 7 2 7 2 3 1 9 1 5 1 1 0 7 9 7 Jb 4 4 3 RRR DE EORR EE 12 9 2 6 2 2 Ls 8 bea Lot 0 27 0 3 9 4 733 4 2 4 H He e ke e ke e k k ke k k k k k k k k k k k k k k 12 4 D gt 1 8 1 4 4 0 0 7 0 3 9 9 9 0 FO 4 0 5 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 2 27 1 4 1 20 0 0 3 9 9 9 5 8 7 6 7 3 9 6 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 13 3 1 0 0 7 0 3 0 0 9 6 9 2 8 4 6 5 3 8 7 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 2520 0 7 0 4 0 0 9 7 9 3 9 0 8 2 6 3 3 8 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 0 7 0 4 0 1 9 7 9 4 9 1 8 7 8 0 6 2
26. 15 16 17 MEM13 17 18 25 Data for these variables are collected by the LFS and indicates the presence of household members of different age ranges For example MEMO 5 indicates the presence of household member s aged 0 5 years EMPLSTAT Data for this variable are collected by the LFS and indicates the employment status of household members aged 18 years and older 1 Employed if any members are employed Employed persons are those who during the reference week did any work for pay or profit or had a job and were absent from work 2 Unemployed if all members are unemployed Unemployed persons are those who during reference week were available for work and were either on temporary layoff had looked for work in the past four weeks or had a job to start within the next four weeks 3 Notin the labour force if all members are not in the labour force Persons not in the labour force are those who during the reference week were unwilling or unable to offer or supply labour services under conditions existing in their labour markets that is they were neither employed nor unemployed 4 No member older than 17 EMPLOYER Data for this variable are collected by the LFS and indicates whether the household has any members aged 18 or older who are employed by an employer EMPLOYER refers to those who work as employees of a private firm or business or those who work for a local provincial or federal government for a g
27. 4 898 records in the LFS were found that should have matched to an HIUS record but did not These records were coded as in scope since they were eligible records from the frame which for one reason or another did not have corresponding HIUS records These records were considered to be non responding records and were used in the weighting process to adjust for non response Data processing of the HIUS was done in a number of steps including verification coding editing imputation estimation confidentiality etc Since the data were collected using a CAI instrument data quality before processing was very high Very few changes were made to the data during editing At each step a picture of the output files is taken and an easy verification can be made comparing files at the current and previous step This greatly improved the data processing stage 8 2 4 Non response A major source of non sampling errors in surveys is the effect of non response on the survey results The extent of non response varies from partial non response failure to answer just one or some questions to total non response Total non response occurred because the interviewer was either unable to contact the respondent no member of the household was able to provide the information the respondent refused to participate in the survey or not enough information was collected in the interview Total non response was handled by adjusting the weight of households that respon
28. 5 4 5 59 5 0 4 9 4 7 4 5 4 1 3 2 1 8 200 222222222222 I e ke e ke ke ke 4 5 4 4 4 2 4 1 3 9 3 6 2 8 1 6 250 ee RARA 222222222222 2222227 3 9 3 8 3 6 3 5 3 2 2 5 1 4 300 KEN RARA RARA ARA RR ke RE ke ke ke ek 3 4 3 3 3 2 2 9 233 1 53 350 HK RR KK I KR KR OR RR RR RR RR RR RK RK 3 1 2 9 2 Di Y 1 2 400 ARA RARA RAR RARA RARA RARA RARA RARA RARA RAR RARA RARA ko kc kk ke kk ke ke 2 9 2 8 2 5 2 0 Lal 450 Be RARA RRA RARA 222222222222 kk ko kk ke ke ke koe 2 6 2 4 1 8 dE 500 KARA RARA RARA RARA ee 2 3 4 27 1 0 750 222222222222 KI BKK RK 222222222222 222222222222 222222222222 222222222223 TA 0 8 1000 Tere Pe eee ee eee ee ee ee che ke ke kk ehe kk ke ko kk ke kk 222222222222 ee 0 7 NOTE FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION 62 Special Surveys Division NUMERATOR OF Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for British Columbia ESTIMATED PERCENTAGE PERCENTAGE 1000 0 1 1 0 2 0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 40 0 50 0 1 92 2 92 47 91 3 89 9 87 5 85 0 82 5 79 9 77 2 74 3 71 4 65 2 2 64 9 64 5 63 6 61 9 60 1 58 3 56 5 54 6 52 6 50 5 46 1 3 LER REE 53 0 52 7 5149 50 5 49 1
29. 6 85 UR RA KK UR 0 8 0 6 0 3 Os T 9 8 9 4 9 1 8 8 8 4 Ts 6 0 3 4 90 GRE ROESEOCACKE EEK ES 05 0 3 0 1 9 8 125121 9 22 8 9 8 5 8 2 V5 558 3 4 95 222222222222 0 1 9 8 9 5 9 2 8 9 8 6 8 3 8 0 7 3 Bey 313 100 222222222222 RR RR 9 8 9 5 9 3 9 0 8 7 8 4 8 7 8 5 5 cee 125 RRR RAK RR RR ICR RRR ER 8 8 85 8 3 8 0 7 8 755 7 3 7 0 6 4 4 9 2 8 150 SOR CKHKGE E KORG REE REE ES HERE E 8 0 7 8 7 6 us Jio 6 9 6 6 6 4 5 8 4 5 2 6 200 EARL ERASER RRA SARA EE 6 9 6 7 6 6 6 4 6 2 5 49 Sad S25 5x0 3 29 2 2 250 FRR RRR HK AK ke ke RK 6 0 5 9 5 21 5 5 5 3 5 4 9 4 5 3 5 2 0 300 222222222222 22222222 5 5 5 4 5 2 5 0 4 9 4 7 4 5 4 1 3 2 1 8 350 222222222222 22222222 BE 5 0 4 8 4 7 4 5 4 3 4 2 3 8 2 9 1 59 400 222222222222 ek ko kk ke ke kk ke ke ke RK 4 8 4 6 4 5 4 4 4 2 4 3 9 E 2 8 1 6 450 222222222222 2222222 4 5 4 4 4 2 4 1 4 0 3 8 37 3 4 226 1 5 500 RARA RARA RARA RARA RARA ke ko ke ke 4 1 4 0 3 9 3 8 3 6 3 5 3 2 2 5 1 4 750 FRR RR KR KR KK KR khe ko RR e ke e ke ke ke 3 3 3 2 32 3 0 2 8 2 6 250 1 2 1000 222222222222 222222222222 22222252227 2 8 26 2 35 2 52 27 1 0 1500 222222222222 222222222222 222222222222 222252227 ST 2 0 Jag od 0 8 2000 Perce RARA RAR I RAR DE RAR RARA RARA RARA RARA e ke kk ke ke ke ke 1 56 1 2 0 7 3000 Sect Peco eee I RAR RAR RARA RARA RARA ko ke ke kk e ke kk e ke ke ke 1 0 0 6 4000 LEI ke ek kk
30. 6 8 3 8 0 7 8 7 4 6 8 53 95 EXER 953 943 8 9 8 6 8 4 854 7 8 75 xs 6 6 5d 100 EXTRA TER 9x 9 0 8 9 85 7 8 4 8 2 Trd 7 6 7 4 Tak 6 4 5 20 125 BOO OK GOAT KOH RE EN WR ES gt 8 0 Week 33 5 753 a 6 8 6 6 63 5 8 4 5 150 JOE GU EHE RUE 7 4 73 7 2 6 9 6 7 6 4 6 2 6 0 5 8 Dia 4 1 200 ER 6 4 6 3 6 1 549 5 58 536 5 4 DO 5x0 4 6 34 5 250 222222222222 5 6 5 5 5 3 BTY 5 0 4 8 4 7 4 5 4 1 3 5 300 222222222222 5 0 4 9 4 7 4 6 4 4 4 2 4 1 3 7 2 9 350 222222222222 4 8 4 6 4 5 4 4 4 2 4 1 3 9 3 8 3 4 2 7 400 222222222222 RR RR 4 4 4 3 4 2 4 1 3 9 3 8 3 7 3 5 222 25 5 450 222222222222 22227 4 2 4 1 4 0 3 8 3227 6 3 5 3 3 3 0 2 4 500 222222222222 RR 4 0 3 9 3 8 2 6 A5 3 4 3 3 37 19 2 9 2 22 750 RR kk ke kk ke ke ke RK 3 2 3 1 3 0 2 9 2 8 259 256 224 1 8 1000 222222222222 222222 DES 22 7 2 6 2 5 2 Qu 2 20 2 0 T 36 1500 RA RARA RARA RARA RARA RAR RAR 222 251 2 0 2 0 1 29 148 1 7 1 3 2000 222222222222 kk I KR e ke kk e ke ke ke 1 8 1 8 4457 26 26 1 4 Tc 3000 222222222222 222222222222 ko ko ke ke kk ek kk e ke ke ke T4 amp S 1 3 1 3 1 2 0 9 4000 222222222222 222222222222 222222222222 ko kk e ke ke RK 1 2 Tat 1 0 0 8 5000 Perce eee ee ee eee RARA RARA ke kk eee kk eee eee ek kk e ke ke ke 0 9 0 7 6000 Perce eee eee eee ee ee P
31. 60 4 4 RRR RRR RK 5 KR RRR RA 6 7 RRR RRR RK 8 RRR RRR RA 9 KR RRR RA 0 KK RRR RK 1 RRR RRR RK 2 okckck kk RA 3 RRR RRR RA 4 5 6 KR RRR RA 7 RRR RRR RA 8 RRR RRR RA 9 20 2 RRR RRR RA 22 KR RRR RA 23 okckck ck kk ke 24 KR RRR EK 25 RRR RRR RA 30 RRR RRR RA 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 200 250 300 350 400 450 500 750 1000 1500 2000 NOTE 58 FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION Special Surveys Division 50 0 0 O4 0000000 io I lOO UO U1P oO UH wa JNONKNFTP POOHs5mD 70 0 00 ARO A EB HE gt OY OY NN OO OO O iO N O 90 0 O O U iS O OO 0H ONA OO io IB 0 lO lOO JUDO nan OOHnHHHH N iS iS iS OO oo
32. 7 3348 32 8 31 9 30 8 29 8 28 7 27 6 25 2 925 11 3 5 ERRKERER 31 7 3145 31 0 30 2 29 4 28 5 27 6 26 7 2574 24 7 2225 7 4 10 1 6 EXETER 28 9 28 8 2853 27 6 26 8 26 0 2522 24 3 23 4 2225 20 6 5529 972 7 WO RO RUE 26 8 26 7 26 2 255 24 8 24 1 23 3 22 5 21 7 20 9 9 0 4 7 8 5 8 EXER 25 4 24 9 24 5 23 9 22 2 2255 21 8 2724 20 3 9 5 7 8 3 8 8 0 9 EXTRA TER 23 6 234 5 22 5 21 59 21542 20 6 9 29 9 4 8 4 6 8 3 0 hed 0 22 4 2253 22 0 21 4 20 8 20 1 935 8 8 8 2 7 4 55 9 253 TT 1 PENE ES 21 4 21 3 20 9 20 4 9 8 922 8 6 8 0 7 3 6 6 Dar 1 8 6 8 2 FREE 20 4 20 0 945 9 0 8 4 7 8 452 6 6 549 4 5 Tas 6 5 3 REF 9 6 93 8 7 8 2 V Fal 6 5 529 553 4 0 0 8 6 2 4 RRA EORR ERE EEK 8 8 8 6 GoL 7 6 70 6 5 5 9 55 3 4 7 35 0 4 6 0 5 ROCK ORDER EEE EN 8 2 59 7 4 7 0 6 5 5 9 5 4 4 8 4 2 3 0 0 1 548 6 ERS 7 6 7 4 6 9 6 4 549 5 4 4 9 4 4 3 8 2 6 9 8 5 56 Y EERE EEK 72 6 8 6 4 5 59 555 5 0 4 5 359 3 4 202 9 5 5 5 8 ERR ee 6 6 6 4 5 59 535 5 0 4 5 4 0 335 3350 1 9 932 5 3 9 6 2 5 9 bab Dat 4 6 4 2 3u7 3 2 247 1 6 9 0 5 2 20 EAE i 5 8 Sack 4 7 4 2 3 8 343 2 8 2753 123 84 7 55 0 21 SEER AEA OR ERROR 5 4 5 2 4 7 4 3 3 29 3 5 3 0 2 5 2 0 240 8 5 4 9 22 ERASER EE 5 0 4 8 4 4 4 0 3 46 34 2 23 1 2 72 1 8 0 7 8 3 4 8 23 4 7 4 5 4 1 3 343 259 2 4 2 0 I5 0 5 8 1 4 7 24 EEK E AAI ERS EEK 4 2 3
33. Coefficient of Variation Tables to Obtain Confidence Limits Although coefficients of variation are widely used a more intuitively meaningful measure of sampling error is the confidence interval of an estimate A confidence interval constitutes a statement on the level of confidence that the true value for the population lies within a specified range of values For example a 9596 confidence interval can be described as follows If sampling of the population is repeated indefinitely each sample leading to a new confidence interval for an estimate then in 95 of the samples the interval will cover the true population value Using the standard error of an estimate confidence intervals for estimates may be obtained under the assumption that under repeated sampling of the population the various estimates obtained for a population characteristic are normally distributed about the true population value Under this assumption the chances are about 68 out of 100 that the difference between a sample estimate and the true population value would be less than one standard error about 95 out of 100 that the difference would be less than two standard errors and about 99 out of 100 that the differences would be less than three standard errors These different degrees of confidence are referred to as the confidence levels Confidence intervals for an estimate X are generally expressed as two numbers one below the estimate and one above the estimate as
34. Internet Use SUVOY er id 22 5 6 Sample Size by Province for the Household Internet Use Survey 22 Data Collection Beer IL 23 6 1 Interviewing for the Labour Force SUrvey nent 23 6 2 Supervision and Quality Control sese 23 6 3 Non response to the Labour Force Survey Nt 23 6 4 Data Collection Modifications for Household Internet Use 24 6 5 Non response to the Household Internet Use 24 Data Processing entem IL ede ice DIL Ee 25 7 1 ee se a E E AT 25 7 2 Editing ti a ers 25 7 3 Coding of Open ended Questions nn 25 7 4 MP a T eee 26 7 5 Creation of Derived Variables pp 26 7 6 Weg MING ES 26 7 7 Suppression of Confidential Information sss eene 27 Data Qualiiy Drm 29 8 1 Fesponse Rales s t sae bebo eie nobit 29 8 2 Survey Errors eL E 29 8 2 1 THe FAME a npe d a HERR 30 8 2 2 Data Collection cion a ll 30 8 2 3 Dat Processing 2 2 x dag ci 31 8 2 4 Non response ceci A 31 8 2 4 1 Mputa 31 8 2 5 Measurement of Sampling Error 34 Special Surveys Division 3 9 0 10 0 12 0 13 0 Household Internet Use Survey 2002 User Guide Guidelines for Tabulation Analysis and Release serene 35 9 1 Rounding ate tt ee aa akida 35 9 2 Sample Weighting Guidelines for T
35. assignment the cumulative effect of all increases may create a workload problem In clusters where substantial growth has taken place sub sampling is used as a means of keeping interviewer assignments manageable The cluster sub weight represents the inverse of this sub sampling ratio in clusters where sub sampling has occurred Stabilization Weight Sample stabilization is also used to address problems with sample size growth Cluster sub sampling addressed isolated growth in relatively small areas whereas sample stabilization accommodates the slow sample growth over time that is the result of a fixed sampling rate along with a general increase in the size of the population Sample stabilization is the random dropping of dwellings from the sample in order to maintain the sample size at its desired level The basic weight is adjusted by the ratio of the sample size based on the fixed sampling rate to the desired sample size This adjustment factor is known as the stabilization weight The adjustment is done within stabilization areas defined as dwellings belonging to the same employment insurance economic region and the same rotation group Special Surveys Division 67 68 Household Internet Use Survey 2002 User Guide Non response For certain types of non response i e household temporarily absent refusal data from a previous month s interview with the household if any is brought forward and used as the current month s data for
36. atleast 4096 of the employed labour force living in the municipality works in the urbanized core commuting flow to the urbanized core or b atleast 25 of the employed labour force working in the municipality lives in the urbanized core commuting flow from the urbanized core The variable CMATAB defines the 15 largest CMAs in Canada Selected LFS households that are outside these 15 CMAs or are in non CMA areas are coded as not applicable The variable NEW CMA is similar to CMATAB except that the selected LFS households in Ottawa Gatineau are combined and the smaller CMAs are grouped as separate categories for the NEW variable The NEW CMA variable will also provide a further breakdown at the Census agglomeration A census agglomeration CA is a large urban area known as the urban core together with adjacent urban and rural areas known as urban and rural fringes which have a high degree of social and economic integration with the urban core A CA has an urban core population of at least 10 000 based on the previous census Special Surveys Division 17 Household Internet Use Survey 2002 User Guide 5 0 Survey Methodology The Household Internet Use Survey HIUS was administered in January 2003 to a sub sample of the dwellings in the Labour Force Survey LFS sample and therefore its sample design is closely tied to that of the LFS The LFS design is briefly described in Sections 5 1 to 5 4 Sections 5 5 and 5 6 de
37. browsing the Internet is a commonly used phrase which refers to the activity of a computer user who enters into the global network with a modem to search for and or retrieve information on various topics For the purpose of this survey time spent surfing the net is considered computer communication E mail Electronic Mail is a service allowing the transmission of files or text messages between two or more computer stations Labour Force Survey The Canadian Labour Force Survey LFS was developed following the Second World War to satisfy a need for reliable and timely data on the labour market Information was urgently required on the massive labour market changes involved in the transition from a war time to a peace time economy The survey was designed to provide estimates of employment by industry and occupation at the regional as well as the national level The LFS is the only source of monthly estimates of total employment including the self employed full and part time employment and unemployment It publishes monthly standard labour market indicators such as the unemployment rate the employment rate and the participation rate The LFS is a major source of information on the personal characteristics of the working age population including age sex marital status education attainment and family characteristics 4 3 Labour Force Survey Variable Definitions FAMTYPE This variable identifies households by family type one person ho
38. example suppose that based upon the survey results one estimates that 30 996 of Canadian households had never used the Internet from home work school or any other location in 2002 GUQ02 2 No and this estimate is found to have a standard error of 0 00360 Then the coefficient of variation of the estimate is calculated as 0 00960 X 100 1 2 0 309 There is more information on the calculation of coefficient of variation in Chapter 10 0 Special Surveys Division Household Internet Use Survey 2002 User Guide 9 0 Guidelines for Tabulation Analysis and Release This Chapter of the documentation outlines the guidelines to be adhered to by users tabulating analysing publishing or otherwise releasing any data derived from the survey microdata files With the aid of these guidelines users of microdata should be able to produce the same figures as those produced by Statistics Canada and at the same time will be able to develop currently unpublished figures in a manner consistent with these established guidelines 9 1 Rounding Guidelines In order that estimates for publication or other release derived from these microdata files correspond to those produced by Statistics Canada users are urged to adhere to the following guidelines regarding the rounding of such estimates a Estimates in the main body of a statistical table are to be rounded to the nearest hundred units using the normal rounding technique In normal round
39. for work or were without work had actively looked for work in the past four weeks and were available for work or had a new job to start within four weeks from the reference week and were available for work N Special Surveys Division Work includes any work for pay or profit that is paid work in the context of an employer employee relationship or self employment It also includes unpaid family work which is defined as unpaid work contributing directly to the operation of a farm business or professional practice owned and operated by a related member of the same household Such activities may include keeping books selling products waiting on tables and so on Tasks such as housework or maintenance of the home are not considered unpaid family work Persons are regarded as available for work if they i reported that they could have worked in the reference week if a suitable job had been offered or if the reason they could not take a job was of a temporary nature such as because of own illness or disability personal or family responsibilities because they already have a job to start in the near future or because of vacation prior to 1997 those on vacation were not considered available were full time students seeking part time work who also met condition i above Full time students currently attending school and looking for full time work are not considered to be available for work during the reference week
40. from the microdata file by multiplying the value of the variable of interest by the final weight for each record then summing this quantity over all records of interest For example to obtain an estimate of the total number of orders for products or services by Canadian households in 2002 over the Internet and not paid for directly by credit card multiply the value reported in question CMQ04 number of orders not paid over Internet by the final weight for the record then sum this value over all records with CMQ02 1 a member of the household has placed an order over the Internet where payment was made but not made directly over the Internet using a credit card To obtain a weighted average of the form X the numerator is calculated as for a quantitative estimate and the denominator is 37 Household Internet Use Survey 2002 User Guide 38 calculated as for a categorical estimate For example to estimate the average number of orders for products or services made by Canadian households in 2002 over the Internet and not paid for directly a estimate the total number of orders as described above b estimate the number of households in this category by summing the final weights of all records with CMQ02 1 then C divide estimate a by estimate b to obtain 9 4 Guidelines for Statistical Analysis The Household Internet Use Survey is based upon a complex sample design with strati
41. k k k k k k k KKK KK RK KK KK KK e KK HK KH k k KH k k k k k k k k k k k k k k k k k k k k k KKK KK KK KK KK KK KR e HK KK KH KH ke k HH k k k k k k k k k k k k k k k k k KKK KK KK RK KK KK e KR KK EK RR k k KH HH HH k k k k k k k k k k k k k k k k k 20 0 45 32 26 22 20 5 oo F2 J iO N owore N 4 OY O UJ OH UJ J N UJ N QU O PRUAN 0 KK KKK KK KK RK KK KK KK KK KK KK KR KR KR KR KK KR KR KER KR ERE k k k k ck ck k k k k k k k KKK KK KK KK KR KK KK KK KK KR KK KK KR KK KR KR KER RR EKER k k k k k k k k k k k k k k k 4 4 KKK KK KK KK RK KK KK KK KK KK KR KK KK KR KR KK KK KK KK KK KEK EKER k k k k k k k k k k k k k k k k 3 8 KKK KK KK KK KK KK KK KK KK KK KR KK KR KR RK KK KK KK KR KK KR KK KR KK EK KK k k k k k k k k k k k k k k k k k k k IA 3 9 KKK KK KK KK RK KK KK KK KK KK KK KK KK KK KK KK RR KK KK KK KK KK KK KK RK KK RK RK k k KR KH k k k k k k k k k k k k k k k k k k k k k k k k k 2 6 Ckckckckckokckokckokckckckckckckckckckokckckckckckckckokckokckokckokckckckokckokckokckckckokckckckckckckckokckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckckck k k k k k k k KKK KK KR KR RK RK KK KK KK HK e e e KR KK KK e k KK k e e e e e e KK k e KK KR k k k k KK k k KH k k ke e k k KK k k k k k k k k k k k k k k k k k
42. k k k k k k k k k k k k KKK KK RK KK RK e e KK e e RK e e KK e k e k e k KK k k e e k k e k k k k k k k e k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k kk k k k 25 0 44 3T 255 22 o AA gt OY OY Oy J J OO OO XO O iO O O O P HP P N BUD OO OY UJ Ul O UJ OY O UI H i0 N OO UJ O HM 0 LB 30 0 NNW gd FOR CORRECT USAGE OF THESE TABLES PLEASE REFER MICRODATA DOCUMENTATION w H4 OO O UJ dH UJ OY OY OO HL OY UN OO ds o PEFR gt gt OY OY 2 O OO iO OO OO I PUAN 35 0 NNN S amp S NM dH OY U OY OO 2 O OY d OY O Ul IP O OY O 1o ONA gt gt gt gt gt HS ON y 0 O iO O lOO O OH WW PRUA 40 0 50 0 70 0 39 36 3 28 28 25 1 9 22 20 9 16 95 8 2 14 2 12 8 iX 7 10 8 9 5 9 5 7 4 8 6 3 UO gt gt gt gt gt gt gt y 00 OO iO iO iO OO IE amp O I OY NM dH Oy OP 4 OY IO UJ IO
43. ke ke kk echec ke ke kk echec 222222222222 222222222222 koc ko kk ko koe ke ko kk ke ke ke ke ee 0 5 NOTE FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION Special Surveys Division 59 NUMERATOR OF PERCENTAGE 1000 Ov Ul N HP O Ul i 0 IN H 00 12 1200 4 d IO IO IO NN INN IS 100 125 150 200 250 300 350 NOTE 60 0 1 1 0 2 0 kk kk k k k k 51 0 50 8 kk kk k k k k 36 1 35 9 kk kk k k k k 29 5 29 3 kk kk k k k k 25 5 25 4 K k k k k k k k k k k k k k k k DORT K k k k k k k k k k k k k k k k 20 7 K k k k k k k k k k k k k k k k 19 2 K k k k k k k k k k k k k 18 0 KR KKK KEKE KEKE Ckokckckckckckckckckckckckckckckckck ck ck k k k k e e e k e k k ke k k k k k k k F A k k e KEKE e k e ke k ke k k k k k k k k k EEEE EEE EEEE EEEE EE EE EE EE Ckokckckckckckckckckckckckckckckckck k k k k k Ckokckckckckckckckckckckckckckckckck k Ckokckckckckckckckckckckckckckckckckckckckckckok Ckokckckckckckckckckckckckckckckckck KKK KKK KEKE KEKE KEKE k k k k k k Ckokckckckokckckckckckckckckckckckck k k Ckokckckckckckckckckckckckckckckckck k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckok Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approx
44. the LFS The LFS weighting procedure is briefly described below 11 1 Weighting Procedures for the Labour Force Survey In the LFS the final weight attached to each record is the product of the following factors the basic weight the cluster sub weight the stabilization weight the balancing factor for non response and the province age sex and sub provincial area ratio adjustment factor Each is described below Basic Weight In a probability sample the sample design itself determines weights which must be used to produce unbiased estimates of the population Each record must be weighted by the inverse of the probability of selecting the person to whom the record refers In the example of a 2 simple random sample this probability would be 0 02 for each person and the records must be weighted by 1 0 02 50 Due to the complex LFS design dwellings in different regions will have different basic weights Because all eligible individuals in a dwelling are interviewed directly or by proxy this probability is essentially the same as the probability with which the dwelling is selected Cluster Sub weight The cluster delineation is such that the number of dwellings in the sample increases very slightly with moderate growth in the housing stock Substantial growth can be tolerated in an isolated cluster before the additional sample represents a field collection problem However if growth takes place in more than one cluster in an interviewer
45. the proportion or percentage is based upon a subset of the total population e g those in a particular province or census metropolitan area reference should be made to the proportion or percentage across the top of the table and to the numerator of the proportion or percentage down the left side of the table The intersection of the appropriate row and column gives the coefficient of variation Rule 3 Estimates of Differences Between Aggregates or Percentages The standard error of a difference between two estimates is approximately equal to the square root of the sum of squares of each standard error considered separately That is the standard error of a difference X is o 0 20 where X is estimate 1 X is estimate 2 and and are the coefficients of variation of xi and X respectively The coefficient of variation of d is given by Special Surveys Division Household Internet Use Survey 2002 User Guide d This formula is accurate for the difference between separate and uncorrelated characteristics but is only approximate otherwise Rule 4 Estimates of Ratios In the case where the numerator is a subset of the denominator the ratio should be converted to a percentage and Rule 2 applied This would apply for example to the case where the denominator is the number of households which have never used the Internet and the numerator is the number of households which have never u
46. to the score and distance functions shared the most similar characteristics eg hourly earnings geographic region provided the numerical value was consistent with the HIUS category 2 income for a given household reporting a categorical CTS value was substituted by the income of a household which reported a numerical HIUS value or whose income had been imputed via step 1 and shared the most similar characteristics provided the numerical value was consistent with the CTS category and 3 missing income for a given household was substituted by the income of a household which reported a numerical HIUS value or whose income had been converted to a numerical value via step 1 or 2 and shared the most similar characteristics Special Surveys Division Household Internet Use Survey 2002 User Guide E commerce Imputation There are two types of e commerce variables that were imputed 1 the number of separate orders that the household placed over the Internet and 2 the cost of these orders These variables were collected separately for two different categories orders which were placed and paid for directly over the Internet with a credit card and those placed but not paid for over the Internet The HIUS first collected the total number of orders and the total cost of orders in each category The HIUS then asked for the number and the cost of these reported orders which were placed with Canadian companies In total there we
47. we measured the accessibility of the Internet from any location as well as the frequency and intensity of Internet use of Canadian households from home Special Surveys Division 9 Household Internet Use Survey 2002 User Guide 4 0 Concepts and Definitions This chapter outlines concepts and definitions of interest to the users The concepts and definitions used in the Labour Force Survey LFS are described in Section 4 1 while those specific to the Household Internet Use Survey HIUS are given in Sections 4 2 and 4 3 Users are referred to Chapter 12 0 of this document for a copy of the actual survey forms used 4 1 Labour Force Survey Concepts and Definitions Labour Force Status Designates the status of the respondent vis vis the labour market a member of the non institutional population 15 years of age and over is either employed unemployed or not in the labour force Employment Employed persons are those who during the reference week did any work at all at a job or business or had a job but were not at work due to factors such as own illness or disability personal or family responsibilities vacation labour dispute or other reasons excluding persons on layoff between casual jobs and those with a job to start at a future date Unemployment Unemployed persons are those who during the reference week were on temporary layoff during the reference week with an expectation of recall and were available
48. 10 N UJ OY H9 10 10 OY OO OU iO iO OO CO NU AOAN 9 KKK KK RK RK AER ARA RARA RARA RARA RARA RARA RARA k RARA k k k k k k k k k k k k k k k k k k k 40 0 375 26 24 4 o iO O dH OY iO Ul UI OO HP 0 Oy d UJ UI oo Bu 12 12 1200 O lOO OO IP NU BUNI 4 k RARA k k k k k k k k k k k k k k k k k KKK KK KK KK RK KK KK KK KK KK KK KK e e KR KR e HK e e e RK e HK e k k k HK HK k k e k HH k ke k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k KKK KK KK KK KK RK RK KK KK KK KK KK KK KK KK e HK HK HK e e HK e k HK e e k KK KK KK KK k k k k ke k KK k k k k k k k k k k k k k k k k k k k k k k k 50 0 34 24 20 LT 15 14 135 12 ql 1 0 10 10 to 3 OO iO IO OY IO AN IO NN OO UJ d gt gt Bs HS Hs OY O 1 1 oo 6 k KR KR KK RK KK RK KK RR KK
49. 2 HH eee eee eee eee eee ee 327 3 6 3 4 2 4 4 125 KR KK RR ke ke ke I kc kk ko kk 222222227 2 3p 2 8 252 3 150 KARA RARA RARA RAR KKK RK I KR I KR RR kk RAR RRA RARA ke kk e ke ke RK 2 8 2 6 2 0 Sor 200 ke I ke ke khe kk kk ke kk ko kk kk e ke kk e ke ke ke T 26 250 Sect Peco eee eee ee ee ke ke ke kk eee eee eee EEEE ee eee eee ee ee ee eee 1 5 0 9 300 RAR RARA ek kk ke he kk ehe kk ke ke ke ck khe kk khe ke kk ke kk ck kk kc ko kk ek kk ke ko ko koe ke ke kk ke ke kk ee 0 8 NOTE FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION Special Surveys Division 61 Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for Alberta NUMERATOR OF ESTIMATED PERCENTAGE PERCENTAGE 1000 0 1 1 0 2 0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 40 0 50 0 70 0 90 0 1 71 22 70 9 70 5 69 4 67 6 65 7 63 57 61 7 59 6 57 4 5542 50 4 39 0 22 5 2 50 1 49 9 49 1 47 8 46 4 45 1 43 6 42 1 40 6 39 0 35 6 27 6 15 9 3 ERKENNEN 40 9 40 7 40 1 39 0 37 59 36 8 35 6 34 4 33 2 3159 29 1 22 5 13 0 4 ERROR 35 4 3553 34
50. 22222222222 222222222222 e ke e ke ke ke 4 8 4 6 4 5 4 3 4 1 3 8 350 KR KR IK RK KKK I 22222222229 4 3 4 1 4 0 3 8 3 5 400 222222222222 222222222222 222222229 4 0 3 9 3 27 3 6 323 450 222222222222 HH a 3 6 3 5 3 4 3 1 500 KR KK KR KK 222222222222 222222222222 ke 3 3 2 2 2 9 750 222222222222 22222 222222222222 222222222222 222222222222 ek kk ke kk ke ke ke ke 2 4 1000 BEI RARA RARA RARA RARA ke kk echec kk khe ko kk RARA RARA RARA RARA e ke kk e ke ke ke NOTE FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION Special Surveys Division 70 0 NNNNWW WONnuwUuOo 0 O JU WUW A DV OOOCO HH BUDA I 63 90 0 29 2 20 6 16 8 14 6 13 0 w amp 212 1100 RL LOHR H gt W 000 E IO ww U100 P KHON NUMERATOR OF PERCENTAGE 1000 Ov Ul N HP 10 O Ul i 0 IN H 00 12 1200 4 d IO IO IO NN INN IS
51. 22227 4 3 4 2 4 0 3 7 2 8 6 90 222222222222 222222222222 222222222222 ke RK 4 1 319 3 6 2 8 6 95 KR KK KR KR I ARA RARA RARA ko kk ke ke kk e ke ke RK 3 9 3 8 3 5 2 57 5 100 KARA RARA RARA RARA RARA 222222222222 222222222222 ke RK 2 8 3 3 4 2 6 5 125 22222222222 ee eee 222222222222 222222222222 222222222223 3 0 2 3 3 150 EI I ke ke ke ke khe kk I RARA RARA RARA 2c uu 200 KARA RARA RARA RARA I kk ke kk echec kk khe ke kk ek kk eee ko kk 222222222223 11 250 ere ee eee eee 222222222222 222222222222 222222222222 ek ko kk ke ke ko koe ke ko kk ke ke kk ee v NOTE FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION Special Surveys Division 57 NUMERATOR OF 104 735 60 52 46 42 39 36 34 33 31 30 28 27 26 26 25 24 23 23 22 22 21 201 20 1 94 op UJ i0 0 IB iO O iO 88 O SIND 0 K k k k k k k k k k k k k k k k K k k k k k k k k k k k k k k k k k k k k k k k k k k K k k k k k k k k k k k k K k k k k k k k k k k k k K k k k k k k k k k k k k N 103 13 59 5T 46 42 39 36 34 32 31 29 28 25 3 26 253 25 24 23 23 22 22 21 21 20 18 17 16 15 14 14 13 99 O d iO N Oy O IN 10 10 UJ
52. 3 40 23 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 8 8 8 5 8 3 B 0 TEXT 7 4 7 2 6 5 5 1 2 9 24 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 8 6 8 3 8 1 7 8 7 6 WY E 7 0 6 4 5 0 2 9 25 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 8 4 8 2 7 9 7 7 7 4 Tl 6 9 6 3 4 9 2 8 30 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 7 7 7 5 y 7 0 6 8 6 5 6 3 5 7 4 4 2 6 35 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 7d 6 9 6c 6 5 6 3 6 0 5 8 5 3 4 1 2 4 40 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 6 5 6 3 6 1 5 9 5 6 5 4 5 0 3 8 2 42 45 ck TH TH TH HH HH HH HH HH HH 6514 5 9 5 7 5 5 5 3 5 1 4 7 3 6 25 50 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 5 8 5 6 5 4 5 2 5 1 4 9 4 4 3 4 2 0 55 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 5 5 5 3 5 2 5 0 4 8 4 6 4 2 3 3 9 60 FRR KR KR I KR KR RR ke ke ke ke 5 3 5 0 4 8 4 6 4 4 4 0 3 21 8 65 222222222222 koe kk ee RR ke ke ko e ke ke ke 4 9 4 8 4 6 4 4 4 3 3 9 3 0 atl 70 FRR KR KR KK KR RR KR RR RR RR RR RR RK 4 7 4 6 4 4 4 3 4 1 35247 2 9 gt 75 222222222222 I kk ke ke ke ke 4 6 4 4 4 3 4 1 4 0 3 6 2 8 6 80 RR RARA RARA RARA RARA 222222222222 kk e ke ke ke 4 3 4 1 4 0 3 8 3 5 2727 6 85 RR RR KR KK KK kk ek RR kk ek kk e ke kk e ke ke ke 4 2 4 0 3 9 au 3 4 2 6 5 90 222222222222 222222222227 4 0 3 9 3 8 3 6 3 3 2 6 5 95 HR RK RR KR KR RR kk e ke ke ke 3 9 3 8 3 7 3 5 3 2 2 5 4 100 22222222
53. 3 6 9 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 0 4 0 1 9 8 9 5 9 2 8 8 8 5 Ju 6 0 3 5 20 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 0 1 9 8 9 5 9 2 8 9 8 6 8 3 7 5 5 8 3 4 2 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 9 9 9 6 9 3 9 0 8 7 8 4 8 1 7 4 5 7 3 3 22 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 9 7 9 4 9 1 8 8 8 5 8 2 7 9 Ju 5 6 345 2 23 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 9 4 9 2 8 9 8 6 8 3 8 0 Pel TO 5 5 3I 24 KR KR KKK KR RK KK KK KR KKK KR a 9 2 9 0 8 7 8 4 8 2 7 9 Tu 6 9 5 3 3 4 25 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 9 1 8 8 8 5 8 3 8 0 757 7 4 6 7 5 2 35 0 30 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 8 0 7 8 7 5 7 3 7 0 6 7 6 2 4 8 2 29 35 ee TH TH TH TH HH HH HH HH HH HK ck 7 4 7 2 7220 6 7 6 5 6 2 5 7 4 4 2 6 40 Cece eee ce HH TH TH TH TH HH HH HH HH HH HH a a 7 0 6 7 6 5 6 3 6 1 5 8 5 3 4 1 2 4 45 Be I 22229 6 4 6 2 6 0 5 7 5 5 5 0 3 9 2 2 50 FRR KK RK KK I 222 6 0 5 8 5 6 5 4 5 2 4 8 3 7 25 1 55 222222222222 I e ke kk e ke ke ke 5 8 5 6 5 4 5 2 5 0 4 6 3 5 2 0 60 KARA RARA RARA RARA RARA echec RK RR RR RR RR ke ke ke e ke ke ke 5 3 5 2 5 0 4 8 4 4 3 4 9 65 222222222222 ke kk RARA echec kk RARA RARA RARA RARA RARA RAR 5 1 5 0 4 8 4 6 4 2 3 2 9 70 RARA RARA RARA RARA RARA 222222222222 2227 4 9 4 8 4 6 4 4 4 0 3 51 8 75 222222222222 e ke kk HH ko koe ke ke kk e ke kk e 4 6 4 4 4 3 3 9 3 0 2 80 KENN HH ko kk ke ko kk ke ke ke ke ee 4 5 4 3 4 1 3 8 2 9 85 KH 222222222222 2222222
54. 4 5 ROCK RUE 23 4 2343 23 0 22 3 21 7 21 1 20 4 9 7 9 0 8 2 6 7 249 6 TOES 22 7 22 6 22 2 21 6 21 0 20 4 9577 ub 8 4 7 7 6 1 24D Y GORGE 22 0 2159 21 6 21 0 20 4 9 8 9 2 8 5 7 8 Ja 5 6 2L 8 21 4 21 3 21 0 20 4 9 8 912 8 6 8 0 75 3 6 7 552 1 8 9 SOR KHOE ES 20 8 20 7 20 4 9x9 9 3 957 8 1 hsb 6 9 6 2 4 8 1 5 20 20 3 20 2 9559 9 23 8 8 8 2 TH Henk 6 4 5 8 4 4 X2 21 EXTRAER 9 8 9 27 9 4 8 9 8 4 IES 722 6 7 6 0 5 4 4 1 0 9 22 RE 943 9 3 9 0 8 4 19 7 4 6 8 6 3 Sn Sak 3 8 23 83 9 8 8 8 5 8 0 TED 2 550 6 5 559 543 4 7 3 4 0 4 24 RE RES 8 5 8 4 8 1 Ju 7 2 6 7 6 41 5 6 5 0 4 4 3 2 0 2 25 8 2 8 1 7 8 753 6 8 6 3 5 8 553 4 7 4 1 2 9 0 0 30 SOR REE 6 6 6 5 6 2 5 8 5 4 4 9 4 4 34 9 3 4 2 9 1 8 9 T 35 Du 553 Da 4 6 4 2 3 8 3 4 2 29 2 4 159 0 9 8 4 40 OR CK DE 4 3 4 3 4 1 3 7 33 2 9 2 5 251 1 6 T2 0 2 159 45 3 55 3a 3 2 2 59 255 2 2 1 8 1 4 1 0 0 v5 9 6 7 4 50 2 8 2 8 2 6 252 14 9 S 1 22 0 8 0 4 0 0 9 1 Fak 55 FERRE EE 2 2 242 2 0 d 123 1x0 0 7 0 3 9 9 9b 8 7 6 7 60 REE TES 1 7 22 77 245 1 2 0 9 0 5 0 2 9 9 9 5 951 8 3 6 4 65 2 2 122 1 0 0 7 0 4 0 22 9 8 9 8 9 1 8 8 8 0 65 2 70 BERATER 0 8 0 8 0 6 0 3 0 1 9 8 9 4 9 2 8 8 8 4 7547 6 0 75 ERROR KE 0 5 0 4 0 3 0 0 Qu 9 4 Ou 8 8 Sub 8 2 7 4 5 8 80 ERRKERER OL OS 9 9 9 Nri 9 4 9 1 8 8 8 5 8 2 9 7 2 5 6 85 EA REE EK 9 8 9 8 9 6 9 4 Sul 8 8 8 6 8 3 8 0 ud 7 0 5 4 90 AAR RE 9 6 Ob 9 4 951 8 9 8
55. 4 0 13 5 13 0 11 8 9552 5 49 8 EER E 5 3 4 9 4 4 4 0 3 6 374 12 6 A 11 1 8 6 5 20 9 EER ORE OR EE 4 4 4 0 3 6 3 2 2 8 2 4 11 9 11 4 10 4 SI 4 7 0 RAE OE EHE AER 33 343 259 25 2 2 7 11 3 10 9 9 29 Fat 4 4 1 REE 3 0 2 57 233 1 9 1 6 1 2 10 8 10 3 9 4 7x3 4 2 2 bt EERE AERA ER ETERNA 255 2l 1 8 1 4 Td 047 10 3 959 9 0 7 0 4 0 3 22 40 1427 23 1 0 0 6 0 3 9 9 9 5 8 7 6 7 3 9 4 KK RR RR RR RR RR RR k k RR k k k k k 1 5 0 9 0 6 0 3 9 9 9 5 9 2 8 4 6 5 3 7 5 1 2 0 9 0 5 0 2 9 9 9 6 9 2 8 9 8 1 6 3 3 46 6 0 8 0 5 0 2 9 9 9 6 9 3 8 9 8 6 7 8 6 1 3 5 7 RR RR RR RR k k k k k k k k k k k k k 0 5 02 9 9 9 6 9 3 9 0 8 7 8 3 7 6 5 9 3 4 8 0 2 9 9 9 6 9 3 9 0 8 7 8 4 8 1 7 4 527 9 9 9 9 6 9 4 9 1 8 8 8 5 8 2 7 9 4 2 5 6 32 20 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 9 4 9 1 8 9 8 6 8 3 8 0 LA 7 0 5 4 ST 2 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 9 2 8 9 8 6 8 4 8 1 7 8 7 5 6 8 5 3 ke e K 22 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 9 0 8 7 8 4 8 72 7 9 7 6 Les 6 7 5 2
56. 4 5 23 8 2342 22 5 21 8 21 0 20 3 9 5 7 8 348 7 9 Y EERE EEE 24 3 24 1 2358 231 22 45 21 8 214 20 4 9 7 8 9 7 2 3 4 ES 8 23 6 pe e 23 1 22 55 21 8 21 2 2055 9 8 9 1 8 4 6 8 3 0 7 5 9 KHOE ES 23 0 22 8 22 5 21 59 21 3 20 6 20 0 933 8 6 7 9 6 3 2 6 Tins 20 22 4 2223 21 9 2153 20 7 20 1 9 5 8 8 Su 7 4 549 23 7 1 21 EXTRAER 21 8 21 27 21 4 20 8 20 2 9 6 9 0 8 4 Tod 7 0 5 5 2 0 6 9 22 ERK RE 21 3 21 2 20 9 20 3 9 8 9 32 8 6 7 9 743 6 6 54 2 2527 6 8 23 20 9 20 8 20 4 9459 9 3 8 8 8 2 5 6 9 6 2 4 8 6 6 24 ORO 20 4 20 3 20 0 9 55 8 9 8 4 7 8 6 5 559 4 5 Te 6 5 25 20 0 9549 9 6 9 8 5 8 0 7 4 6 8 6 2 5 6 4 2 1 0 6 4 30 BERKER 18 3 8 2 7 9 7 4 6 9 6 4 5459 5 4 4 8 4 2 3 0 0 1 5 8 35 EROR KO 16 9 6 8 6 6 6 1 Dal 4 7 4 2 3 352 2 0 953 5 4 40 OR 15 8 55 555 BV 4 7 4 2 3 8 323 2 8 2 43 152 827 5 0 45 14 9 4 8 4 6 4 2 3 8 3 4 30 2 5 25 1 6 0 6 8 2 4 7 50 ROCK AA RUE UK ER 4 1 3 9 345 Bb 2 7 253 149 1555 1 0 0 1 7 8 4 5 55 TORRE ON REGERE ERI 3 4 342 2 9 2 5 241 T7 153 04 9 0 5 9 6 7 4 4 3 60 EFERAERRERIERA AER 2 8 2 27 2 73 2 0 1 6 1 2 0 9 0 5 Ol 9 2 Tet 4 1 65 UO 2 3 2 2 1 8 T s 2 22 0 8 0 4 0 9 8 8 6 8 3 9 70 EEA RE CE EE ES 15 9 1 7 1 4 Tor 0 7 0 4 0 1 95 17 953 8 5 6 6 3 8 75 ERA AR RARER ER LB 123 Tsg 0 7 0 4 0 1 OU 9 4 9 0 942 6 4 3 7 80 RR EE To 10 0 7 0 4 0 1 9 7 9 4 9 8 7 7 49 6 2 3
57. 40 6 23 4 46 8 42 8 33 1 19 1 40 6 37 0 28 7 16 6 36 3 33 1 25 7 14 8 10 5 9 6 7 4 4 3 10 1 9 2 7 1 4 1 9 7 8 9 6 9 4 0 9 4 8 6 6 6 3 8 9 1 8 3 6 4 3 7 8 8 8 0 6 2 3 6 8 6 7 8 6 0 3 5 8 3 7 6 5 9 3 4 8 1 7 4 5 7 3 3 7 3 6 6 5 1 3 0 6 6 6 0 4 7 2 7 5 7 5 2 4 1 2 3 5 1 4 7 3 6 2 1 4 7 4 3 3 3 1 9 4 3 4 0 3 1 1 8 4 1 3 7 2 9 1 7 3 8 3 5 2 7 1 6 3 6 3 3 2 6 1 5 3 0 2 7 2 1 1 2 2 6 2 3 1 8 1 0 hien 1 9 1 5 0 9 1 3 0 7 NOTE POUR UTILISER CES TABLEAUX VEUILLEZ R F RER LA DOCUMENTATION RELI E AUX MICRO DONN ES 48 Special Surveys Division Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for Ontario ESTIMATED PERCENTAGE NUMERATOR OF PERCENTAGE 000 0 1 1 0 2 0 5 0 10 0 15 0 30 0 35 0 40 0 50 0 70 0 90 0 1 100 5 100 0 98 0 95 4 92 84 1 1 77 9 71 1 55 1 31 8 2 71 1 70 7 70 4 69 3 67 4 65 5 59 5 57 3 55 1 50 3 38 9 22 5 3 580 578 575 56 6 55 1 53 5 48 6 46 8 45 0 41 0 31 8 18 4 4 50 2 50 0 49 8 49 0 47 7 46 3 42 1 40 5 38 9 35 5 27 5 15 9 5 TUUS 24427 4 5 43 8 2 7 41 5 37 6 6 3 34 8 1 8 4 14 2 60 V vocem 2109 12 7 12 3 12 0 10 9 10 5 10 1 9 2 7 1 4 1 65 Don ctus pg 12 2 11 8 11 5 10 4 10 1 9 7 8 8 6 8 3 9 70 WE ecce 1 9 11 7 11 4 11 1 10 1 9 7 9 3 8 5 6 6 3 8 75 pee re RES 11 3 11 0 10 7 9 7 9 4 9 0 8 2 6 4 3 7 80 md 11 0 10 7 10 4 9 4 9 1 8 7 7 9 6 2 3 6 85 eee 108 10 6 10 3 10 1
58. 47 6 46 1 44 5 42 9 41 2 37 6 4 45 9 45 6 44 9 43 7 42 5 41 2 3949 38 6 32 6 5 41 0 40 8 40 2 39 1 38 0 36 9 357 34 5 33 2 3159 29 2 6 EA RRR EK a5 3753 36 7 35457 34 7 3347 32 6 31 5 30 4 29 2 26 6 7 VOCE KOREA 34 7 34 5 34 0 3351 32 1 31 2 30 2 29 2 28 1 27 0 24 6 8 ERE 32 4 32 3 31 8 30 9 3044 29 2 28 2 27 3 26 3 2543 23 4 9 EXTRA TES 30 6 30 4 30 0 29 2 28 3 27 5 26 6 2547 24 8 23 8 21 7 0 29 0 28 9 28 4 2 se 26 9 26 1 25483 24 4 2355 22 6 20 6 1 SOR Ke 27 7 2055 27 1 26 4 25 6 24 9 24 1 2343 22 4 2125 9 47 2 FAA EE 26 5 26 4 25 9 2513 24 5 23 8 23542 22 3 22 5 20 6 8 8 3 EXERAEES 25 4 2553 24 9 24 3 23 6 22 9 22 21 21 4 20 6 9 8 8 21 4 PRE 24 5 24 4 24 0 23 4 22 7 22 0 2158 20 6 9 9 9 1 7 4 5 2347 23 6 232 22 6 22 0 21 23 20 6 9 9 9 2 8 4 6 8 6 TOES REOR 22 9 22 8 2245 21 9 2123 20 6 20 0 943 8 6 7 9 6 3 Y GORCACKORCEORCR EERE EEK 22 1 21 8 21522 20 6 20 0 9 4 8 7 8 0 73 5 8 8 ARR UR URN 2225 21 2 20 6 20 0 9 4 8 8 8 2 6 8 5 4 9 20 9 20 6 20 1 9 5 8 9 8 3 VES Vl 6 4 5 0 20 PARE EAR RETA AR 20 4 20 1 9 46 9 0 8 4 749 72 3 6 6 6 0 4 6 21 SEER AEA 19 9 9 6 9 1 8 6 8 0 7 4 6 8 6 2 5 6 4 2 22 EERE EAA EE EEE ER 19 5 9 2 8 7 852 7 6 7250 6 4 559 315 9 23 19 0 8 7 8 2 152 6 7 6 1 54b 4 9 3 6 24 OR 18 6 8 3 355 9 7 4 6 8 6 3 5 217 5 2 4
59. 5 4 6 5 6 4 6 2 5 8 553 4 9 4 4 3 9 3 4 249 27 9 1 5 3 5 ROCK AEE 6 0 5 9 536 522 4 8 4 4 E 3 4 2 29 2 4 1 4 8 8 5 1 6 5 5 5 4 53s 4 7 4 3 3 9 3 5 3 30 25b 2 0 1 0 8 5 4 9 Y REFEREER 5 50 4 9 4 7 4 3 3 9 3 2 6 2 2 1 7 0 27 8 3 4 8 8 4 6 4 5 4 3 3 29 35 35 25 2 3 1 8 1 4 0 4 8 0 4 6 9 4 2 4 1 3 9 345 3I 2 8 2 4 129 21 25 1 0 0 1 7 8 4 5 20 EN 3 58 345 342 2 8 2 4 2 0 1 36 152 0 8 9 8 7 36 4 4 21 NUR OK 3 4 332 2 9 225 2 1 247 1 4 0 9 0 5 9 6 7 4 4 3 22 ERASER EE IEE ER SI 2 9 2 6 2 2 1 9 TI 0 7 0 3 9 4 73 4 2 23 2 8 2 6 243 2 0 1746 T2 0 8 0 5 0 0 942 eq 4 1 24 AER 2 6 2 4 2 0 127 l4 T0 0 6 0 2 9 8 9 0 7 0 4 0 25 EI RER 2 53 2 1 1 8 1 5 Tcl 0 8 0 4 0 0 9 6 8 8 6 8 349 30 BERKER ER ES 1 2 2 0 8 0 5 0 2 9 8 955 922 8 8 8 0 6 2 3 6 35 FARRER RE RIERA EK 0 4 0 2 0 0 9 7 9 4 ONE 8 8 84b 7 4 548 3 3 40 ROCK e EUR 9 6 9 43 9 1 8 8 8 5 8 2 749 7 6 7 0 5 4 3 1 45 9 0 8 8 855 853 8 0 7 8 s 72 6 6 St 29 50 ROC had EORR RO 8 6 833 8 1 7 9 7 6 7 4 6 8 6 2 4 8 2 8 55 8 2 8 0 TEST Ub 75 3 750 6 8 6 5 54 9 4 6 25 1 60 RELA EERE SL ok E 7 8 7 6 7 4 32 7 50 6 7 6 5 6 2 5 47 4 4 2 25 65 RRR ERR ERR ERE 73 752 6 9 67 6 5 6 2 6 0 535 4 2 2 4 70 EEE RR RACER TER EUER EE E
60. 6 0 4 0 1 9 8 9 6 9 4 9 2 9 0 KKK KK KK KK KK KK KK KK KK KK KK KK KK KEKE KEKE KER ck k k ck k k k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckckckckckokckokckckckckckckckck k k 20 0 43 30 25 21 Fa to PRUAN UJ OY O UJ OY J OY 2 OY ut 4 KKK KK RK KK KK KK KR KK KK KK KK KR KR KR KK ER KK ERE KEKE k k k k k k k k k k k k k k KKK KK KK KR KK KK KK KK KK KK KK KK KR KR KR KR KR KR RRR RA k k k k k k k k k k 25 0 42 30 24 21 to NAIAYAWADWUWUUWOUWUWODODODOORPKHFRNNWHUD I 6 KKK KK k k k k k k k k k k k k k k k k 3 ARA RARA RARA RARA RARA KARA k k k k k k k k k k k 30 0 40 28 23 20 54 oo gt 1 ON 4 Oy 2 Oy 10 OY UJ UI iO 5 ER KK KK KK k k k k k k k k k k k k k k k E E e KK KK KK KK KK KK KK KK k e HK HK HK e KR RR KR KR KK KR KK KR k k k k KER k k k k k k k k k k k k k k k k k k k 35 0 39 27 22 4 o F2 OY
61. 6 353 25 ENEE 1853 8 0 755 2 0 6 5 6 0 5 4 4 9 4 3 3 0 30 HERRERA EEE REE ES 165 77 6 4 6 0 525 DL 4 6 4 1 3 6 3 0 1 9 35 KR KR KKK KKK 5 2 4 8 4 4 3 9 3 5 340 2 56 2 12 1 0 40 kkkkkkkkkkkkkkkkkkkkkkkk 4 2 3 28 3 4 a0 2 6 2 2 1 48 1 3 0 3 45 KR KKK KKK ck ck KR KKK 3 4 3 0 257 2 53 TaS 1 5 AST 0 6 9 7 50 KKK KKK KK KKK KR KKK 2 7 2 4 2 0 7 1 53 0 9 0 5 0 1 9 2 55 ck ckckckckckckckckckckckckckckckckckckckckokok 2wd 18 1 5 1 1 0 8 0 4 0 0 9 6 8 8 60 ck KR KKK KK 1 6 1 3 1 0 0 6 0 3 0 0 9 6 9 2 8 4 65 Ckckckckckckckckckckckckckckck ck KR KKK 1 1 0 9 0 5 Q2 9 9 9 6 9 2 8 9 8 1 70 kkkkkkkkkkkkkkkkkkkkkkkk 0 7 0 5 022 9 9 9 5 9 2 8 9 8 5 7 8 75 KKK KKK KKK 0 4 OT 9 8 9 5 9 2 8 9 8 6 8 2 Fy 80 kkkkkkkkkkkkkkkkkkkkkkkk 0 0 9 8 9 5 9 2 8 9 8 6 8 3 8 0 7 3 85 9 5 9 2 8 9 8 7 8 4 8 1 7 4 B Sup 90 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 9 2 9 0 8 7 8 4 7 8 7 5 6 9 95 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 9 0 8 77 8 5 8 2 7 9 7 6 733 6 7 100 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 8 7 8 5 8 2 8 0 7 27 7 4 Jl 6 5 125 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 7 8 7 6 7 4 51 6 9 6 6 6 4 5 8 150 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 7 1 6 9 6 7 6 5 6 3 6 1 5 8 5 3 200 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 6 0 5 8 5 6 5 5 5 3 4 6 250 KARA RARA RARA RARA I kk e ke ke ke 5 2 5 1 4 9 4 7 4 5 4 21 300 2
62. 8 KKK KK RK KK RK KK KK KK KK KK KK KK KK KK KR KK KR KK KK KK KR k 3 9 TH KK KK KH KK KR KR KR KK KR KR KK KK KK KK KH KK k k KK k k KH k k k k k k k k k k k k k k k k k k k k k k k k k E E e KK RK e e RK RK KK KK KK KR KK KK e HK HK HK HK k KK k HK e k k KK KR KK k k KR KR KK KR KK k k KK KK k k KK KR KK k k k k k k k k k k k k k k k k k k k k k RK KK RK KK RK KK KK e e e e e e e e e k e e e e e k k k e e k k k k e k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k 56 ESTIMATED PERCENTAGE 25 47 33 27 24 21 to Ul OY OY O OO OO O iO O O O O HP HP HP N N MN UJ O HD OO 2002 Household Internet Use Survey 2002 User Guide for Nova Scotia 99 N H9 OY NM O UJ OOo Ul o HP 10 30 0 a FOR CORRECT USAGE OF THESE TABLES PLEASE REFER MICRODATA DOCUMENTATION i0 O NN io UJ Oo J iO HP gt Oy 10 OY amp O Oy ds 4d Ul 10 IN M OY OY O iO OO OOo NI UO gd O I 35 0 ootNunmnpsi ONU 2
63. 8 3 4 3 0 2 6 2 2 1 7 1 3 0 3 8 0 4 6 25 E RRR 359 345 322 25 4 2 3 2 9 1 5 1 0 0 1 7 8 4 5 30 REE 2 7 2 3 2 0 1 6 123 0 9 9225 051 942 SE 4 1 35 ERE AEE Ts 1 4 lcd 0 8 0 4 QE IH 923 855 6 6 3 8 40 LEAR ARRAS ERIE ER 1 0 0 27 0 4 0 1 9 8 9 4 9 41 8 8 0 6 2 3 6 45 ROKR RR RR RR RR RR RR k k k k RR 0 3 0 9 8 9 5 9 2 8 9 8 6 8 2 75 5 8 3 4 50 ROC ER OK RE HER IO Re 9 8 9 6 9 3 9 0 SN 8 4 8 1 7 8 Tas bb 3 2 55 9 4 9 8 9 8 6 8 3 8 0 y 7 4 6 8 5 3 3 0 60 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 8 7 8 5 8 2 8 0 7 20 7 4 FL 6 5 5 0 2 9 65 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 8 4 8 1 7 9 7 7 7 4 6 8 6 2 4 8 2 8 70 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 8 7 8 7 6 7 4 Tod 6 9 6 6 6 0 4 7 2 7 75 KR KR KKK ARK RAK KR KKK a a 7 8 7 6 7 4 gu 6 9 6 6 6 4 5 8 4 5 2 6 80 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 7 6 7 3 TEIL 6 9 6 7 6 4 6 2 5 6 4 4 2 5 85 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 7 3 7 1 6 9 6 7 6 5 6 2 6 0 5 5 4 2 2 4 90 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 7 6 9 6 7 6 5 6 3 6 1 5 8 4 1 2 4 95 KR KR KKK KKK RR 6 9 6 7 6 5 6 3 6 1 5 9 5 7 542 4 0 2 3 100 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 6 8 6 6 6 4 6 2 6 0 5 7 5 5 5 0 3 9 2 3 125 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 5 9 5 7 5 5 5 3 5 1 4 9 4 5 3 5 25 0 150 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
64. 9 1 8 8 8 4 7 7 6 0 3 4 90 RR 10 5 10 3 10 1 9 8 8 9 8 5 8 2 7 5 5 8 3 4 95 IO KMS El 10 1 9 8 9 5 8 6 8 3 8 0 7 3 5 7 3 3 100 Go EM id 9 8 9 5 9 3 8 4 8 1 7 8 7 1 5 5 3 2 125 PENAL eT TE 8 8 8 5 8 3 7 5 7 3 7 0 6 4 4 9 2 8 150 UNE 8 0 7 8 7 6 6 9 6 6 6 4 5 8 4 5 2 6 200 vr D M 6 6 7 6 6 5 9 5 7 5 5 5 0 3 9 2 2 250 E a NIA 6 0 5 9 5 3 5 1 4 9 4 5 3 5 2 0 300 NU NEC CC M D PR 5 5 5 4 4 9 4 7 4 5 4 1 3 2 1 8 350 BE Ed RR 5 1 5 0 4 5 4 3 4 2 3 8 2 9 1 7 400 ee T 4 8 4 6 4 2 4 1 3 9 3 6 2 8 1 6 450 ee d 4 5 4 4 4 0 3 8 3 7 3 4 2 6 1 5 500 kkkk k ok x ok x x ok ve nx 4 1 3 8 3 6 3 5 3 2 2 5 1 4 750 kkkk k kkkkk KKKKk ke nx 3 1 3 0 2 8 2 6 2 0 1 2 1000 kkkk k kkkkk kk x x kkkk k kkkk k 2 7 2 6 2 5 2 2 1 7 1 0 1500 kkkk k kkkkk kkkkk ok ve XY kkkk k ok XY kkkkk k kkkkk 2 1 0 1 8 1 4 0 8 2000 kkkk k kkkkk kkkkk kkkkk KKKKK kkkkk kkkkk 1 6 1 2 0 7 3000 kkkkk kkkkk kkkkk kkkkk kkkkk ee de ve Xx kkkkk kkkkk 1 0 0 6 4000 ok x x ok x x Lid Ed kkkkk KKKKK kkkkk kkkkk kkkkk kkkkk 0 5 NOTE POUR UTILISER CES TABLEAUX VEUILLEZ R F RER LA DOCUMENTATION RELI E AUX MICRO DONN ES Using Rule 3 the standard error of a difference X is gt where X is estimate 1 Quebec X is estimate 2 Ontario and
65. IERs for the use of Human Resources Development Canada The intersections of the two types of regions form the first level of stratification for the LFS These ER EIER intersections are treated as primary strata and further stratification is carried out within them see Section 5 2 3 Note that a third set of regions census metropolitan areas CMA is also respected by stratification in the current LFS design since each CMA is also an EIER 5 2 2 Types of Areas The primary strata ER EIER intersections are further disaggregated into three types of areas rural urban and remote areas Urban and rural areas are loosely based on the Census definitions of urban and rural with some exceptions to allow for the formation of strata in some areas Urban areas include the largest CMAs down to the smallest villages categorized by the 1991 Census as urban 1 000 people or more while rural areas are made up of areas not designated as urban or remote detailed description of the LFS design is available in the Statistics Canada publication entitled Methodology of the Canadian Labour Force Survey Catalogue no 71 526 XPB Special Surveys Division 19 20 Household Internet Use Survey 2002 User Guide All urban areas are further subdivided into two types those using an apartment list frame and an area frame as well as those using only an area frame Approximately 196 of the LFS population is found in remote areas of provinces
66. KK KK KK KK KK KR KK KK KK KK KK KK KK KK KK KK KR KR k k k k k k k k k k k k k k k k KKK KK KK KK KK KK KK KK KK KK KK KR KK KR KR KR KR KR KR KR KK KR KK KK KK KK KK KK KK KK KK KK KK KK KK KK KR k k k k k k k k k k k k k k k k k 1 7 KKK KK RK KK KR KR KK KR KK KK KK KK KK KK KK KR KK KK KK KK KR KR KK KR KK k RK KK RK RK KK RK KK RK KK KK RK RK KK KK RK KK RK KK KK KK k k k k k k k k k k k k k k k k k k k k k k k k k k k k ck ck k k k k k k k k k k k k k k k k k k k k k k kk k k k FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION 40 0 50 0 70 0 39 35 27 27 25 159 22 20 16 9 qu 13 16 12 14 il 18 10 12 9 11 11 10 m tod 01 l0 IO I 4 0000 OO U10 O0 JA N 0 de ds Hs Hs y OO iO OO OO OO NU 0 AOAN 1 9 dS ds Hs gt HS 1 1 1 1 1 OO OO H9 OY O UJ iO O HP UJ 0 HP d O O O Ul IO HP amp O U N WADA I Special Surveys Division 90 0 ON ON
67. KK KK KR KK KK KK KK KK KK KK KR KK KK KK KK k k k RK KK KK RK KK KK KK KK KK KK KK KK KK KK KK KK KK KR KR KK KK KK KK KK KK k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k k FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION 54 Special Surveys Division 70 0 26 19 15 TL 12 I O0 0 O H DN UJ OY ON O OY OH P UI i0 UI PP o o o oF IO IO lO 00 Yu Y Y Y 0 4 e de Bs ds Hs OY OO ou iO O N d OY N IN d O OY OO 10 O HB UJ Ul iO N XO u Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY Approximate Sampling Variability Tables for NUMERATOR OF 20 0 27 4 19 23 15 8 13 12 2 11 2 10 3 9 7 OL 8 7 ESTIMATED PERCENTAGE 25 26 18 15 137 a E 10 10 92 T Y Prince Edward Island m o NHNORMKROMMONWIU s w 2002 30 0 6 gt OD J J J Oo OO 4 PERCENTAGE 1000 0 1 1 0 2 0 5 0 10 0 15 0 1 Kk kk k k k k k k k k k k k k 30 3 29 8 29 0 28 2 2 FOR RR KA KR k k k RR RIKER k k k k k k 2151 20 5 19 9 3
68. S the next stage of data collection was to administer the Household Internet Use Survey In total 44 129 households were eligible for the supplementary survey the HIUS interview was completed for 31 650 of these households for a response rate of 71 796 More detailed information on response rates is presented in Chapter 8 0 Data Quality Special Surveys Division Household Internet Use Survey 2002 User Guide 7 0 Data Processing The main output of the Household Internet Use Survey HIUS is a clean microdata file This chapter presents a brief summary of the processing steps involved in producing this file 7 1 Data Capture Responses to survey questions are captured directly by the interviewer at the time of the interview using a computerized questionnaire The computerized questionnaire reduces processing time and costs associated with data entry transcription errors and data transmission The response data are encrypted to ensure confidentiality and sent via modem to the appropriate Statistics Canada Regional Office From there they are transmitted over a secure line to Ottawa for further processing Some editing is done directly at the time of the interview Where the information entered is out of range too large or small of expected values or inconsistent with previous entries the interviewer is prompted through message screens on the computer to modify the information However for some questions interviewers have the opt
69. abulation pp 36 9 3 Definitions of Types of Estimates Categorical and Quantitative 36 9 3 1 Categorical Estimates eene nnne 36 9 3 2 Quantitative 36 9 3 3 Tabulation of Categorical Estimates sse 37 9 3 4 Tabulation of Quantitative Estimates 37 9 4 Guidelines for Statistical AnalySsis 38 9 5 Coefficient of Variation Release Guidelines nn 39 9 6 Release Cut off s for the Household Internet Use 41 Approximate Sampling Variability 43 10 1 How to Use the Coefficient of Variation Tables for Categorical Estimates 44 10 1 1 Examples of Using the Coefficient of Variation Tables for Categorical Estimales ss EE ER RE 45 10 2 How to Use the Coefficient of Variation Tables to Obtain Confidence Limits 51 10 2 1 Example of Using the Coefficient of Variation Tables to Obtain Confidence EH ET 52 10 8 Howto Use the Coefficient of Variation Tables to Do a 52 10 3 1 Example of Using the Coefficient of Variation Tables to Do a T test 53 10 4 Coefficients of Variation for Quantitative Estimates pp 53 10 5 Coefficient of Variation Tables 54 UP Vamped 67 11 1 Weighting P
70. as electronic banking are also increasing e 2002 78 of households in the highest income group had a member who used the Internet from home Five years earlier 3396 of households with the highest incomes used the Internet from home Households in the second highest income group exhibited the largest increase in Internet use from home in 2002 rising from 56 of households in 2001 to 62 of households in 2002 e In contrast among the households the lowest income group only 25 had a member who used the Internet from home However this proportion had increased five times from only 596 in 1997 e All provinces showed relatively constant Internet use rates or slight increase in penetration rates from home Newfoundland and Labrador Nova Scotia Ontario and the Western provinces showed slightly increased rates e Only three provinces British Columbia Ontario and Alberta had rates of Internet use from home higher than the national average of 5196 About 58 of households in Ontario and British Columbia had someone who used the Internet regularly from home the highest proportions They were followed by Alberta at 5496 Special Surveys Division 7 Household Internet Use Survey 2002 User Guide In 2002 896 000 households indicated that a member of the household either used the Internet infrequently or had pulled the plug entirely The size of this group has remained relatively unchanged over the past three years Of these
71. at use was from home work school a public library or some other location These users are identified by a household responding yes to the question GUQ02 Has anyone in your household ever used the Internet E mail or world wide web from home work school or any other location and responding yes to the question GUQ03 In a typical month does anyone in this household use the Internet from any location A household that uses the Internet regularly is categorised as a regular or typical user Non regular Ever user A household responding yes to the question GUQ02 Has anyone in your household ever used the Internet E mail or world wide web from home work school or any other location and responding no to the question GUQ03 a typical month does anyone in this household use the Internet from any location In other words a household that has used the Internet but does not use it regularly Drop out A household responding yes to the question GUQ02 Has anyone in your household ever used the Internet E mail or world wide web from home work school or any other location responding no to the question GUQ03 In a typical month does anyone in this household use the Internet from any location and responding yes to the question GUQO6 In the past has any member of this household used the Internet a typical month from any location In other words a household that does not presently use the Internet
72. ata file for the HIUS 9 3 1 Categorical Estimates Categorical estimates are estimates of the number or percentage of the surveyed population possessing certain characteristics or falling into some defined category The number of households which have never used the Internet or the proportion of households for which one or more members have used a computer at home for E mail are examples of such estimates An estimate of the number of households possessing a certain characteristic may also be referred to as an estimate of an aggregate Examples of Categorical Questions Q How often do members of your household use the Internet at home in a typical month R At least 7 times per week At least 4 times per month etc Q What is your best estimate of the total income before deductions of all household members from all sources during the past 12 months Was the total household income R Less than 5 000 Between 5 000 9 999 etc 9 3 2 Quantitative Estimates Quantitative estimates are estimates of totals or of means medians and other measures of central tendency of quantities based upon some or all of the members of the surveyed population They also specifically involve estimates of the form X where X is an estimate of surveyed population quantity total and Y is an estimate of the number of units in the surveyed population contributing to that total quantity Special Surveys Division Special Surveys Division H
73. atification strategies for rural areas were based not only on concentration of population but also on cost efficiency and interviewer constraints In each province remote settlements are sampled proportional to the number of dwellings in the settlement with no further stratification taking place Dwellings are selected using systematic sampling in each of the places sampled 5 2 4 Cluster Delineation and Selection Households in final strata are not selected directly Instead each stratum is divided into clusters and then a sample of clusters is selected within the stratum Dwellings are then sampled from selected clusters Different methods are used to define the clusters depending on the type of stratum Within each urban stratum in the urban area frame a number of geographically contiguous groups of dwellings or clusters are formed based upon 1991 Census counts These clusters are generally a set of one or more city blocks or block faces The selection of a sample of clusters always six or a multiple of six clusters from each of these secondary strata represents the first stage of sampling in most urban areas In some other urban areas census enumeration areas EA are used as clusters In the low density urban strata a three stage design is followed Under this design two towns within a stratum are sampled and then 6 or 24 clusters within each town are sampled For urban apartment strata instead of defining clusters the apart
74. ded to the survey to compensate for those that did not respond In most cases partial non response to the survey occurred when the respondent did not understand or misinterpreted a question refused to answer a question or could not recall the requested information Item non response was very low for the HIUS Most questions had non response rates which were less than 1 0 8 2 4 1 Imputation Imputation is the process that supplies valid values for those variables that have been identified as requiring a change because of invalid information or because of missing information The new values are supplied in such a way as to preserve the underlying structure of the data and to ensure that the resulting records will pass all required edits Imputation was limited in the HIUS to item non response for a few variables Total non respondents were dropped from the data file and accounted for in the weighting process Imputation was performed for the income variable and for some of the e commerce variables 31 32 Household Internet Use Survey 2002 User Guide A nearest neighbour imputation procedure was used to find donors from which data was transferred to the record requiring imputation recipients Donors were selected using a score function Certain characteristics were compared between records requiring imputation and all plausible donors Whenever the recipient and the donor shared the same characteristic a value was added to
75. ds in Ontario which reported that one or more members of their household use a computer at home for E mail in a typical month 2 Referto the coefficient of variation tables for QUEBEC and ONTARIO see above 3 The numerator of this ratio estimate is 1 192 540 The figure closest to it is 1 000 000 The coefficient of variation for this estimate is found by referring to the first non asterisk entry on that row in the QUEBEC table namely 2 796 4 The denominator of this ratio estimate is 2 523 213 The figure closest to it is 3 000 000 The coefficient of variation for this estimate is found by referring to the first non asterisk entry on that row in the ONTARIO table namely 1 096 5 Sothe approximate coefficient of variation of the ratio estimate is given by Rule 4 which is 2 2 an 0 Special Surveys Division Household Internet Use Survey 2002 User Guide where and are the coefficients of variation of X and X respectively That is ag 0 027 0 010 40 000729 0 0001 0 029 ratio of Quebec versus Ontario households which reported that one or more members of their household use a computer at home for E mail in a typical month is 1 192 540 2 523 213 which is 0 47 1 to be rounded according to the rounding guidelines in Section 9 1 The coefficient of variation of this estimate is 2 996 which is releasable with no qualifications 10 2 How to Use the
76. e kk khe ke ke ke kk kk kk kk eee ke ke ke kk ke ke kk ee 0 5 NOTE FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION Special Surveys Division 65 NUMERATOR OF Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for Canada ESTIMATED PERCENTAGE PERCENTAGE 1000 0 1 1 0 2 0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 40 0 50 0 70 0 T 9142 90 8 90 3 88 9 86 5 84 1 81 6 79 0 76 3 735 5 70 7 64 5 50 0 2 64 5 64 2 63 8 62 9 61 2 59 5 BT 55 9 54 0 52 0 50 0 45 6 3553 3 52 6 52 4 52 5133 50 0 48 6 47 1 45 6 44 1 42 5 40 8 37 2 28 8 4 45 6 45 4 45 44 5 43 3 42 0 40 8 39 5 38 2 36 8 3553 32 2 25 0 5 40 8 40 6 40 4 39 8 38 7 37 6 36 5 3553 34 1 3249 31 6 28 8 2253 6 37 2 37 1 36 9 36 3 35 3 34 3 3353 32 2 31 2 30 0 28 8 26 3 20 4 Y 34 5 34 3 34 33 6 327 31 8 30 8 29 9 28 8 27 8 26 7 24 4 8 9 8 3242 32 1 3159 31 4 30 6 29577 28 8 2759 27 0 26 0 25 0 22 8 HERE 9 30 4 30 3 30 29 6 28 8 28 0 27 2 26 3 25 4 24 5 23 6 2145 6 7 0 28 8 28 28 6 28 21 27 4 26 6 25 8 25 0 24 1 2353 2253 20 4 5 8 1 2 155 27 4 27 2 26 8 26 1 25 4 24 6 23 8 23 0 22 2 21 3 9 4 2 26 3 26 2 26 2550 24 3 23 6 22 8 22 0 21 2 20 4 8 6 4 4 3 ERRKERER 25 2 25 50 24 7 24 0 2323 22 6 21 9 21 52 20 4 9 6 7 9 349 4 i 24 3 24 2358 235 1 2255 21 8 2151 20 4 9 7 8 9 722 3
77. e result of actions taken to protect the anonymity of individual survey respondents Users requiring access to information excluded from the microdata files may purchase custom tabulations Estimates generated will be released to the user subject to meeting the guidelines for analysis and release outlined in Chapter 9 0 of this document Province Suppression of Geographic Identifiers The survey master data file includes explicit geographic identifiers for province economic region and census metropolitan area The survey public use microdata files usually do not contain any geographic identifiers below the provincial level However since the HIUS is a household based survey the variable CMATAB is contained on the microdata file Special Surveys Division 27 Household Internet Use Survey 2002 User Guide 8 0 Data Quality 8 1 Response Rates The following table summarizes the response rates to the Labour Force Survey LFS and to the Household Internet Use Survey HIUS conducted in January 2003 Household Household Household Response Rate Response Rate Response Rate for Full LFS for LFS Rotations to the HIUS 2 3 4 5 and 6 January 2003 Newfoundland and Labrador Prince Edward Island Nova Scotia New Brunswick Qu bec Ontario Manitoba Saskatchewan Alberta British Columbia Canada Response rate is the number of LFS responding households as a percentage of the number of eligible households Respon
78. ears of age or older Respondent burden is minimized for the elderly age 70 and over by carrying forward their responses for the initial interview to the subsequent five months in the survey 5 3 Sample Size The sample size of eligible persons in the LFS is determined so as to meet the statistical precision requirements for various labour force characteristics at the provincial and sub provincial level to meet the requirements of federal provincial and municipal governments as well as a host of other data users The monthly LFS sample consists of approximately 60 000 dwellings After excluding dwellings found to be vacant dwellings demolished or converted to non residential uses dwellings containing only ineligible persons dwellings under construction and seasonal dwellings about 54 000 dwellings remain which are occupied by one or more eligible persons From these dwellings LFS information is obtained for approximately 102 000 civilians aged 15 or over 5 4 Sample Rotation The LFS follows a rotating panel sample design in which households remain in the sample for six consecutive months The total sample consists of six representative sub samples or panels and each month a panel is replaced after completing its six month stay in the survey Outgoing households are replaced by households in the same or a similar area This results in a five sixths month to month sample overlap which makes the design efficient for estimating month to
79. ee ee eee ee eee ee eee eee eee eee er 0 8 0 6 7000 Cece Peco ee ee ee eee ee ee ee eee eee eee eee eee 0 6 8000 Cece Peco eee ee ee eee eee ehe kk khe ke eee eee eee 0 6 9000 tree eee eee eee ee ee ke kk ehe kk ke ke ke kk echec kk khe ke ke ee eee eee eee ee eee ke ke ke ke e 10000 cree eee eee eee ee ee eee eee kc RAR khe ke kk a kk 222222222222 ck kk RARA ke ko koe ke ke kk ke ke ke ke ek NOTE FOR CORRECT USAGE OF THESE TABLES PLEASE REFER TO MICRODATA DOCUMENTATION 66 Special Surveys Division 90 0 Ae OY iO 4 UN OO 4 OY lOO I RARAN E 00 OY iO 00 iO OH 4 BAN O HO N WO FT BO HH 650000 YY Y Y ds d Bs HS O1 OY O1 OY 1 NND m wo Household Internet Use Survey 2002 User Guide 11 0 Weighting Since the Household Internet Use Survey HIUS used a sub sample of the Labour Force Survey LFS sample the derivation of weights for the survey records is clearly tied to the weighting procedure used for
80. ember Such proxy reporting which accounts for approximately 6596 of the information collected is used to avoid the high cost and extended time requirements that would be involved in repeat visits or calls necessary to obtain information directly from each respondent If during the course of the six months that a dwelling normally remains in the sample an entire household moves out and is replaced by a new household information is obtained about the new household for the remainder of the six month period At the conclusion of the LFS monthly interviews interviewers introduce the supplementary survey if any to be administered to some or all household members that month 6 2 Supervision and Quality Control All LFS interviewers are under the supervision of a staff of senior interviewers who are responsible for ensuring that interviewers are familiar with the concepts and procedures of the LFS and its many supplementary surveys and also for periodically monitoring their interviewers and reviewing their completed documents The senior interviewers are in turn under the supervision of the LFS program managers located in each of the Statistics Canada regional offices 6 3 Non response to the Labour Force Survey Interviewers are instructed to make all reasonable attempts to obtain LFS interviews with members of eligible households For individuals who at first refuse to participate in the LFS a letter is sent from the Regional Office to t
81. fication multiple stages of selection and unequal probabilities of selection of respondents Using data from such complex surveys presents problems to analysts because the survey design and the selection probabilities affect the estimation and variance calculation procedures that should be used In order for survey estimates and analyses to be free from bias the survey weights must be used While many analysis procedures found in statistical packages allow weights to be used the meaning or definition of the weight in these procedures differ from that which is appropriate in a sample survey framework with the result that while in many cases the estimates produced by the packages are correct the variances that are calculated are poor Approximate variances for simple estimates such as totals proportions and ratios for qualitative variables can be derived using the accompanying Approximate Sampling Variability Tables For other analysis techniques for example linear regression logistic regression and analysis of variance a method exists which can make the variances calculated by the standard packages more meaningful by incorporating the unequal probabilities of selection The method rescales the weights so that there is an average weight of 1 For example suppose that analysis of all Quebec households is required The steps to rescale the weights are as follows 1 select all households from the file who reported PROVINCE 24 Quebec
82. former or infrequent users 402 000 had a computer at home Asked why they no longer used the Internet 3296 said they didn t have a need or interest in using it 2296 said it was too costly and 1296 indicated their computer was too old or broken In 2002 about 3 8 million Canadian households had never used the Internet Most of the households in this group 8596 were either families without children or one person households As well many of these non users earned below average household income with 47 in the lowest income group Special Surveys Division 3 0 Household Internet Use Survey 2002 User Guide Objectives The main objectives of this survey were to Gain a better understanding of how Canadian households use the Internet Measure the demand for Internet services by Canadian households Identify the types of Internet services used at home Determine the reasons why some households are not using the Internet Determine what factors would influence households to start using the Internet Assess the extent to which former typical user households no longer use the Internet on a regular basis Understand the influence of the Internet on purchases of products and services from home Track the purchase of goods and services from home over the Internet for households Determine the extent to which households are concerned about security and privacy issues when engaging the Internet In assessing the use of the Internet
83. he coefficient of variation of the corresponding proportion of households that placed an order for products or services Hence if the coefficient of variation of the proportion is not releasable then the coefficient of variation of the corresponding quantitative estimate will also not be releasable Coefficients of variation of such estimates can be derived as required for a specific estimate using a technique known as pseudo replication This involves dividing the records on the microdata files into subgroups or replicates and determining the variation in the estimate from replicate to replicate Users wishing to derive coefficients of variation for quantitative estimates may contact Statistics Canada for advice on the allocation of records to appropriate replicates and the formulae to be used in these calculations Special Surveys Division 53 NUMERATOR 1000 Ov Ui N HB O Ul iS 0 I0 H VO 00 12 1200 1 d Ulocuococuococuococu ocu oU oou wumu N HpmP OW 100 125 150 NOTE 10 5 Household Internet Use Survey 2002 User Guide HOUSEHOLD INTERNET USE SURVEY 2002 Coefficient of Variation Tables Approximate Sampling Variability Tables for Newfoundland and Labrador 0 1 1 0 2 0 RRR KEKE 48 7 48 4 RRR KR k k RARA k k k k k k 34 3 RRR KR KAR k k k k k k k k k k 28 0 F F k A e KEKE e ke e k k ke k k k k k k Ckokckckckckckckckckckckckckckckc
84. he dwelling address stressing the importance of the survey and the household s cooperation This is followed by a second call or visit from the interviewer For cases in which the timing of the interviewer s call or visit is inconvenient an appointment is arranged to call back at a more convenient time For cases Special Surveys Division 23 24 Household Internet Use Survey 2002 User Guide in which there is no one home numerous call backs are made Under no circumstances are sampled dwellings replaced by other dwellings for reasons of non response Each month after all attempts to obtain interviews have been made a small number of non responding households remain For households non responding to the LFS and for which LFS information was obtained in the previous month this information is brought forward and used as the current month s LFS information No supplementary survey information is collected for these households 6 4 Data Collection Modifications for Household Internet Use Survey Information for the Household Internet Use Survey HIUS was obtained from a knowledgeable household member Upon completion of the Labour Force Survey interview the interviewer introduced the HIUS and proceeded with the interview with the respondent s permission The January 2003 HIUS was administered only as a computer assisted telephone interview 6 5 Non response to the Household Internet Use Survey For households responding to the LF
85. imate Sampling Variability Tables for Manitoba No Ul OOo L2 10 dH HEB OO J J IO d 99 to KKK KKK KKK KR KEKE RK KEKE KEKE k k k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckckckckckckckckckok Ckckckckckckckckckckckckckckckckckckckckckckckck ck ck k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckckckck k k k k k k KKK KKK KKK KER EKER KERR EKER KEKE k k k k k k KKK KK KK KEKE RE KER KEKE KEKE k k k k k k k k k k KKK KKK KKK KR KEK KER KEKE KEKE k k k k k k k k k 10 0 N ND 7 ho 40 iO dH OY O0 OO OY O UI ANN gs 10 AA 0 OO OO oO HD HF N QU UJ amp d Ul 10 7 KH HH k k k k k k k k k k k k k k k KKK KK RK KK KK KK KK KR ER EKER EKER EKER KER k k k k k k k Ckckckckckckckokckokckokckckckokckckckckckokckokckckckckckckckck ck k k k k k k KKK KKK KR KK KR KK KR e HK HK HK k k k k k k k k k k k k k k k k k k ESTIMATED PERCENTAGE 15 0 47 33 27 23 21 to NHAIANAWDAWUWOUWUWODODODOORPKRFRPHFNNWWHKHUYND I BW a Ckokckckckckckckckokckckckokckckckokckckckckckckckckckckckckckckckckckckckckckckckck k k k k k k KKK KK KR KR KK KR KK KK KK KR KK ER e k ke k ke HH k k k k k k k k k k
86. in Internet use among Canadian households has levelled off e In 2002 nearly 8 4 million households or about 69 of the total contained someone who had used the Internet at some time in their life from one location or another either from home work school or a public library e Of these households 7 5 million households had at least one member who used the Internet regularly either from home work school a public library or another location up only 4 from 2001 This rate of growth was far below the gains of 19 in 2001 and 24 in 2000 e than 6 3 million households 51 of all households had at least one member who regularly used the Internet from home an increase of only 400 000 or 796 from 2001 This growth was a fraction of the 2396 growth in 2001 and the 4296 growth in 2000 The HIUS data showed continued growth in Internet connections by cable from home In 2002 an estimated 2 2 million households or 35 reported regular Internet access from home through cable connection This was up from 1 75 million or 26 in 2001 The majority of the remaining households almost 4 million connected using a telephone line e Canadians still use the Internet from home mostly for e mail and general browsing However growing numbers of households rely on the Internet to obtain information on their health to research and make travel arrangements and to obtain information from various levels of government Specialized uses such
87. ing if the first or only digit to be dropped is O to 4 the last digit to be retained is not changed If the first or only digit to be dropped is 5 to 9 the last digit to be retained is raised by one For example in normal rounding to the nearest 100 if the last two digits are between 00 and 49 they are changed to 00 and the preceding digit the hundreds digit is left unchanged If the last digits are between 50 and 99 they are changed to 00 and the preceding digit is incremented by 1 b Marginal sub totals and totals in statistical tables are to be derived from their corresponding unrounded components and then are to be rounded themselves to the nearest 100 units using normal rounding C Averages proportions rates and percentages are to be computed from unrounded components i e numerators and or denominators and then are to be rounded themselves to one decimal using normal rounding In normal rounding to a single digit if the final or only digit to be dropped is 0 to 4 the last digit to be retained is not changed If the first or only digit to be dropped is 5 to 9 the last digit to be retained is increased by 1 d Sums and differences of aggregates or ratios are to be derived from their corresponding unrounded components and then are to be rounded themselves to the nearest 100 units or the nearest one decimal using normal rounding e In instances where due to technical or other limitations a rounding technique other than n
88. ion of bypassing the edits and of skipping questions if the respondent does not know the answer or refuses to answer Therefore the response data are subjected to further edit and imputation processes once they arrive in head office 7 2 Editing The editing and imputation phases of processing involve the identification of logically inconsistent or missing information items and the modification of such conditions Since the true value of each entry on the questionnaire is not known the identification of errors can be done only through recognition of obvious inconsistencies If a value is suspicious but reasonable the erroneous value will find its way into the surveys statistics For that reason emphasis must be placed on quality controls and interviewer training to ensure that errors are both minimal in number and non systematic in nature The first type of error treated was errors in questionnaire flow where questions which did not apply to the respondent and should therefore not have been answered were found to contain answers In this case a computer edit automatically eliminated superfluous data by following the flow of the questionnaire implied by answers to previous and in some cases subsequent questions The second type of error treated involved a lack of information in questions which should have been answered For this type of error a non response or not stated code was assigned to the item 7 3 Coding of Open ended Question
89. kckckckckckckok Ckokckckckckckckckckckckckckckckckck KR KKK KEKE KEKE KEKE KEKE KR KKK KEKE KEKE KEKE KEKE Ckokckckckckckokckckckckckckckckckck q5 00U1U0 JJ 9 Ckokckckckckckckckokckckckckckckckckckckckckckck k k k k k k k Ckckckckckckckckckckckckckckckckckckckckckck k k k k k k k k k Ckokckckckckckckckokckokckckckckckckckckckckckck k k k k k k k k E A e e e KKK e e e KK KR e k k k k k k k k k k k k k k k KKK KK KKK KER e e e e e k ke k k k k k k k k k k k k k k KKK KK KKK KKK KEKE KKK ke k k k k k k k k k k k k k k KKK KK KEKE KEK KER ke e ke HK k k k k k k k k k k k k k k KKK KK KKK KKK KERR KEKE KEKE KER k k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckck 10 0 46 4 32 8 26 8 23 2 20 8 9 0 Tas 6 4 bab 4 7 4 0 3 4 259 2 4 2 0 1 6 La 0 29 0 7 KH HH HH k k k k k k k k KKK KK KK KK KK KK KK KR KR RR EKER ER EKER k k k k k k k k KR KKK KK KK KK KK KR KR KR KR ERE RRR EKER k k k k k k k k KKK KK KK KK KR KK KK KK KR KER KEKE RE KER k k k k k k k k Ckokckckckckckckckckckckckckckokckckckokckckckckckckckckckckckckckck k k k k k KKK KK KK KK KK KK e e KK KER KR KEKE k k k k k k k k k k k k k k k ESTIMATED PERCENTAGE 15 0 45 1 31 9 26 0 22 6 20 2 8 4 vs 6 0 540 4 3 316 3 0 255 2 1 1 6 1 3 0 9 0
90. kckckckckckokckckckokckckckckckck ck ck k k k k k 15 0 36 29 253 22 20 6 o gt AOPA N uU Ho NN WUW BUD 9 k k KKK KK RK KK RK KK KK KK KK EK KK KR EK EKER EKER k k k k k k k k k k k k k Ckokckckckckckckckckckckckckckckckckckokckckckckckokckckckckckckckckckckckckckck ck k k k k k 20 0 49 35 28 24 22 20 5 gt OPRAN 2 i0 O Ui BUD I 9 E AE e A e F e e e e e e e e e e e e e e e e e ke e ke e e k e k k k ke k k k k k k k k k k k k k k k k k k k k k k E E e KK KK KK KK KK KK KK KK KR KK KR KR KR ke k ke k KH k k k k k k k k k k k k k k k k k k k k k KKK KK KK KR KK KK KK e KK HK HK HK e ke k k k ke k HH k k k k k k k k k k k k k k k k k k k k k KKK KK RK KK KK KK KR KK KK KR KR KR KR KR KK KR KR EKER KKK k k ckck k k k k k k k k 5 1 RK KK RK KK KR KK KK KK KK KK KR KR KR KR KK KK KK KK KR KR EKER k k k k k k k k k k ck k k k k k k k k KKK KK KK KK RK KK KK KK KK KK KR KK KR KR KR KK KR KR KK ERK RRR KR KEK k k k k k k k k k k k k k k k 4 6 KKK KK KK KK KK KK KK KK KK KR KR KK KK KK KK KR KR KK KR KK KR KR KEK KR KR ERK AAA KEK k k k k k k k k k k k k k k k 4 0 3
91. kokckckckckckckckckckckckckckckckck Ckokckckckckckckckckckckckckckckckck ck KR KKK KEKE KEKE KEKE KKK k k k k k e ke k ke k k k k k k k k k k Ckokckckckckckokckckckckckckckckckckckckckckckok Ckokckckckckckckckckckckckckckckckck ck ck KR KKK KEKE KEKE KEKE KKK k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckok Ckokckckckckckckckckckckckckckck ck Ckokckckckckckckckckckckckckckckckckckckckckckok WWW Hs OY OO 25 99 7 E A e e e e e e e e e e e e e e Ckokckckckckckckckckckckckckckckckckckckckckckck k k k k k k k k KKK KK KK KKK RK KER EKER KKK KKK KR KKK KKK EKER EKER k k k k k k k k k KKK KKK e e e e e KKK e ke k ke k k k k k k k k k k k k k k KKK KK KKK e e e e KER ke k e k k k k k k k k k k k k k KKK KK KKK e ke e e KEKE k ke k k k k k k k k k k k k k k KKK KK KK KKK e e e e e e k e k k k k k k k k k k k k k Ckokckckckckckckckckckckckckckckckckckckckckckckckck k k k k k k 10 0 No ON WW OU TN Bx OY O O FP OY O OY N OY OY OO UJ Hr U i0 O O HP HP P N N UJ A 9 KEKE KER k k k k k k k k k KKK KK KK KK KK KK e KK RRR k k k ke k k k k k k k k k k k k k k k KKK KK KK KK KK KK KK KK EKER ERE RRR EKER EKER k k k k k Ckckckckckckckokckckckokckckcko
92. ment building is the primary sampling unit Apartment buildings are sampled from the list frame with probability proportional to the number of units in each building Special Surveys Division Household Internet Use Survey 2002 User Guide Within each of the secondary strata in rural areas where necessary further stratification is carried out in order to reflect the differences among a number of socio economic characteristics within each stratum Within each rural stratum Six EAs or two or three groups of EAs are sampled as clusters 5 2 5 Dwelling Selection In all three types of areas urban rural and remote areas selected clusters are first visited by enumerators in the field and a listing of all private dwellings in the cluster is prepared From the listing a sample of dwellings is then selected The sample yield depends on the type of stratum For example in the urban area frame sample yields are either six or eight dwellings depending on the size of the city In the urban apartment frame each cluster yields five dwellings while in the rural areas and EA parts of cities each cluster yields ten dwellings In all clusters dwellings are sampled systematically This represents the final stage of sampling 5 2 6 Person Selection Demographic information is obtained for all persons in a household for whom the selected dwelling is the usual place of residence LFS information is obtained for all civilian household members 15 y
93. month changes The rotation after six months prevents undue respondent burden for households that are selected for the survey Because of the rotation group feature it is possible to readily conduct supplementary surveys using the LFS design but employing less than the full size sample Special Surveys Division 21 Household Internet Use Survey 2002 User Guide 5 5 Modifications to the Labour Force Survey Design for the Household Internet Use Survey The HIUS used five of the six rotation groups in the January 2003 LFS sample For the HIUS the coverage of the LFS was set at the household level However unlike the LFS where information is collected for all eligible household members the HIUS only collected information from one household member who reported about the household 5 6 Sample Size by Province for the Household Internet Use Survey The following table shows the number of households in the LFS sampled rotations that were eligible for the HIUS supplement This table includes households which were non respondents to the LFS Province Sample Size Newfoundland and Labrador 1 589 Prince Edward Island 1 174 Nova Scotia 2 629 New Brunswick 2 393 Quebec 8 412 Ontario 12 717 Manitoba 3 169 Saskatchewan 3 299 Alberta 4 452 British Columbia 4 295 Canada 44 129 Special Surveys Division Household Internet Use Survey 2002 User Guide 6 0 Data Collection Data collection for the Labour Force Su
94. mple in the HIUS computer assisted interviewing CAI application each record must have an accurate stratum cluster and rotation group codes Moreover it requires accurate coding of the time zone field corresponding to province and each of the telephone number fields Such analysis of the sampling frame provides important feedback on the quality of the frame used in the survey At times duplication of records occurs This did not happen in January 2003 8 2 2 Data Collection Interviewer training consisted of reading the HIUS Procedures Manual practicing with the HIUS training cases on the laptop computer and discussing any questions with senior interviewers before the start of the survey A description of the background and objectives of the survey was provided as well as a glossary of terms and a set of questions and answers Interviewers collected HIUS information after the LFS information was collected The collection period ran from the 19 of January to the 2 d of February 2003 Special Surveys Division Special Surveys Division Household Internet Use Survey 2002 User Guide 8 2 3 Data Processing During processing of the data six HIUS records did not match to corresponding records in the LFS Thus they were coded as out of Scope and were dropped from further processing When supplementary survey records do not match to host survey records they must be dropped since a weight cannot be derived for them Conversely
95. of products or services This manual has been produced to facilitate the manipulation of the microdata file of the survey results For more information on the Household Internet Use Survey please visit the Statistics Canada website at www statcan ca and click on the following links 1 Our products and services 2 Free publications 3 Communications 4 Internet use in Canada Questions regarding the survey subject matter or the data set should be directed to Statistics Canada Jonathan Ellison Science Innovation and Electronic Information Division 13th floor Jean Talon Building Tunney s Pasture Ottawa Ontario K1A OT6 Telephone 613 951 5882 E mail jonathan ellison statcan ca Statistics Canada 2000 Estimates 2000 2001 A Report on Plans and Priorities Special Surveys Division 5 Household Internet Use Survey 2002 User Guide 2 0 Background The 2002 Household Internet Use Survey HIUS was conducted for the sixth time in January 2003 by Statistics Canada The survey examined Canadian households access to the Internet at home in the workplace and in a number of other locations The resulting data and analysis sheds light on relationships between usage and location of use household income as well as other demographic factors Additionally the 2002 survey repeats the detailed module on e commerce introduced in 1999 The 2002 survey results showed that e After surging during the late 1990s the growth
96. on in the sample represents besides himself or herself several other persons not in the sample For example in a simple random 296 sample of the population each person in the sample represents 50 persons in the population The same principle also applies to households The weighting phase is a step which calculates for each record what this number is This weight appears on the microdata file and must be used to derive meaningful estimates from the survey For example if the number of households typically using the Internet from home is to be estimated it is done by selecting the records referring to those households in the sample with that characteristic and summing the weights entered on those records Details of the method used to calculate these weights are presented in Chapter 11 0 5 For the HIUS a record was deemed a respondent either complete or partial if a YES response had been obtained to question LUQO2 or to question NUQO1 or failing either of these conditions then a YES or NO response had been given for question NUQOS Otherwise the record was classified as a non respondent 26 Special Surveys Division Household Internet Use Survey 2002 User Guide 7 7 Suppression of Confidential Information It should be noted that the Public Use microdata files described above differ in a number of important respects from the survey master files held by Statistics Canada These differences are th
97. ormal rounding is used resulting in estimates to be published or otherwise released which differ from corresponding estimates published by Statistics Canada users are urged to note the reason for such differences in the publication or release document s f Under no circumstances are unrounded estimates to be published or otherwise released by users Unrounded estimates imply greater precision than actually exists Special Surveys Division 35 36 Household Internet Use Survey 2002 User Guide 9 2 Sample Weighting Guidelines for Tabulation The sample design used for the Household Internet Use Survey HIUS was not self weighting When producing simple estimates including the production of ordinary statistical tables users must apply the proper sampling weight If proper weights are not used the estimates derived from the microdata files cannot be considered to be representative of the survey population and will not correspond to those produced by Statistics Canada Users should also note that some software packages may not allow the generation of estimates that exactly match those available from Statistics Canada because of their treatment of the weight field 9 3 Definitions of Types of Estimates Categorical and Quantitative Before discussing how the HIUS data can be tabulated and analysed it is useful to describe the two main types of point estimates of population characteristics which can be generated from the microd
98. oups by province These are population projections based on the most recent Census data records of births and deaths and estimates of migration In the final step this auxiliary information is used to transform the sub weight into the final weight This is done using a calibration method This method ensures that the final weights it produces sum to the census projections for the auxiliary variables namely totals for various age sex groups economic regions ER census metropolitan areas CMA rotation groups household and economic family size Weights are also adjusted so that estimates of the previous month s industry and labour status estimates derived from the present month s sample sum up to the corresponding estimates from the previous month s sample This is called composite estimation The entire adjustment is applied using the generalized regression technique This final weight is normally not used in the weighting for a supplement to the LFS Instead it is the sub weight which is used as explained in the following paragraphs 11 2 Weighting Procedures for the Household Internet Use Survey The principles behind the calculation of the weights for the HIUS are nearly identical to those for the LFS However this survey is a household weighted survey not a person weighted survey Also further adjustments are made to the LFS sub weights in order to derive a final weight for each record on the HIUS microdata file 1 An adjustment
99. ousehold Internet Use Survey 2002 User Guide An example of a quantitative estimate is the average number of orders for products or services made by Canadian households in 2002 over the Internet and not paid for directly The numerator is an estimate of the total number of orders placed and not paid for directly and its denominator is the number of households reporting making at least one such order Examples of Quantitative Questions Q During the last 12 months how many separate orders for products or services did your household place but did not pay for directly over the Internet R Number of orders Q During the last 12 months what was the estimated total cost in Canadian dollars of the products and services your household ordered but did not pay for directly over the Internet 9 3 3 Tabulation of Categorical Estimates Estimates of the number of people with a certain characteristic can be obtained from the microdata file by summing the final weights of all records possessing the characteristic s of interest Proportions and ratios of the form X Y are obtained by a summing the final weights of records having the characteristic of interest for the numerator X b summing the final weights of records having the characteristic of interest for the denominator then C divide estimate by estimate b 9 3 4 Tabulation of Quantitative Estimates Estimates of quantities can be obtained
100. overnment service or agency a crown corporation or a government owned public establishment such as a school or a hospital Special Surveys Division Household Internet Use Survey 2002 User Guide SELF EMP Data for this variable are collected by the LFS and indicates whether the household has any members aged 18 or older who are self employed SELF EMP includes 1 Working owners of incorporated businesses Working owners of an incorporated business farm or professional practice This group is further subdivided into With paid help and Without paid help 2 Working owners of unincorporated businesses and other self employed Working owners of a business farm or professional practice that is not incorporated and self employed persons who do not have a business for example baby sitters newspaper carriers This group is further subdivided into With paid help and Without paid help 3 Unpaid family workers Persons who work without pay on a farm or in a business or professional practice owned and operated by another family member living in the same dwelling CMATAB A census metropolitan area CMA refers to a labour market area with an urbanized core or continuously built up area having at least 100 000 inhabitants A CMA is generally known by the name of the urban area forming the urbanised core CMA s include 1 municipalities completely or partly inside the urbanized core and 2 other municipalities if a
101. re eight e commerce variables requiring imputation two types of variables number of orders cost for the two categories of orders paid over the Internet versus paid through other means for both Canadian companies and all companies In order to make the imputation process consistent two additional variables were also imputed They were the two introductory questions asking 1 whether the respondent had placed any orders at all over the Internet which they paid for over the Internet with a credit card and 2 whether the respondent had placed any orders at all which they did not pay for over the Internet Each record with at least one of the ten e commerce variables of interest with a missing or invalid value was identified as requiring imputation The imputation process was performed in three stages In the first two stages records were imputed which had one or more of the e commerce variables missing but also had some of the e commerce variables reported The first two stages differed in the pattern of responses The reported e commerce variables along with variables from other sections of the questionnaire were used by way of the score and distance functions to determine the donors The pattern of responses and non response affected the choice of variables included in the score function The last stage of the imputation dealt with those records which had missing values for all of the e commerce variables Information from other sec
102. rect procedure to be used for ratios Province and Region Acceptable Marginal Unacceptable CV 0 0 16 5 CV 16 6 33 3 CV gt 33 3 ue 10000 ETE Manitoba under 2 500 Saskatchewan 7 000 amp over to under 2 000 Special Surveys Division 41 Household Internet Use Survey 2002 User Guide 10 0 Approximate Sampling Variability Tables In order to supply coefficients of variation CV which would be applicable to a wide variety of categorical estimates produced from this microdata file and which could be readily accessed by the user a set of Approximate Sampling Variability Tables has been produced These CV tables allow the user to obtain an approximate coefficient of variation based on the size of the estimate calculated from the survey data The coefficients of variation are derived using the variance formula for simple random sampling and incorporating a factor which reflects the multi stage clustered nature of the sample design This factor known as the design effect was determined by first calculating design effects for a wide range of characteristics and then choosing from among these a conservative value usually the 75 percentile to be used in the CV tables which would then apply to the entire set of characteristics The table below shows the conservative value of the design effects as well as sample sizes and population counts by province which were used to produce the Approximate Sampling Variabili
103. require high school graduation Such education is now considered as post secondary while only primary or secondary would have been recognized prior to 1990 Finally more information is collected on the type of post secondary education 1 Some post secondary 2 Trades certificate or diploma from a vocational or apprenticeship training 3 Non university certificate or diploma from a community college general and vocational college CEGEP school of nursing etc 4 University certificate below bachelors degree Special Surveys Division 15 Household Internet Use Survey 2002 User Guide 5 Bachelors degree and 6 University degree or certificate above bachelors degree HEDUCL Data for this variable are collected by the LFS and indicates the highest level of education attained by the Head of Household in three ranges HEDUCL2 Data for this variable are collected by the LFS and indicates the highest level of education attained by the Head of Household in five ranges HHLD ED Data for this variable are collected by the LFS and indicates the highest level of education attained by any member of the LFS household STUDENTF Data for this variable are collected by the LFS and indicates the presence of a full time college university student in the household STUDENTP Data for this variable are collected by the LFS and indicates the presence of a part time college university student in the household MEMO 5 MEM6 12 MEM13
104. responding households in province household size group The resulting weight HWEIGHT is the final weight which appears on the HIUS microdata file Special Surveys Division 69 Household Internet Use Survey 2002 User Guide 12 0 Questionnaires 12 1 The Labour Force Survey Questionnaire The Labour Force Survey Questionnaire LFS_QuestE pdf is used to collect information on the current and most recent labour market activity of all household members 15 years of age or older It includes questions on hours of work job tenure type of work reason for hours lost or absent job search undertaken availability for work and school attendance 12 2 The Household Internet Use Survey Questionnaire The Household Internet Use Survey questionnaire was used in January 2003 to collect the information for the supplementary survey The file HIUS2002 QuestE pdf contains the English questionnaire Special Surveys Division 71 Household Internet Use Survey 2002 User Guide 13 0 Record Layout with Univariate Frequencies See HIUS2002 CdBk pdf for the record layout with univariate counts Special Surveys Division 73
105. riation of the estimate is 1 2 The finding that there are 3 757 514 households to be rounded according to the rounding guidelines in Section 9 1 which have never used the Internet is publishable with no qualifications 46 Special Surveys Division Household Internet Use Survey 2002 User Guide Example2 Estimates of Proportions or Percentages of Households Possessing a Characteristic Suppose that the user estimates that 470 656 3 757 514 12 5 of households which have never used the Internet GUQO2 2 No reported that they have a computer at home NUQ03 1 Yes How does the user determine the coefficient of variation of this estimate 1 Refer to the coefficient of variation table for CANADA see above 2 Because the estimate is a percentage which is based on a subset of the total population i e households which have never used the Internet it is necessary to use both the percentage 12 596 and the numerator portion of the percentage 470 656 in determining the coefficient of variation 3 The numerator 470 656 does not appear in the left hand column the Numerator of Percentage column so it is necessary to use the figure closet to it namely 450 000 Similarly the percentage estimate does not appear as any of the column headings so it is necessary to use the figure closest to it 15 096 4 The figure at the intersection of the row and column used namely 4 096 is the coefficient of variation to be
106. rocedures for the Labour Force Survey pp 67 11 2 Weighting Procedures for the Household Internet Use 68 DELE 71 12 1 Labour Force Survey Questionnaire uunsnsssessnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnen 71 12 2 Household Internet Use Survey Questionnaire pp 71 Record Layout with Univariate 73 Special Surveys Division Household Internet Use Survey 2002 User Guide 1 0 Introduction The Internet potentially offers individuals institutions small and large businesses all communities and all levels of government with new opportunities for learning interacting transacting business and developing their social and economic potential The Household Internet Use Survey HIUS was conducted for the sixth time in January 2003 for the Science Innovation and Electronic Information Division at Statistics Canada The annual HIUS collects detailed data on the Internet activities of Canadian households It reports on Canadians using the Internet and measures the extent of their use location of use frequency of use and their reasons for using or not using the Internet In 1999 data on electronic commerce from home were provided With 2002 data users can study the growth of e commerce by tracking orders purchases or use of Internet that influence acquisition
107. rvey LFS is carried out each month during the week following the LFS reference week The reference week is normally the week containing the 15 day of the month 6 1 Interviewing for the Labour Force Survey Statistics Canada interviewers are employees hired and trained to carry out the LFS and other household surveys Each month they contact the sampled dwellings to obtain the required labour force information Each interviewer contacts approximately 75 dwellings per month Dwellings new to the sample are usually contacted through a personal visit using the Computer assisted personal interview CAPI The interviewer first obtains socio demographic information for each household member and then obtains labour force information for all members aged 15 and over who are not members of the regular armed forces Provided there is a telephone in the dwelling and permission has been granted subsequent interviews are conducted by telephone This is done out of a centralized Computer assisted telephone interviewing CATI unit where cases are assigned randomly to interviewers As a result approximately 85 of all households are interviewed by telephone In these subsequent monthly interviews the interviewer confirms the socio demographic information collected in the first month and collects the labour force information for the current month In each dwelling information about all household members is usually obtained from one knowledgeable household m
108. s A few data items on the questionnaire were recorded by interviewers in an open ended format These data items were related to such things as other locations where household members typically used the Internet additional reasons for using the Internet and other types of products services ordered over the Internet etc Using automated coding techniques and manual verification many of these open ended responses were recoded back into existing data items on the questionnaire or in some cases where sufficient responses were indicated new derived variable fields were created for the data file Special Surveys Division 25 Household Internet Use Survey 2002 User Guide 7 4 Imputation Imputation is the process that supplies valid values for those variables that have been identified for a change either because of invalid information or because of missing information The new values are supplied in such a way as to preserve the underlying structure of the data and to ensure that the resulting records will pass all required edits In other words the objective is not to reproduce the true microdata values but rather to establish internally consistent data records that yield good aggregate estimates We can distinguish between three types of non response Complete non response is when the respondent does not provide the minimum set of answers These records are dropped and accounted for in the weighting process see Chapter 11 0 Item non re
109. sampling error as reflected by the coefficient of variation as shown in the table below Nonetheless users should be sure to read Chapter 8 0 to be more fully aware of the quality characteristics of these data First the number of records that contribute to the calculation of the estimate should be determined If this number is less than 30 the weighted estimate should be considered to be of unacceptable quality For weighted estimates based on sample sizes of 30 or more users should determine the coefficient of variation of the estimate and follow the guidelines below These quality level guidelines should be applied to weighted rounded estimates All estimates can be considered releasable However those of marginal or unacceptable quality level must be accompanied by a warning to caution subsequent users Special Surveys Division 39 40 Household Internet Use Survey 2002 User Guide Quality Level Guidelines Quality Level of Estimate 1 Acceptable 2 Marginal 3 Unacceptable Estimates have a sample size of 30 or more and low coefficients of variation in the range of 0 096 16 596 No warning is required Estimates have a sample size of 30 or more and high coefficients of variation in the range of 16 696 33 396 Estimates should be flagged with the letter M or some similar identifier They should be accompanied by a warning to caution subsequent users about the high levels of error associated wi
110. scribe how the HIUS departed from the basic LFS design in January 2003 5 1 Population Coverage The LFS is a monthly household survey whose sample of individuals is representative of the civilian non institutionalised population 15 years of age or older in Canada s ten provinces Specifically excluded from the survey s coverage are residents of the Yukon Northwest Territories and Nunavut persons living on Indian Reserves full time members of the Canadian Armed Forces and inmates of institutions These groups together represent an exclusion of approximately 296 of the population aged 15 or over 5 2 Sample Design The LFS has undergone an extensive redesign culminating in the introduction of the new design at the end of 1994 The LFS sample is based upon a stratified multi stage design employing probability sampling at all stages of the design The design principles are the same for each province A diagram summarizing the design stages can be found in the document LFS AppendixA pdf 5 2 1 Primary Stratification Provinces are divided into economic regions ER and employment insurance economic regions EIER ERs are geographic areas of more or less homogeneous economic structure formed on the basis of federal provincial agreements They are relatively stable over time EIERs are also geographic areas and are roughly the same size and number as ERs but they do not share the same definitions Labour force estimates are produced for the E
111. se rate is the number of households responding to the HIUS as a percentage of the number of households responding to or imputed by the LFS in the rotations sampled 8 2 Survey Errors The estimates derived from this survey are based on a sample of households Somewhat different estimates might have been obtained if a complete census had been taken using the same questionnaire interviewers supervisors processing methods etc as those actually used in the survey The difference between the estimates obtained from the sample and those resulting from a complete count taken under similar conditions is called the sampling error of the estimate Errors which are not related to sampling may occur at almost every phase of a survey operation Interviewers may misunderstand instructions respondents may make errors in answering questions the answers may be incorrectly entered on the questionnaire and errors may be introduced in the processing and tabulation of the data These are all examples of non sampling errors Special Surveys Division Number of Respondents in the HIUS 29 30 Household Internet Use Survey 2002 User Guide Over a large number of observations randomly occurring errors will have little effect on estimates derived from the survey However errors occurring systematically will contribute to biases in the survey estimates Considerable time and effort was made to reduce non sampling errors in the survey Quality as
112. sed the Internet and have a computer at home In the case where the numerator is not a subset of the denominator as for example the ratio of the number of households in Quebec which use a computer at home for electronic banking in a typical month as compared to the number of households in Ontario which use a computer at home for electronic banking in a typical month the standard deviation of the ratio of the estimates is approximately equal to the square root of the sum of squares of each coefficient of variation considered separately multiplied by Thatis the standard error of a ratio R 5 is D 2 2 c a where and 0 are the coefficients of variation of X and x respectively The coefficient of variation of R is given by R The formula will tend to overstate the error if X and x are positively correlated and understate the error if X and x are negatively correlated Rule 5 Estimates of Differences of Ratios In this case Rules 3 and 4 are combined The CVs for the two ratios are first determined using Rule 4 and then the CV of their difference is found using Rule 3 10 1 1 Examples of Using the Coefficient of Variation Tables for Categorical Estimates The following examples based on the Household Internet Use Survey 2002 are included to assist users in applying the foregoing rules Example 1 Estimates of Numbers of Households Possessing a Characteristic Aggregates Suppose that a user e
113. sponse is when the respondent does not provide an answer to one question but goes on to the next question These are usually handled using the not stated code or they are imputed Finally partial non response is when the respondent provides the minimum set of questions but does not finish the interview These records can be handled like either complete non response or multiple item non response In the case of the HIUS donor imputation was used to fill in missing data for item and partial non response Further information on the imputation process is given in Chapter 8 0 Data Quality 7 5 Creation of Derived Variables A number of data items on the microdata file have been derived by combining items on the questionnaire in order to facilitate data analysis The variable CMATAB for example is actually a combination of census metropolitan area CMA and census agglomeration CA The CAs have been recoded to 0 while the CMAs remain the same Other examples are the income quartile QUARTILE and quintile QUINTILE variables constructed from income information collected during the interview and from information collected for the Canadian Travel Survey conducted on the same sample An imputation technique was used for records where the variable income was missing see Section 8 2 4 1 for more details on the method used to impute income 7 6 Weighting The principle behind estimation in a probability sample such as the LFS is that each pers
114. stimates that 3 757 514 households have never used the Internet GUQO2 2 No How does the user determine the coefficient of variation of this estimate 1 Refer to the coefficient of variation table for CANADA Special Surveys Division 45 Household Internet Use Survey 2002 User Guide 2 The estimated aggregate 3 757 514 does not appear in the left hand column the Numerator of Percentage column so it is necessary to use the figure closest to it namely 4 000 000 3 The coefficient of variation for an estimated aggregate is found by referring to the first non asterisk entry on that row namely 1 296 HOUSEHOLD INTERNET USE SURVEY 2002 Approximate Sampling Variability Tables for Canada NUMERATOR OF ESTIMATED PERCENTAGE PERCENTAGE 000 01 1 0 20 5 0 10 0 7150 30 0 35 0 40 0 50 0 70 0 90 0 1 912 908 902 86 5 76 3 735 707 645 500 28 2 645 642 638 629 612 595 540 520 500 456 353 20 3 526 524 521 513 500 486 441 425 408 372 288 1 4 456 454 451 445 433 420 382 368 353 322 250 1 5 408 406 404 8 387 376 341 329 216 288 223 1 60 117 117 115 112 109 99 95 91 83 64 65 113 112 110 107 104 95 91 88 80 62 70 108 108 106 103 1041 91 88 84 77 60 75 105 104 103 10 0 97 88 85 82 74 58 80 101 101 99 97 94 85 82 79 72 56 85 98 98 96 294 94 83 80 77 70 54 9004 96 95 94941 8 9 80 78 74 68 53 95 92 293 91 89 86 78 75 72 66 5 100 91 90 89 87 84 76 74 71 64 50 125
115. surance measures were implemented at each step of the data collection and processing cycle to monitor the quality of the data These measures include the use of highly skilled interviewers extensive training of interviewers with respect to the survey procedures and questionnaire observation of interviewers to detect problems of questionnaire design or misunderstanding of instructions procedures to ensure that data capture errors were minimized and coding and edit quality checks to verify the processing logic 8 2 1 The Frame Because the HIUS was a supplement to the LFS the frame used was the LFS frame Any non response to the LFS had an impact on the HIUS frame Because non response to the LFS is quite low usually less than 5 this impact was minimal The quality of the sampling variables in the frame was very high The HIUS sample consisted of five rotation groups from the LFS No records were dropped due to missing rotation group number or any other type of sampling variable Note that the LFS frame excludes about 2 of all households in the ten provinces of Canada Therefore the HIUS frame also excludes the same proportion of households in the same geographical area It is unlikely that this exclusion introduces any significant bias into the survey data All variables in the LFS frame are updated monthly Some variables on the sampling frame may play a critical role with respect to the software application used in the survey For exa
116. t gt OY 3 RK KK KK RK RK KK KR KK KK KK KK KK KR KR KK KK KK KR KR KR KR KR KK KK KK k 70 0 4 RK KK RK KK RK KK KK KK KK KK KK KK KR KK KR KR KK KK KK KK KK KK KR KK KK KR KK KK KK KK KK KK KK KK KK KK KK KK KK KK KR KR KKK k k k k k k k k k k k k k k k k k k UJ UJ UJ UO QJ UJ Ud d gd dH gd Hs F2 og OY J J OO O0 IB N QJ OY IS 10 OO 8 KKK KK KK KK KK KK KR KK KK KK KR KK KK KK KK KR KR KR KK KK KK KR KR KR KK KK KK KK KK KK KK KK KK KK KK KK KK KK KK KK KK KK k k k k k k k k k k k k ck k k ck ck k k k 55 90 0 PRPRPRFPRFENNNNNNNNNNNNNNWWWWW ds Ul 4 Ul OY 0O O HP PH N QU UJ amp Ul O 0 N UJ I NUMERATOR OF PERCENTAGE 1000 Ov Ul N HP 10 O Ul i 0 IN H 00 12 1200 4 d IO IO IO NN INN IS 100 125 150 200 250 300 HOUSEHOLD INTERNET USE SURVEY Approximate Sampling Variability Tables 0 1 1 0 2 0 55 1 54 8 RA 38 9 38 7 31 8 31 6 KR kk kk KEK k kk k k 27 4 RRR KEKE k k k k k k 24 5 Kk kK k k RARA k k k k k k 22 4 Kk kk kk k k k k k k k k k k 20 7 Ckokckckckckckckckckckckckckckckckckckckckckckok C
117. te organizations conducted over computer mediated networks The goods and services are ordered over these networks but the payment and ultimate delivery of the goods or services may be conducted on line or off line Special Surveys Division 13 Household Internet Use Survey 2002 User Guide Internet transaction The sale or purchase of goods or services whether between businesses households individuals governments and other public or private organizations conducted over Internet protocol based networks The goods and Services are ordered over these networks but the payment and ultimate delivery of the goods or services may be conducted on line or off line Digital products A variety of products and services that are delivered directly to the customer s computer Examples of products are music gameware computer Software or services such as courses taken over the Internet Privacy The household s concern that their personal information is accessible to others on the Internet such as people finding out about the websites the household has visited or the fear of others reading your e mail Security The household s concern in conducting financial transactions over the Internet such as purchasing products over the Internet using a credit card or banking over the Internet Window shopping A household that uses the Internet to browse or do comparison shopping but not necessarily buying Surfing Browsing the Internet Surfing or
118. th the estimates Estimates have a sample size of less than 30 or very high coefficients of variation in excess of 33 3 Statistics Canada recommends not to release estimates of unacceptable quality However if the user chooses to do so then estimates should be flagged with the letter U or some similar identifier and the following warning should accompany the estimates Please be warned that these estimates flagged with the letter U do not meet Statistics Canada s quality standards Conclusions based on these data will be unreliable and most likely invalid Special Surveys Division Household Internet Use Survey 2002 User Guide 9 6 Release Cut off s for the Household Internet Use Survey The following table provides an indication of the precision of population estimates as it shows the release cut offs associated with each of the three quality levels presented in the previous section These cut offs are derived from the coefficient of variation CV tables discussed in Chapter 10 0 For example the table shows that the quality of a weighted estimate of 5 000 households possessing a given characteristic in Newfoundland and Labrador is marginal Note that these cut offs apply to estimates of population totals only To estimate ratios users should not use the numerator value nor the denominator in order to find the corresponding quality level Rule 4 in Section 10 1 and Example 4 in Section 10 1 1 explains the cor
119. the score function The potential donors with the highest scores were then compared by the way of a distance function involving other collected variables The record with the smallest distance from the recipient was chosen as the donor Income Imputation The HIUS collected information on household income Respondents were asked for a best numerical estimate of household income and failing that for the best categorical estimate from among 11 possible categories from less than 5 000 to 100 000 If an estimate was not given income was coded as missing Households in the HIUS for which income was coded as missing were linked to the Canadian Travel Survey CTS an LFS supplement also conducted in January 2003 In the CTS respondents were asked for the best estimate of household income among five broad categories from less than 20 000 to 80 000 If an estimate was not given income was coded as missing Overall 5896 of the households reported income as numerical 2196 as an HIUS category and 396 as a CTS category For 1896 of the households no income was available from HIUS or CTS In order to produce income quartiles categorical and missing income values were imputed to have numerical values The imputation process was performed in three steps 1 income for a given household reporting a categorical HIUS value was substituted by the income of a household which reported a numerical HIUS value and according
120. the age in four ranges of the Head of Household HAGE2 Data for this variable are collected by the LFS and indicates the age in six ranges of the Head of Household HSEX Data for this variable are collected by the LFS and indicates the sex of the Head of Household HMARSTAT Data for this variable are collected by the LFS and indicates the marital status reported by the Head of Household The classification of single is reserved for those who have never married otherwise respondents are classified as either widowed or separated divorced HEDUCLEV Data for this variable are collected by the LFS and indicates the highest level of education attained by the Head of Household Beginning January 1990 data on primary and secondary education reflects the highest grade completed This provides a more consistent measure for those who accelerate or fail a grade than did years of school A question on high school graduation has also been added since it is generally believed that persons who have never completed their secondary education have greater difficulty competing in the labour market With the new questions any education that could be counted towards a degree certificate or diploma from an educational institution is taken as post secondary education The change allows more persons into the post secondary education category For example trades programs offered through apprenticeship vocational schools or private trade schools do not always
121. the household In other cases non response is compensated for by proportionally increasing the weights of responding households The weight of each responding record is increased by the ratio of the number of households that should have been interviewed divided by the number that were actually interviewed This adjustment is done separately for non response areas which are defined by employment insurance economic region type of area and rotation group It is based on the assumption that the households that have been interviewed represent the characteristics of those that should have been interviewed within a non response area Labour Force Survey Sub weight The product of the previously described weighting factors is called the LFS sub weight All members of the same sampled dwelling have the same sub weight Sub provincial and Province Age Sex Adjustments The sub weight can be used to derive a valid estimate of any characteristic for which information is collected by the LFS However these estimates will be based on a frame that contains some information that may be several years out of date and therefore not representative of the current population Through the use of more up to date auxiliary information about the target population the sample weights are adjusted to improve both the precision of the estimates and the sample s representation of the current population Independent estimates are available monthly for various age and sex gr
122. tions of the questionnaire was used in the score and distance functions to find the donor Only those respondents who were usual users of the Internet from any location were eligible for the e commerce questions In total 58 of the HIUS respondents were eligible for the e commerce section Of those eligible 5 596 needed at least one of the e commerce fields to be imputed Special Surveys Division 33 34 Household Internet Use Survey 2002 User Guide 8 2 5 Measurement of Sampling Error Since it is an unavoidable fact that estimates from a sample survey are subject to sampling error sound statistical practice calls for researchers to provide users with some indication of the magnitude of this sampling error This section of the documentation outlines the measures of sampling error which Statistics Canada commonly uses and which it urges users producing estimates from this microdata file to use also The basis for measuring the potential size of sampling errors is the standard error of the estimates derived from survey results However because of the large variety of estimates that can be produced from a survey the standard error of an estimate is usually expressed relative to the estimate to which it pertains This resulting measure known as the coefficient of variation CV of an estimate is obtained by dividing the standard error of the estimate by the estimate itself and is expressed as a percentage of the estimate For
123. to account for the use of a five sixths sub sample instead of the full LFS sample 2 An adjustment to account for the additional non response to the supplementary survey i e households that did not respond to the HIUS but did respond to the LFS or for which Special Surveys Division Household Internet Use Survey 2002 User Guide previous month s LFS data was brought forward Statistical techniques are used to group together records that are similar in terms of demographic variables obtained from LFS responses The adjustment is made separately within all non response groups created for each province Adjustments 1 and 2 are taken into account by multiplying the LFS sub weight for each responding Household Internet Use Survey record by sum of LFS sub weights from each household responding to LFS sum of LFS sub weights from each household responding to the HIUS to obtain a non response adjusted HIUS sub weight WEIGHT 1 This adjustment is performed at the non response group level for each province 3 The final adjustment ensured that estimates produced for a province household size group would agree with the known population totals for that province household size group The adjustments were made for household size groupings of 1 2 and 3 or more people Adjustment 3 is calculated by multiplying WEIGHT1 for each HIUS respondent by known population total for province household size group sum of WEIGHT 1 for
124. ty Tables for the Household Internet Use Survey HIUS Province and Region Design Effect Sample Size Population a50 2565382 All coefficients of variation in the Approximate Sampling Variability Tables are approximate and therefore unofficial Estimates of actual variance for specific variables may be obtained from Statistics Canada on a cost recovery basis Since the approximate CV is conservative the use of actual variance estimates may cause the estimate to be switched from one quality level to another For instance a marginal estimate could become acceptable based on the exact CV calculation Remember If the number of observations on which an estimate is based is less than 30 the weighted estimate is most likely unacceptable and Statistics Canada recommends not to release such an estimate regardless of the value of the coefficient of variation Special Surveys Division 43 44 Household Internet Use Survey 2002 User Guide 10 1 How to Use the Coefficient of Variation Tables for Categorical Estimates The following rules should enable the user to determine the approximate coefficients of variation from the Approximate Sampling Variability Tables for estimates of the number proportion or percentage of the surveyed population possessing a certain characteristic and for ratios and differences between such estimates Rule 1 Estimates of Numbers of Households Possessing a Characteristic Aggregates
125. used 5 Sothe approximate coefficient of variation of the estimate is 4 096 The finding that 12 596 of households which have never used the Internet have a computer at home can be published with no qualifications Example 3 Estimates of Differences Between Aggregates or Percentages Suppose that a user estimates that 1 192 540 3 114 447 38 3 of households in Quebec PROVINCE 24 reported that one or more members of their household use a computer at home for E mail in a typical month HUQM 1 1 Yes while 2 523 213 4 539 838 55 6 of households in Ontario PROVINCE 35 reported that one or more members of their household use a computer at home for E mail in a typical month HUQ 1 1 Yes How does the user determine the coefficient of variation of the difference between these two estimates 1 Using the QUEBEC and ONTARIO coefficient of variation tables in the same manner as described in Example 1 gives the CV of the estimate for households in Quebec as 2 7 and the CV of the estimate for households in Ontario as 1 096 Special Surveys Division 47 Household Internet Use Survey 2002 User Guide NUMERATOR OF PERCENTAGE 000 0 1 1 0 1 104 7 104 2 2 74 0 73 7 3 60 4 60 2 4 bs 52 1 5 TM 46 6 60 65 70 75 80 85 90 95 100 125 150 200
126. useholds single family households without unmarried children under the age of 18 single family household with unmarried children under the age of 18 and multi family households Multi family households are identified according to the LFS criteria for economic families a group of two or more persons who live in the same dwelling and who are related by blood marriage including common law or adoption A person living alone or who is related to no one else in the dwelling where he or she lives is classified as an unattached individual Special Surveys Division Household Internet Use Survey 2002 User Guide UNDER18 The LFS collects socio demographic data such as age sex marital status for each household member living in a selected LFS household The UNDER 18 variable is defined by the LFS age variable that is collected for all household members and defines households that have household members that are less than 18 years of age and households that do not have members that are less than 18 years of age HHSIZE Data for this variable are collected by the LFS and indicates the household size by household members of all ages for the survey month HLFSSTAT Designates the status of the Head of Household vis vis the labour market a member of the non institutional population 15 years of age and over is either employed unemployed or not in the labour force HAGE Data for this variable are collected by the LFS and indicates
127. which are less accessible to LFS interviewers than other areas For administrative purposes this portion of the population is sampled separately through the remote area frame Some populations not congregated in places of 25 or more people are excluded from the sampling frame 5 2 3 Secondary Stratification In urban areas with sufficiently large numbers of apartment buildings the strata are subdivided into apartment frames and area frames The apartment list frame is a register maintained for the 18 largest cities across Canada The purpose of this is to ensure better representation of apartment dwellers in the sample as well as to minimize the effect of growth in clusters due to construction of new apartment buildings In the major cities the apartment strata are further stratified into low income strata and regular strata Where it is possible and or necessary the urban area frame is further stratified into regular strata high income strata and low population density strata Most urban areas fall into the regular urban strata which in fact cover the majority of Canada s population High income strata are found in major urban areas while low density urban strata consist of small towns that are geographically scattered In rural areas the population density can vary greatly from relatively high population density areas to low population density areas resulting in the formation of strata that reflect these variations The different str
128. y Concepts and Definitions All households Household count 12 166 352 The HIUS is a sample survey weighted to the entire count of households in Canada The yearly figure for the number of households in Canada is projected from the Census of population From 1999 to 2002 the HIUS used a population projection based on the 1996 Census of population The 1997 and 19968 files have been re weighted based on the 1996 Census of population Household Any person or group of persons living in a dwelling A household may consist of any combination of one person living alone one or more families a group of people who are not related but who share the same dwelling Head of household For the purposes of this report the head of a household is determined as follows in families consisting of married couples with or without children the husband is considered the head in lone parent families with unmarried children the parent is the head in lone parent families with married children the member who is mainly responsible for the maintenance of the family becomes the head in families where relationships are other than husband wife or parent child normally the eldest in the family is considered the head and in one person households the individual is the head Special Surveys Division Household Internet Use Survey 2002 User Guide Regular user Households with at least one person that uses the Internet in a typical month regardless of whether th

Microdata User Guide HOUSEHOLD INTERNET USE SURVEY

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