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Household Internet Use Survey

1. 2 4 1 4 200 KKK KKK KKK KKK KKK RK RIK ek ek RK KKK KK RK RK ke ek ek RK koe eek RK ek eek deck KKK RK k e de de HK de k e de de KR RRR RRR KEK EAE 2 1 1 2 250 1 1 Note For correct usage of these tables please refer to the microdata documentation 54 Statistics Canada Catalogue no 56M0002GIE Approximate Sampling Variability Tables Quebec Numerator of Percentage 000 0 1 1 05 2 05 5 05 10 05 15 05 20 05 25 05 30 05 1 115 1 114 6 114 0 112 2 109 2 106 2 103 0 99 7 96 3 2 81 4 81 0 80 6 79 4 77 2 75 1 72 8 70 5 68 1 3 66 4 66 1 65 8 64 8 63 1 61 3 59 5 57 6 55 6 4 RRKKKKKEK 57 3 57 0 56 1 54 6 53 1 51 5 49 9 48 2 5 RRKKKKKE 51 2 51 0 50 2 48 9 47 5 46 1 44 6 43 1 6 e de e de ke deke e 46 8 46 5 45 8 44 6 43 3 42 0 40 7 39 3 7 RRKKKKKE 43 3 43 1 42 4 41 3 40 1 38 9 37 7 36 4 8 Kekeke ekekeke k 40 5 40 3 39 7 38 6 37 5 36 4 35 3 34 1 9 RRKKKKKE 38 2 38 0 37 4 36 4 35 4 34 3 33 2 32 1 10 36 2 36 0 35 5 34 5 33 6 32 6 31 5 30 5 11 RRKKKKKE 34 5 34 4 33 8 32 9 32 0 31 1 30 1 29 0 12 33 1 32 9 32 4 31 5 30 6 29 7 28 8 27 8 13 Fkk keke kk
2. 2 0 1 5 0 9 450 KKK KK KKK KK KKK KKK RIK KKK KK RK RIK RK RIKKI KK RK KKK IK e de de k e KKK de de ke e de de k kede d KKK k kk k kkk k 1 9 1 5 0 8 500 KKK KKK KK KKK KKK KK RK RIK IKK RRR IK KK RK RK RIK ek RK RIK KKK KAKA KKK RRR de de k e RK KK de de k e de de k e de de KK RRR RARE KEK EAE 1 4 0 8 750 KR KKK KKK KK RK KKK KR KKK ek ek RK ke ke ke ek RRR KKK KK RK ke ke ek RK KKK IK RRR KKK KKK KKK KR k ede de k kede d k de d d k dek k k kk k k kk k kkk k 0 7 Note For correct usage of these tables please refer to the microdata documentation Statistics Canada Catalogue no 56M0002GIE 61 Household Internet Use Survey 2003 User Guide Numerator of Percentage 000 WDIHUEPWNHE Note For correct usage of these tables Household Internet Use Survey 2003 Approximate Sampling Variability Tables Prairie Provinces 0 1 1 05 2 05 81 5 81 1 80 7 57 6 57 4 57 1 46 8 46 6 40 6 40 3 KKK KKK 36 3 36 1 KKK KKK 33 1 32 9 30 7 30 5 28 7 28 5 KKK KKK 27 0 26 9 25 6 25 5 kkkkkkk 24 5 24 3 23 4 23 3 kkk kk kk k 22 5 22 4 RRR KKK 21 7 21 6 kkkkkkkk 20 9 20 8 deck eee 20 3 20 2 KKK KKK 19 7 19 6 19 1 19 0 18 6 18 5 deck eee 18 1 18 0 RRR K
3. 9 5 9 2 9 0 8 7 8 4 8 1 7 8 7 1 5 5 3 2 95 9 3 9 0 8 7 8 4 8 2 7 9 7 6 6 9 5 3 3 1 100 KKK KK KKK KKK KKK KEK He e de He He e de He He ke ke ke 9 0 8 8 8 5 8 2 8 0 7 7 7 4 6 7 5 2 3 0 125 7 8 7 6 7 4 7 1 6 9 6 6 6 0 4 7 2 7 150 He He e He He e He He e He e e He e ke He ke ke e ke He e ke ke ke ke k k k k k k k k k kk kk k 7 2 6 9 6 7 6 5 6 3 6 0 5 5 4 3 2 5 200 6 0 5 8 5 6 5 4 5 2 4 8 3 7 2 1 250 5 2 5 0 4 8 4 7 4 3 3 3 1 9 300 4 6 4 4 4 3 3 9 3 0 1 7 350 KK KKK KKK KK KKK IK KK RK KKK IK ek ek RAK KK RK de de de He RK RK RAK KK de He ke ede He k keke k kkk k 4 3 4 1 3 9 3 6 2 8 1 6 400 3 8 3 7 3 4 2 6 1 5 450
4. 13 1 12 7 12 3 11 9 11 5 11 1 10 7 9 7 7 5 21 KR KKK ke ke ek khe ek ke ke e ke ke ek ke e e koe e ee 12 7 12 4 12 0 11 6 11 2 10 8 10 4 9 5 7 4 22 c e ke e e e e e e e ee e e eee e ee ke e ee ee ee e e ee 12 4 12 1 11 7 11 4 11 0 10 6 10 2 9 3 7 2 23 12 2 11 8 11 5 11 1 10 7 10 3 9 9 9 1 7 0 24 11 9 11 6 11 2 10 9 10 5 10 1 9 7 8 9 6 9 25 ck e e ke ke ke ek ke he ek khe ek khe ec ke koe ke koe e ee 11 7 11 3 11 0 10 7 10 3 9 9 9 5 8 7 6 7 30 ce kc e ke e ke e ke eee ke e ke e ke e ke e ke ke e ke e ke e ke ee 10 7 10 4 10 0 9 7 9 4 9 1 8 7 7 9 6 2 35 9 9 9 6 9 3 9 0 8 7 8 4 8 1 7 4 5 7 40 9 0 8 7 8 4 8 1 7 8 7 5 6 9 5 3 45 8 5 8 2 7 9 7 7 7 4 7 1 6 5 5 0 50 ke e he e e he ee e e e ke ke e ke ke He e ke ke ce k ke e c k k k k e k k k k e 8 0 7 8 7 5 7 3 7 0 6 7 6 2 4 8 55 7 6 7 4 7 2 6 9 6 7 6 4 5 9 4 5 60 KK KKK KKK KK ek koe eek ek ek eek ek RK RRR KKK RRR KKK KEK ERK 7 1 6 9 6 6 6 4 6 2 5 6 4 4 65 FE e e e e de e He He de e He He de e He
5. 12 5 12 2 11 9 11 5 11 1 10 8 85 12 2 11 8 11 5 11 2 10 8 10 4 90 11 8 11 5 11 2 10 9 10 5 10 2 95 KKKKKKK KKK KKK KKK KK KKK KKK 11 5 11 2 10 9 10 6 10 2 9 9 100 oR RR RR eee eee e ke 11 2 10 9 10 6 10 3 10 0 9 6 125 10 0 9 8 9 5 9 2 8 9 8 6 150 Kk ke eee eee eee eee eee eee ee ke 9 2 8 9 8 7 8 4 8 1 7 9 200 7 7 7 5 7 3 Y 6 8 250 6 9 6 7 6 5 6 3 6 1 300 6 3 6 1 5 9 5 8 5 6 350 5 7 5 5 5 3 5 1 400 Kk kc ke kk ke ke ke kk ke kk kk kk kk kk ke KKK KKK KKK KK KKK KKK 5 3 5 1 5 0 4 8 450 KKK ke e ke e e e e e e eee eee ee ee ee eee ee ee eee e ee ee ke 5 0 4 9 4 7 4 5 500 Kk ke ke e ke ke e e ke e e e e ee e eee ee ee He e e eee eee eee ee ee k k ee k 4 6 4 5 4 3 750 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 3 6 3 5 1 000 ke e ee e e eee e eee e eee e eee e e e eee e oe e eee e ee ke e eee e ee e e ec ke e ce ce ke e ce e e ek v v n X 1 500 e ke e eee e ee ke e eee e eee e ee e e eee e ee e eee e e e oe e e eee e eee
6. 3 5 3 2 2 5 1 4 500 3 0 2 3 1 3 50 1 9 1 1 1 000 KR KKK KKK KK KKK KKK RK ke ke ek ek ke ke ke ek RK KKK ek ek ke ke ke ek RK KKK IK RK RIK IKK RK KKK KKK d k kede de k kede d k de de d kde de k k k k k kkk kk kk k 1 0 Note For correct usage of these tables please refer to the microdata documentation Statistics Canada Catalogue no 56M0002GIE 59 Household Internet Use Survey 2003 User Guide Approximate Sampling Variability Tables British Columbia Numerator of Household Internet Use Survey Estimated Percentage 2003 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 1 109 3 108 8 108 3 106 6 103 7 100 8 97 8 94 7 91 5 88 2 84 7 77 3 2 Me koe eee 76 9 76 6 75 4 73 4 71 3 69 2 67 0 64 7 62 3 59 9 54 7 3 62 8 62 5 61 5 59 9 58 2 56 5 54 7 52 8 50 9 48 9 44 6 4 EERE ERS 54 4 54 1 53 3 51 9 50 4 48 9 47 4 45 7 44 1 42 4 38 7 5 RARER RE 48 7 48 4 47 7 46 4 45 1 43 7 42 4 40 9 39 4 37 9 34 6 6 44 4 44 2 43 5 42 4 41 2 39 9 38 7 37 4 36 0 34 6 31 6 7 41 1 40 9 40 3 39 2 38 1 37 0
7. 5 4 5 3 5 1 5 0 4 8 4 6 4 5 4 3 3 9 3 0 400 5 0 4 9 4 8 4 6 4 5 4 3 4 2 4 0 3 7 2 8 450 KKKKKKK KKK KKK KKK KK KKK KKK 4 8 4 6 4 5 4 4 4 2 4 1 3 9 3 8 3 5 2 7 500 He e ke e e eee RRR RRR RK RR k ke ke 4 5 4 4 4 3 4 1 4 0 3 9 3 7 3 6 3 3 2 5 750 3 6 3 5 3 4 3 3 3 2 3 0 2 9 2 7 2 1 1 000 KKKKK KKK ke kk kk kk kk KKK KKK KKK KKK KKK 3 1 3 0 2 9 2 8 2 7 2 6 2 5 2 3 1 8 1 500 FR e e ke e e e e e KKK KR KR RRR KK EK 2 5 2 4 2 3 2 2 2 2 2 1 1 9 1 5 2 000 2 1 2 0 1 9 1 9 1 8 1 6 1 3 3 000 1 6 1 6 1 5 1 5 1 3 1 0 4 000 KKK kk ke ke ke ke kk ke ke kk ke ke kk ke kk ke e kk kk kk kk kk kk kk kk kk kk kk kk kk kk kk e e kk KKK KK ke 1 3 1 3 1 2 0 9 5 000 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 1 0 0 8 6 000 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 0 9 0 7 7 000 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 0 7 8 000 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 0 6 9 000 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
8. MS O Ban Catalogue no 56M0002GIE Household Internet Use Survey Microdata User s Guide 2003 Eel mx eun Canada How to obtain more information Specific inquiries about this product and related statistics or services should be directed to Science Innovation and Electronic Information Division Statistics Canada Ottawa Ontario K1A OT6 telephone 1 800 263 1136 For information on the wide range of data available from Statistics Canada you can contact us by calling one of our toll free numbers You can also contact us by e mail or by visiting our Web site National inquiries line 1 800 263 1136 National telecommunications device for the hearing impaired 1 800 363 7629 Depository Services Program inquiries 1 800 700 1033 Fax line for Depository Services Program 1 800 889 9734 E mail inquiries infostats statcan ca Web site www statcan ca Ordering and subscription information This product Catalogue no 56M0002GIE is available on Internet free Users can obtain single issues at http www statcan ca cgi bin downpub freepub cgi Standards of service to the public Statistics Canada is committed to serving its clients in a prompt reliable and courteous manner and in the official language of their choice To this end the Agency has developed standards of service which its employees observe in serving its clients To obtain a copy of these service standards please contact Statistics Canada toll fr
9. 12 1 11 8 11 5 11 1 10 7 10 3 9 9 9 1 7 0 25 KKK e ke ke he e kk he ek ke ke ek ke ke e ke koe ke ke e ke ee 11 9 11 6 11 2 10 9 10 5 10 1 9 7 8 9 6 9 30 10 9 10 6 10 2 9 9 9 6 9 2 8 9 8 1 6 3 35 10 1 9 8 9 5 9 2 8 9 8 6 8 2 7 5 5 8 40 9 4 9 1 8 9 8 6 8 3 8 0 7 7 7 0 5 4 45 ke e he eee he e ee He e ke He e e hee He e he ke ke e ke ke ke e e ke k e e e k k k k 8 6 8 4 8 1 7 8 7 5 7 2 6 6 5 1 50 He He e He He e He He e He He e He e He He e He ke ke He e ke ke ke ke k ke k k k k k k k kk kk k 8 2 7 9 7 7 7 4 7 2 6 9 6 3 4 9 55 7 8 7 6 7 3 6 8 6 6 6 0 4 6 60 e He e e He e He He e He e He He e He He e He e e He e ke He e ke ke ke ke ke ke ke k ke k k k k k k 7 5 7 2 7 0 6 8 6 5 6 3 5 7 4 4 65 KKK KKK ek ek RRR ke ek RK He de e He He KEK de e He KK e He He e e He ke ee e ke kek ke 7 0 6 7 6 5 6 3 6 0 5 5 4 3 70 6 7 6 5 6 3 6 0 5 8 5 3 4 1 75 KKK KKK KKK KK He He e ke ke RK eek deck RK RRR KK e He He e de He He EKER ERK 6 5 6 3 6 1 5 8 5 6 5 1 4 0 80 KKK KKK KKK KK KKK KK RRR KKK RK RK RAK KEKE KER de de ke KEK EKER 6 3 6 1 5 9 5 7 5
10. Canada 89 3 667 The LFS response rate is the number of LFS responding households as a percentage of the number of LFS eligible households The HIUS response 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 26 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide Over a large number of observations randomly occurring errors will have little effect on estimates derived from the survey However errors occurring sys
11. e e ke ke e eee e eee e ee ke e ce e ke ce e e ee e e 2 000 ke e eee e eee e eee e ee e e eee e eee e eee e eee e ee oe eee e eee e eee e eee e ee oe e eee e eee e eee e eee e ee ke e cese ke e ce e ke e e e e e v x Note For correct usage of these tables please refer to the microdata documentation Household Internet Use Survey 2003 User Guide Household Internet Use Survey Estimated Percentage Statistics Canada Catalogue no 56M0002GIE 2003 35 0 PRPRPRPRPRPRPRPRPRPRPRPRFPFRPRPENNNNNNNNNNNNWWWW i di Ul O X0 4 IND iS OY O s X0 OY OY U U UI O0 H i H UI O Ui H 00 J N XO Oy OR O0 UJ OO U 10 UI INN O 00 J O0 O i o OO 10 Ul i OY OY OO N WEP UI UI UI Oy J OO ww 40 0 HHHHHHHHHBHHH HH H HH NN NN N NN N NN N OU U U Q BUD O O O H H N N U i5 UI O J O OO o 0 0 O IP IB IN U QU iS UI OY O 0 IB UO OY Oo S ID 0 9 0 QONMUOH POUUOIO0N5 JOU JIPBUOOUHPIPUONOOCUIUO0U000 0N U I5 0UHN sS aS uS uS UI UI Oy OO OO Www 50 0 81 57 47 40 36 33 30 28 27 25 24 23 22 21 21 20 19 19 18 18 17 17 17 16 16 14 13 12 12 11 11 10 10 9 tRPNDODODOEFBAKRFPOADWHEADOAFBIKFUOUrRPWODWWHOKRDANAINABPOWHAUUAKFAWAANBAOCH HL 9 9 8 8 8 8 7 6 5 5 4 4 4 3 3 3
12. 2 2 70 0 HHBPHHHHHHHHBHHHBHHHBHBHHBNNMNNMNULOU S50 O O H N NM QU U QU S iS iS UL UL OY O 1 O0 0 0 IB IB U UT OO ID OY AW 000004 00Ui l otU1O00 IU U10O IP U1 io O 40U1 OY tO lO i O0 IP U1 t 000 0 C0 t 00o 0 00 1 NUA i 2 H IN ND N OQ U UJ U 4 d UI UI O O OY O J J J OO CO OO to 90 0 N N ray m oo BPR Ww 5 N N m m m o o OOnnHH H H H N N N U U U U U U i iis iiS uS uS UIS ULUL UL OY OY 1 yyy O0 00 O0 OO 10 tO 0 55 to NM UJ OY J OO H2 QU OY O UJ Oy J OO OPN iS UI Oy i Oy OO PH Oy OO HP 4 HP UI O UI H o 0 0 0 N O Household Internet Use Survey 2003 User Guide Approximate Sampling Variability Tables Ontario Numerator of Household Internet Use Survey Estimated Percentage 2003 25 0 96 68 55 48 43 39 36 34 32 30 29 27 26 25 24 24 23 22 22 21 21 20 20 19 19 17 16 15 14 13 13 12 12 11 11 10 10 10 o 3 WRK UI UI OY Oy U 16000009 OH 0 i00 11 l9 U10OO IO UO U1O 24 QU OY 0 2 2OY P2 00 IP 10 00 0 IB C000 OH 1 30 0 PRPRPRPRPRPRPRPRPRPRPRPRPRPERENNNNNNNNNNNNWWWW BE BUD 9 CORPFRFPNNWWHEUAITDOWUWOORrNNWHEBKBUDHDAWAOKFPWUAH AW OY UO PNEPATIOCBODAWWADKHFBRANHAOANWWAAWAOTOUWOBROBROKDWRFWOWOUORFURPOWFAT1I00W m uS iS i
13. 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 require high school graduation Such education is now consi
14. The different stratification 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
15. Variation Tables for Categorical Estimates The following examples based on the January 2003 Household Internet Use Survey 2002 reference year 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 estimates 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 42 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 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 Canada Estimated Percentage Numerator of Percentage 9000 0 1 1 0 2 0 5 0 10 0 150 30 0 35 0 40 0 50 0 70 0 90 0 1 91 2 0 8 90 3 88 9 86 84 1 76 73 70 7 64 5 50 0 28 8 2 64 5 64 2 63 8 62 9 61 2 59 5 54 0 52 0 50 0 45 6 35 3 20 4 3 52 6 52 4 52 1 51 3 50 0 48 6 44 1 42 5 40 8 37 2 28 8 16 7 4 45 6 45 4 45 1 44 5 43 3 42 0 38 2 36 8 35 3 32 2 25 0 14 4 5 40 8 0 6 40 4 39 8 38 7 37 6
16. three years but was slightly reduced this year e In 2003 about 3 6 million Canadian households had never used the Internet Most of the households in this group 8796 were either families without children or one person households As well many of these non users earned below average household income with 49 of non users in the lowest group 8 Statistics Canada Catalogue no 56M0002GIE 3 0 Household Internet Use Survey 2003 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 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 Statistic
17. with stratification 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 may 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 PROVINC
18. 0 14 6 11 3 30 18 8 18 7 18 4 17 9 17 4 16 9 16 4 15 8 15 2 14 6 13 4 10 4 35 Seek e ewe 17 4 17 3 17 1 16 6 16 1 15 7 15 2 14 6 14 1 13 6 12 4 9 6 40 16 3 16 2 16 0 15 5 15 1 14 6 14 2 13 7 13 2 12 7 11 6 9 0 45 MK Ke eee 15 4 15 3 15 0 14 6 14 2 13 8 13 4 12 9 12 4 12 0 10 9 8 5 50 14 6 14 5 14 3 13 9 13 5 13 1 12 7 12 3 11 8 11 3 10 4 8 0 55 Sel Kok e ERS 13 9 13 8 13 6 13 2 12 9 12 5 12 1 11 7 11 3 10 8 9 9 7 6 60 13 3 13 2 13 0 12 7 12 3 12 0 11 6 11 2 10 8 10 4 9 5 7 3 65 MERC Kk eee 12 8 12 7 12 5 12 2 11 8 11 5 11 1 10 7 10 4 10 0 9 1 7 0 70 12 3 12 3 12 1 11 7 11 4 11 1 10 7 10 4 10 0 9 6 8 8 6 8 75 Seco eoe 11 9 11 8 11 7 11 3 11 0 10 7 10 4 10 0 9 6 9 3 8 5 6 6 80 11 5 11 5 11 3 11 0 10 7 10 4 10 0 9 7 9 3 9 0 8 2 6 3 85 Mee eee 11 2 11 1 10 9 10 7 10 4 10 0 9 7 9 4 9 1 8 7 7 9 6 2 90 10 9 10 8 10 6 10 4 10 1 9 8 9 5 9 1 8 8 8 5 7 7 6 0 95 Seek eee 10 6 10 5 10 4 10 1 9 8 9 5 9 2 8 9 8 6 8 2 7 5 5 8 100 HERE KERR 10 3 10 3 10 1 9 8 9 5 9 3 9 0 8 7 8 4 8 0 7 3 5 7 125 HRK CR Hee e e eee e dee 9 2 9 0 8 8 8 5 8 3 8 0 7 8 7 5 7 2 6 6 5 1 150 8 4 8 2 8 0 7 8 7 6 7 3 7 1 6 8 6 6 6 0 4 6 200 Seek eoe eek 7 3 7 1 6 9 6 8 6 6 6 3 6 1 5 9 5 7 5 2 4 0 250 6 4 6 2 6 0 5 9 5 7 5 5 5 3 5 1 4 6 3 6 300 5 8 5 7 5 5 5 3 5 2 5 0 4 8 4 6 4 2 3 3 350
19. 096 44 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2002 Approximate Sampling Variability Tables Quebec Estimated Percentage Numerator of Percentage 900 0 190 1 0 2 096 5 0 10 0 15 0 30 0 35 0 40 0 50 0 70 0 90 0 1 104 7 104 2 103 7 102 1 87 6 84 4 81 1 57 4 33 1 2 74 0 73 7 73 72 2 70 8 68 3 62 0 59 7 57 4 52 4 40 6 23 4 3 60 4 60 2 59 9 58 9 57 4 55 8 50 6 48 8 46 8 42 8 33 1 19 1 4 52 1 51 8 51 0 497 48 3 43 8 42 2 40 6 37 0 28 7 16 6 5 iuc 46 6 46 4 45 7 4 43 2 39 2 37 8 6 3 1 25 7 14 8 60 e 13 4 13 2 12 8 12 5 11 3 10 9 10 5 9 6 7 4 4 3 65 TANE TOU 12 7 123 12 0 10 9 10 5 10 1 9 2 7 1 4 1 70 ia AMEN EM 12 2 11 9 11 5 10 5 10 1 9 7 8 9 6 9 4 0 75 RN dd 11 8 11 5 11 2 10 1 9 8 9 4 8 6 6 6 3 8 80 KR 11 4 11 1 10 8 9 8 9 4 9 1 8 3 6 4 3 7 85 TARAR TRANS Bee 11 1 10 8 10 5 9 5 9 2 8 8 8 0 6 2 3 6 90 elas E 10 8 10 5 10 2 9 2 8 9 8 6 7 8 6 0 3 5 95 ee pude andes 10 5 10 2 9 9 9 0 8 7 8 3 7 6 5 9 3 4 100 OMS 10 2 9 9 9 7 8 8 8 4 8 1 7 4 5 7 3 3 125 ee E RUE 9 1 8 9 8 6 7 8 7 6 7 3 6 6 5 1 3 0 150 Nod ids dd 8 3 8 1 7 9 7 2 6 9 6 6 6 0 4 7 2 7 200 d nde AM 7 0 6 8 6 2 6 0 5 7 5 2 4 1 2 3 250 Moi ae RA 6 3 6 1 5 5 5 3 5 1 4 7 3 6 2 1 300 ania gage ee ip 5 7 5 6 5 1 4 9 4 7 4 3 3 3 1 9 350 kkkkk kkkkk ke e x ok e e x x kkkkkk 5 2 4 7 4 5 4 3 4 0 3 1 1 8 400 kkkkk kkk
20. 1 42 4 41 3 40 1 38 9 37 7 36 4 35 1 33 7 30 8 23 8 3 wdedesedeiedek 35 3 35 2 34 6 33 7 32 7 31 8 30 8 29 7 28 6 27 5 25 1 19 5 4 30 5 30 0 29 2 28 4 27 5 26 6 25 7 24 8 23 8 21 8 16 8 5 NOR Klee eode 27 2 26 8 26 1 25 4 24 6 23 8 23 0 22 2 21 3 19 5 15 1 6 Mese ede je hee ee dee ee desee 24 9 24 5 23 8 23 2 22 5 21 8 21 0 20 2 19 5 17 8 13 8 7 Wee koe jo KO deeodeeke 23 0 22 7 22 1 21 4 20 8 20 1 19 5 18 7 18 0 16 4 12 7 8 Ade EEN 21 2 20 6 20 1 19 5 18 8 18 2 17 5 16 8 15 4 11 9 9 dede 20 0 19 5 18 9 18 3 17 8 17 2 16 5 15 9 14 5 11 2 10 ede Se ehe he e ede ee dede ee dee ee ede 19 0 18 5 17 9 17 4 16 8 16 3 15 7 15 1 13 8 10 7 11 18 1 17 6 17 1 16 6 16 1 15 5 15 0 14 4 13 1 10 2 12 eee woe ee eee 17 3 16 8 16 4 15 9 15 4 14 9 14 3 13 8 12 6 9 7 13 NOCICK ee 16 6 16 2 15 7 15 3 14 8 14 3 13 8 13 2 12 1 9 3 14 ee Wee dee REN ANS 16 0 15 6 15 2 14 7 14 2 13 8 13 3 12 7 11 6 9 0 15 15 5 15 1 14 6 14 2 13 8 13 3 12 8 12 3 11 2 8 7 16 KERR ee ee eoe 15 0 14 6 14 2 13 8 13 3 12 9 12 4 11 9 10 9 8 4 17 deiedek 14 5 14 2 13 8 13 3 12 9 12 5 12 0 11 6 10 6 8 2 18 Mee Se eese e ee hee ERR REN NN 14 1 13 8 13 4 13 0 12 6 12 1 11 7 11 2 10 3 7 9 19 c e e e ke e e e ke e ke e e e ke e ee e e ke e ke e k e ke e e e e k 13 4 13 0 12 6 12 2 11 8 11 4 10 9 10 0 7 7 20
21. 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 Statistics Canada Catalogue no 56M0002GIE 13 Household Internet Use Survey 2003 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 UNDER18 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 the age in four ranges of the Head of Household HAGE 2
22. 25 Data for these variables are collected by the LFS and indicates the presence of household members of different age ranges For example MEMOO 05 indicates the presence of household member s aged 0 to 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 government service or agency a
23. 25 3 24 9 24 3 23 6 22 9 20 HERE See ee 24 8 24 7 24 3 23 7 23 0 22 3 21 NOI kde dew 24 2 24 1 23 7 23 1 22 4 21 8 22 Weide Se eie 23 7 23 5 23 2 22 6 21 9 21 3 23 23 1 23 0 22 7 22 1 21 4 20 8 24 Ade deese 22 7 22 5 22 2 21 6 21 0 20 4 25 22 2 22 1 21 7 21 2 20 6 20 0 30 20 3 20 2 19 8 19 3 18 8 18 2 35 eei ewe 18 8 18 7 18 4 17 9 17 4 16 9 40 WII dede 17 5 17 5 17 2 16 7 16 3 15 8 45 WEAR 16 5 16 5 16 2 15 8 15 3 14 9 50 15 6 15 4 15 0 14 5 14 1 55 14 9 14 7 14 3 13 9 13 5 60 NCKCOICK Ree CR EEE REN 14 3 14 0 13 7 13 3 12 9 65 Meses de esee e dee ee desee 13 7 13 5 13 1 12 8 12 4 70 deo deedeodesee 13 2 13 0 12 6 12 3 11 9 75 12 8 12 6 12 2 11 9 11 5 80 eee 12 3 12 2 11 8 11 5 11 2 85 Mese ede je hehe e de dese ee dee 12 0 11 8 11 5 11 2 10 8 90 11 6 11 5 11 2 10 8 10 5 95 11 2 10 9 10 6 10 2 100 ce ke e ke e ee ee ke e ke ee e e ee e k e e e 10 9 10 6 10 3 10 0 125 e e e e He e e He He e e He He e e He He e e He ke ke ke e 9 7 9 5 9 2 8 9 150 8 9 8 6 8 4 8 1 200 Fe e e e ke e ke e ke e ke e ke e e e ke e e e e ke ke ke 7 7 7 5 7 3 7 1 250 6 7 6 5 6 3 300 KKK KK KKK KK KR e e e He KEK He e e e He e e He ke ke ke e 6 1 5 9 5 8 350 H e e e e e e He He ek He He e e He He de e He He de de H
24. 28 4 16 5 46 3 46 1 45 9 45 1 43 9 42 7 41 4 40 1 38 8 37 3 35 9 32 8 25 4 14 6 42 3 42 1 41 9 41 2 40 1 39 0 37 8 36 6 35 4 34 1 32 8 29 9 23 2 13 7 39 1 39 0 38 8 38 2 37 1 36 1 35 0 33 9 32 8 31 6 30 3 27 7 21 4 12 8 36 6 36 4 36 3 35 7 34 7 33 8 32 8 31 7 30 6 29 5 28 4 25 9 20 1 11 9 34 5 34 4 34 2 33 7 32 8 31 8 30 9 29 9 28 9 27 8 26 7 24 4 18 9 10 10 32 7 32 6 32 4 31 9 31 1 30 2 29 3 28 4 27 4 26 4 25 4 23 2 17 9 10 11 31 2 31 1 30 9 30 4 29 6 28 8 27 9 27 0 26 1 25 2 24 2 22 1 17 1 12 29 9 29 7 29 6 29 1 28 4 27 6 26 7 25 9 25 0 24 1 23 2 21 1 16 4 13 28 6 28 4 28 0 27 3 26 5 25 7 24 9 24 0 23 2 22 3 20 3 15 7 14 KK Ke eee 27 5 27 4 27 0 26 3 25 5 24 8 24 0 23 2 22 3 21 4 19 6 15 2 15 26 6 26 5 26 1 25 4 24 7 23 9 23 2 22 4 21 6 20 7 18 9 14 6 16 FERRERS 25 8 25 6 25 2 24 6 23 9 23 2 22 4 21 7 20 9 20 1 18 3 14 2 17 25 0 24 9 24 5 23 8 23 2 22 5 21 8 21 0 20 3 19 5 17 8 13 8 18 MERC KC eee 24 3 24 2 23 8 23 2 22 5 21 8 21 1 20 4 19 7 18 9 17 3 13 4 19 23 6 23 5 23 2 22 5 21 9 21 3 20 6 19 9 19 2 18 4 16 8 13 0 20 23 0 22 9 22 6 22 0 21 4 20 7 20 1 19 4 18 7 17 9 16 4 12 7 21 22 5 22 4 22 0 21 4 20 8 20 2 19 6 18 9 18 2 17 5 16 0 12 4 22 Med kk eee 22 0 21 9 21 5 20 9 20 4 19 8 19 1 18 5 17 8 17 1 15 6 12 1 23 PERE ERE 21 5 21 4 21 0 20 5 19 9 19 3 18 7 18 1 17 4 16 7 15 3 11 8 24 EERE ERS 21 0 20 9 20 6 20 1 19 5 18 9 18 3 17 7 17 0 16 4 14 9 11 6 25 RARER RK 20 6 20 5 20 2 19 7 19 1 18 5 17 9 17 3 16 7 16
25. 34 1 32 9 31 6 28 8 22 3 12 9 60 idis 11 7 11 7 11 5 11 2 10 9 9 9 9 5 9 1 8 3 6 4 3 7 65 iib 11 3 11 2 11 0 10 7 10 4 9 5 9 1 8 8 8 0 6 2 3 6 70 non 10 8 10 8 10 6 10 3 10 1 9 1 8 8 8 4 7 7 6 0 3 4 75 bi 10 5 10 4 10 3 10 0 9 7 8 8 8 5 8 2 7 4 5 8 3 3 80 did 10 1 10 1 9 9 9 7 9 4 8 5 8 2 7 9 7 2 5 6 3 2 85 idi 9 8 9 8 9 6 9 4 9 1 8 3 8 0 7 7 7 0 5 4 3 1 90 ds 9 6 9 5 9 4 9 1 8 9 8 0 7 8 7 4 6 8 5 8 3 0 95 idi 9 3 9 3 9 1 8 9 8 6 7 8 7 5 7 2 6 6 5 1 3 0 100 uns 9 1 9 0 8 9 8 7 8 4 7 6 7 4 7 1 6 4 5 0 2 9 125 ood obs 8 1 8 0 7 7 US 6 8 6 6 6 3 5 8 4 5 2 6 150 dci iiid 7 4 7 3 7 1 6 9 6 2 6 0 5 8 5 3 4 1 2 4 200 dd 6 4 6 3 6 1 5 9 5 4 5 2 5 0 4 6 3 5 2 0 250 nis T 5 6 5 5 5 3 4 8 4 7 4 5 4 1 3 2 1 8 300 nds e ndi 5 1 5 0 4 9 4 4 4 2 4 1 3 7 2 9 1 7 350 ME TEM 4 8 4 6 4 5 4 1 3 9 3 8 3 4 2 7 1 5 400 I uale 4 4 4 3 4 2 3 8 3 7 3 5 3 2 2 5 1 4 450 iig i 4 2 4 1 4 0 3 6 3 5 3 3 3 0 2 4 1 4 500 nd ATA Mn 4 0 3 9 3 8 3 4 3 3 3 2 2 9 2 2 1 3 750 d TER FOREN dud 3 2 3 1 2 8 2 7 2 6 2 4 1 8 1 1 1000 edi di TENE 2 7 2 7 2 4 2 8 2 2 2 0 1 6 0 9 1500 Te TE 2 2 2 0 1 9 1 8 1 7 1 3 0 7 2000 kkkk kkkk kkkk kkkkk kkkk kkkk 1 7 1 6 1 6 1 4 1 1 0 6 3000 kkkk kkkk kkkk kkkkk kkkk kkkk 1 4 1 3 1 3 1 2 0 9 0 5 4000 KKK ck kkkk kkkkk kkk k kkkk kkkk 1 o 1 1 1 0 0 8 0 5 5000 kkkk kkkk kkkk ok e x x kkkk kkkk ok e XX ok e Xx kkkkkk 0 9 0 7 0 4 6000 kkkk kkkk kkkk ok e x x kkkk kkkk kkkk kkkk kkkkkk 0 8 0 6 0 4 7000 kkkk kkk
26. 4 4 HERKEN HH 47 3 47 1 46 3 45 1 43 8 42 5 41 2 39 8 38 3 36 8 33 6 26 0 15 0 5 Noe dede ek 42 3 42 1 41 4 40 3 39 2 38 0 36 8 35 6 34 3 32 9 30 1 23 3 13 4 6 HERA 38 6 38 4 37 8 36 8 35 8 34 7 33 6 32 5 31 3 30 1 27 4 21 3 12 3 7 FERRER EE 35 8 35 6 35 0 34 1 33 1 32 1 31 1 30 1 29 0 27 8 25 4 19 7 11 4 8 HERE ENN 33 4 33 3 32 8 31 9 31 0 30 1 29 1 28 1 27 1 26 0 23 8 18 4 10 6 9 31 5 31 4 30 9 30 1 29 2 28 3 27 4 26 5 25 6 24 5 22 4 17 4 10 0 10 29 9 29 8 29 3 28 5 27 7 26 9 26 0 25 2 24 2 23 3 21 3 16 5 9 5 11 wdedeiedesede 28 5 28 4 27 9 27 2 26 4 25 6 24 8 24 0 23 1 22 2 20 3 15 7 9 1 12 Sede deieeescieoedeew 27 2 26 8 26 0 25 3 24 5 23 8 23 0 22 1 21 3 19 4 15 0 8 7 13 NOICICK ele hodie eee 26 1 25 7 25 0 24 3 23 6 22 8 22 1 21 3 20 4 18 6 14 4 8 3 14 Mee ehe je hee eee ee dee 25 2 24 8 24 1 23 4 22 7 22 0 21 3 20 5 19 7 18 0 13 9 8 0 15 24 3 23 9 23 3 22 6 22 0 21 3 20 5 19 8 19 0 17 4 13 4 7 8 16 deis decies 23 5 23 2 22 5 21 9 21 3 20 6 19 9 19 2 18 4 16 8 13 0 7 5 17 eee 22 8 22 5 21 9 21 3 20 6 20 0 19 3 18 6 17 9 16 3 12 6 7 3 18 Mee see de eee e dee ee desee 22 2 21 8 21 3 20 7 20 0 19 4 18 7 18 1 17 4 15 8 12 3 7 1 19 deo deedeodesee 21 6 21 3 20 7 20 1 19 5 18 9 18 2 17 6 16 9 15 4 11 9 6 9 20 dee deese ee EER 21 0 20 7 20 2 19 6 19 0 18 4 17 8 17 1 16 5 15 0 11 6 6 7 21 Nee kde eookedeeeeieke 20 5 20 2 19 7 19 1 18 6 18 0 17 4 16 7 16 1 14 7 11 4 6 6 22 PEEK Je hehe e
27. 4 5 0 3 8 85 6 1 5 9 5 7 5 5 5 3 4 8 3 7 90 KKK KKK KKK KK ke KKK ek RK KKK KK RK He He de de He RRR KKK de de He KKK KER ERR KK 5 7 5 5 5 3 5 1 4 7 3 6 95 5 6 5 4 5 2 5 0 4 6 3 5 100 5 4 5 2 5 1 4 9 4 4 3 4 125 4 7 4 5 4 3 4 0 3 1 150 KK KKK KKK ck RRR KKK RR KKK ek RK KKK KKK RK KKK ek KKK KKK KK RK KKK RK e de de k KER ERR KK 4 1 4 0 3 6 2 8 200 3 1 2 4 250 2 2 300 KKK KKK IKK KKK KK KK RK O O RK RK RIK KKK RIK KK RK KKK IKK RK RK KKK RK RK RIK e de de k e KKK RR RRR RRR KEK ERE 2 0 350 KK KKK KKK KK RK KKK KK RRR KKK RRR KKK ek RK RIK ek RK RK ke ke k k k k kde d KKK IK KKK KK KKK RRR RRR EKER kk k kkk k kkk k Note For corre
28. 5 10 1 9 7 9 3 8 5 6 6 3 8 21 ck ke e ke ke ke ek khe ek ke hee kc ke ek ke ke ke koe e e ee 11 2 10 9 10 5 10 2 9 9 9 5 9 1 8 3 6 5 3 7 22 KR ke ek ke hee ke ke he ek ke ke ek ke hee ke ke e e ke ee e e ee 10 9 10 6 10 3 10 0 9 6 9 3 8 9 8 1 6 3 3 6 23 KKK ke ke ke e ke ke ke ek ke hee ke ke hee ke ke e ke ke e e ke 10 7 10 4 10 1 9 7 9 4 9 1 8 7 8 0 6 2 3 6 24 KR KKK ehe ek ke he ek ke he ek ke ke ee ke ke ee ke ee e e ke 10 5 10 2 9 9 9 5 9 2 8 9 8 5 7 8 6 0 3 5 25 10 2 10 0 9 7 9 3 9 0 8 7 8 4 7 6 5 9 3 4 30 e He e e He e He He e He e e He e ke He e ke ke ke ke ke ke ke k ke k k k k k k k kk kk kkk 9 1 8 8 8 5 8 2 7 9 7 6 7 0 5 4 3 1 35 8 4 8 2 7 9 7 6 7 4 7 1 6 5 5 0 2 9 40 7 9 7 6 7 4 7 1 6 9 6 6 6 0 4 7 2 7 45 7 2 7 0 6 7 6 5 6 2 5 7 4 4 2 5 50 6 8 6 6 6 4 6 2 5 9 5 4 4 2 2 4 55 KKK KKK KKK KK RRR KK KK RK KKK KKK RK e e He He e e He He e de e He KEKE 6 5 6 3 6 1 5 9 5 6 5 1 4 0 2 3 60 6 0 5 8 5 6 5 4 4 9
29. 90 0 1 ERR ERE 56 1 55 8 54 9 53 5 52 0 50 4 48 8 47 2 45 4 43 7 39 9 30 9 17 8 2 39 7 39 5 38 9 37 8 36 7 35 7 34 5 33 3 32 1 30 9 28 2 21 8 12 6 3 32 4 32 2 31 7 30 9 30 0 29 1 28 2 27 2 26 2 25 2 23 0 17 8 10 3 4 ERE ENA 28 0 27 9 27 5 26 7 26 0 25 2 24 4 23 6 22 7 21 8 19 9 15 4 8 9 5 25 1 25 0 24 6 23 9 23 2 22 5 21 8 21 1 20 3 19 5 17 8 13 8 8 0 6 HERA 22 9 22 8 22 4 21 8 21 2 20 6 19 9 19 3 18 6 17 8 16 3 12 6 7 3 7 FERRER EE 21 2 21 1 20 8 20 2 19 6 19 1 18 5 17 8 17 2 16 5 15 1 11 7 6 7 8 PERE ENA 19 8 19 7 19 4 18 9 18 4 17 8 17 3 16 7 16 1 15 4 14 1 10 9 6 3 9 SERRE ek 18 7 18 6 18 3 17 8 17 3 16 8 16 3 15 7 15 1 14 6 13 3 10 3 5 9 10 Mee je eee e dee dee desee 17 6 17 4 16 9 16 4 15 9 15 4 14 9 14 4 13 8 12 6 9 8 5 6 11 16 8 16 6 16 1 15 7 15 2 14 7 14 2 13 7 13 2 12 0 9 3 5 4 12 Sede dece 16 1 15 9 15 4 15 0 14 6 14 1 13 6 13 1 12 6 11 5 8 9 5 1 13 eee eoe 15 5 15 2 14 8 14 4 14 0 13 5 13 1 12 6 12 1 11 1 8 6 4 9 14 HEARNE eee ee de ee 14 9 14 7 14 3 13 9 13 5 13 0 12 6 12 1 11 7 10 7 8 3 4 8 15 Wei Glo Je Ko dede 14 4 14 2 13 8 13 4 13 0 12 6 12 2 11 7 11 3 10 3 8 0 4 6 16 dee decies EER 14 0 13 7 13 4 13 0 12 6 12 2 11 8 11 4 10 9 10 0 7 7 4 5 17 NOKCICK Rede e ERE EEK 13 5 13 3 13 0 12 6 12 2 11 8 11 4 11 0 10 6 9 7 7 5 4 3 18 Mee ee de eee e dee ee desee 13 2 13 0 12 6 12 2 11 9 11 5 11 1 10 7 10 3 9 4 7 3 4 2 19 12 6 12 3 11 9 11 6 11 2 10 8 10 4 10 0 9 1
30. Household Internet Use Survey 2003 User Guide correction and were flagged along with the other imputed values for the variables concerned The number of outliers so identified was in keeping with past years processing however this was the first year that winsorization was used instead of donor imputation to treat influential observations Generally when an outlier is winsorized its value is replaced by a value equal to that of the next largest observation not judged to be an outlier Outliers that are identified based solely on their reported values would simply be assigned the next largest reported value Outliers identified based on their weighted values reported values multiplied by the survey weights would not have their weights reduced but their reported values would be modified so that the resulting rounded weighted value would approximately equal the next largest weighted value However all outliers of the first kind were also weighted outliers this year Winsorization was therefore applied to the weighted values only for electronic commerce expenditures and this was done separately in categories by 1 types of orders paid over the Internet versus paid through other means and 2 according to the type of products ordered The same 47 categories had also been used during outlier detection 8 2 4 1 Imputation Imputation is the process that supplies valid values for those variables that have been identified as requ
31. Survey 2003 User Guide 30 100 000 or more 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 a LFS supplement also conducted in January 2004 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 a HIUS category and 2 as a CTS category For 19 of the households no income was available from the HIUS or the 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 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 valu
32. Survey 2003 User Guide 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 one person two people and three 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 responding households in province household size group The resulting weight WTHM is the final weight which appears on the HIUS microdata file Statistics Canada Catalogue no 56M0002GIE 67 Household Internet Use Survey 2003 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
33. UI UI UI OY OY O J J OO OO OO O O 1 0 9 40 0 HHHHHHHHHHHHNNMNms O O H H H N UQ UQ i Ul O O O UU t0 H Q 9 58 NU iS O 00 HQ UL O HU 0 0 IQ S UL 1 9 I ANON OO i OO O JU 01 9001 9O O 55 aS UI UI UI UI OY OY OO OO OO OO OO o o WO 50 0 PRPRPRPRPRPRPRRPERNNW COOrRRPNWRHR UD OF ODO WIADDEFENWUADOWHDOWDUDADONAHOPABAIOCHROWOUNHEWIIKHUD U9 9 9 0 U iS iS uS aS aS iiS UI UI UI UI OY O 1 O0 OO OO OO OO O0 o WO 1 70 0 PRPRPRPENN H H UQ i OO tDOORHDWORFPKRFPNWUBDATORFWHAHWOWDWOWONWUHAAWAONUAOCKANAINOWOUATNO o UN N N N UQ UU QU U Q QU Q QU Ui i iS uS UI UI UI OY O O OY OY O 1 1 1 1 OO OO OO o o 90 0 16 Statistics Canada Catalogue no 56M0002GIE HH H H H IN N S S NS SB iB U U U U U U U U UJ ds AAAA iS UI UI UI UI O OO OPNA UNN O0 OO to t0 O H N QU i UI Oy OO H2 RUAN OO 10 H1 INN U UI O0 OO O U Oy o U OO UI i 10 Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2003 Approximate Sampling Variability Tables Alberta Numerator of Estimated Percentage Percentage 000 0 15 1 05 2 05 5 05 10 05 15 05 20 05 25 05 30 05 35 05 40 0 50 0 70 0 90 05 1 95 0 94 6 94 1 92 7 90 2 87 7 85 0 82 3 79 5 76 7 73 6 67 2 52 1 30 1 2 Wee je eee 66 9 66 6 65 5 63 8 62 0 60 1 58 2 56 2 54 2 52 1 47 5 36 8 21 3 3 54 6 54 3 53 5 52 1 50 6 49 1 47 5 45 9 44 3 42 5 38 8 30 1 17
34. UI UI UI UI UI O OY Oy H J OO o WOW WO 40 0 HHHHHHHHHHHHHHHNWNN Ud O O O HP HP HP N N U UQ i UI Oy O UF i 2Jt oO lU 1O ODON i 00HnP 5 Joisi0l JIUt o Jooo Joii nm P mo iS iS uS uis uS UI UI UI UI UI O O J 1 OO OO oO WOW WO WO 50 05 COORRPNNWHEUDDWOW DOO CHWUDWHOPNERUDADONBRIOCRDWONRHDDONUDKPRINDHEIANNBORPNAN NN N N IN U Q s AAAA UI UI UI UI OY OY O 000 0 o o o o WW 70 0 PRPRPRPRPRENW OrRPRPNWUAN EF SUBD TIMONUAERFNWHEUAATOON BP HDOWAINBPUDAWOKRFWAWOWHDOHRDAHOWOIOHDOOF FPRPRFPRFPRPNNNNWWWWWWWWPh AAAA UI U1OYO OO O J OO OO OO O O 90 0 BPR N o SCOORPRPRPRPRPRFRFRFRPNNNNNNNNNNWWWWWWW i is i i iS uS UI UI UI UI OY Oy HD 10 O 63 cO OO t 00O0HF U10000 o2OO HP HrP N00 01 9200 00 0 12 120 iO I io i U10OY00O ID oO i 000000 10 Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2003 Approximate Sampling Variability Tables Canada Numerator of Estimated Percentage Percentage 000 0 1 1 05 2 05 5 05 10 05 15 05 20 0 25 0 30 0 35 0 40 0 50 0 70 0 90 0 1 103 5 103 1 102 5 101 0 98 3 95 5 92 6 89 7 86 7 83 5 80 2 73 2 56 7 32 2 73 2 72 9 72 5 71 4 69 5 67 5 65 5 63 4 61 3 59 0 56 7 51 8 40 1 23 3 59 8 59 5 59 2 58 3 56 7 55 1 53 5 51 8 50 0 48 2 46 3 42 3 32 8 18 4 51 8 51 5 51 3 50 5 49 1 47 7 46 3 44 8 43 3 41 8 40 1 36 6
35. Ui H 0 0 O N UI Q H J HP UI 10 i O OY U H O H i O H O H Estimated Percentage 15 0 52 37 30 26 23 21 19 18 17 16 15 15 14 14 13 13 12 12 12 11 11 11 10 10 10 5 Statistics Canada Catalogue 56M0002GIE U1 Ui Ut U O OY Oy on I J J 0 OO NPUANTORPWUDOFBDAWHOHDUTONBAIOBARFPUOUrRFAHUUABRBRNWE BS 20 0 50 36 29 25 22 20 19 18 17 16 15 14 14 13 13 12 12 12 11 11 11 10 10 10 10 4 4i Ul UI UI UI Ui UI O OF OD OF 1 CO NUPEN BUN x0 HU Oy 10 INN OY O O0 U N 5 OY OO O U J IP O9 P J UO HP O O N O0 5 5 O 25 49 34 28 24 22 20 18 17 16 15 14 14 13 13 12 12 11 11 11 11 10 10 10 10 d a au UI UI UI UI Ui UI O O GD NNOO 2003 Od wHn NUU J 0H Bi oo9ouUo0UOCO0Hd PUU IoUo to U 0 JN INOO0 55 amp o0onmn BOOd5og0N 5 30 0 HHHHHHHHHBHHBHBB DHBHB DHBNNNU OOO0nnH N NM QU U i Ur UI O O to H U 1 0 i0 0 0 i0 O IN U UI I U1 O UI i OY tO IN U1 i00 2D H 0 O 00 O 000 01 OY Ov m iS A i UI UI UI UI UI UI OY O O J OO OO WO A 35 0 PRPRPRPRPRPRPRPRPBPRPRPEBPRPENNNWA 0 ONNEAAN O0 O H QU UI J i0 NN Ui ONN ANA OY OO O QU Ui OO UI xo UB m N CQ Q0 iS us aS iiS UI
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 Non CMA 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 CMA 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 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide 5 0 Survey Methodology The Household Internet Use Survey HIUS was administered in January 2004 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 an
37. de k kek k kkk k 4 2 3 3 1 9 90 PAO ek RK KKK KK RRR KKK KK O ke KK RK KKK ek kde d k kede d k e RI KKK IK e de de k e de de k e de de ke KKK kede k k kk k k kk kkk k k 4 1 3 2 1 8 95 POOO OOO O O k RK KKK ek ede d k kede de k ede de k e de de ke ede de k e de de k e de de k e de de k ede de k kede ke k kk k kkk k 4 0 3 1 1 8 100 KR KKK KKK KK RK khe KK RK O KK ek ke KK KKK RIK k ke ke ek k kde ek kede de k kede de k kede de k kede de k ede de k kede de k kede k kkk k kkk k kkk k 3 0 1 7 125 KKK KKK KKK ek che KKK KKK ke ke ek ek ke ke eek RK ke ke ek ek RK RK KKK RK eee ek deck de k RK KKK RK RK de de k e de de k e de de k e de k kede k k kkk k kkk k 2 7 1 5 150 KR KKK KKK KK KKK KKK RK KKK ek KKK KK IKK RK KKK ek ek RK ke ke k RIK IKK RK KKK IK RRR KKK KK KKK de de d k deke RARER RRR ERE k 1 4 Note For correct usage of these tables please refer to the microdata documentation Statistics Canada Catalogue no 56M0002GIE 51 Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2003 Approximate Sampling Variability Tables Prince Edward Island Numerator of Estimated Percentage Percentage 000 0 15 1 05 2 05 5 05 10 05 15 05 20 05 25 05 30 05 35 05 40 0 50 0 70 0 90 0 1 LEER Ree eek eee AEH 30 7 30 2 29 4 28 6 27 7 26 8 25 9 25 0 24 0 21 9 17 0 9 8 2 HERKEN RUNS 21 4 20 8 20 2 19 6 19 0 18 3 17 7 17 0 15 5 12 0 6 9 3 cce ek eee ke ehe ek e e e ke eek eee OS eee e k k 17 0 16 5 16 0 15 5 15 0 14 4 13 9 12 7 9
38. e k k k e k k k k k k k k kkk k k kkk kk e e e e e e e e ec he ke e ee e e e e e 4 7 4 6 4 4 4 2 3 9 450 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 4 3 4 2 4 0 3 6 500 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 3 9 3 8 3 5 750 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 2 8 1 000 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk Note For correct usage of these tables please refer to the microdata documentation 60 70 0 HEPEPPRPEPEPEPEPREPPRPEPRPRPRPEPRENNNNNNWBAU OONNNNWWWA iS UL UL OY OO OO O 5 OY Q0 M 1 0 O N Ui O N 0 s O HU UI 1 HP UI Q0 Ul IP Q0 O N Ui 0 IH i IH UI O UI O OQ U P t0 O N O Ui O iS O HL N NM NM Q Q QU QU i5 i8 UI OY OY OY OY OY O 1 1 OO OO OO 90 0 0 A ONU OWOPN BAUN ON AOON ODOR OUNPPUAWOUO H H H H H H NM N N N U U U U U U i iiS AA UIS US ULUL UL OY 0 1 HE RE SE O0 00 00 O0 o Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2003 Approximate Sampling Variability Tables Atlantic Provinces Numerator of Estimated Percentage Percentage 000 0 15 1 05 2 05 5 05 10 05 15 05 20 05 25 05 30 05 35 05 40 0 50 0 70 0
39. eee 54 5 54 2 53 4 51 9 50 5 49 0 47 4 45 8 44 1 42 4 38 7 30 0 17 3 2 deu ewe sce EEN 38 3 37 7 36 7 35 7 34 6 33 5 32 4 31 2 30 0 27 4 21 2 12 2 3 NOR Kodeee eode 31 3 30 8 30 0 29 1 28 3 27 4 26 4 25 5 24 5 22 4 17 3 10 0 4 Mese ede je hee ERNE dede hehe de eee dedee 26 7 26 0 25 2 24 5 23 7 22 9 22 1 21 2 19 4 15 0 8 7 5 23 9 23 2 22 6 21 9 21 2 20 5 19 7 19 0 17 3 13 4 7 7 6 PEEK ee oe oe dee desee 21 8 21 2 20 6 20 0 19 4 18 7 18 0 17 3 15 8 12 2 7 1 7 NICK ek dele LEER dede 20 2 19 6 19 1 18 5 17 9 17 3 16 7 16 0 14 6 11 3 6 5 8 Weed de hehehe e dece hee hee e e dee eje eee 18 9 18 4 17 8 17 3 16 8 16 2 15 6 15 0 13 7 10 6 6 1 9 EEA EERE ERR EERE 17 8 17 3 16 8 16 3 15 8 15 3 14 7 14 1 12 9 10 0 5 8 10 16 4 16 0 15 5 15 0 14 5 14 0 13 4 12 2 9 5 5 5 11 KR e ke ehe e ke eee ke ee cec ee e e ee e e eee e k k 15 7 15 2 14 8 14 3 13 8 13 3 12 8 11 7 9 0 5 2 12 KR ee ke ke he ek ke he ek ke ke ek khe ek ke ke ee ke eee ke 15 0 14 6 14 1 13 7 13 2 12 7 12 2 11 2 8 7 5 0 13 14 4 14 0 13 6 13 2 12 7 12 2 11 8 10 7 8 3 4 8 14 13 9 13 5 13 1 12 7 12 2 11 8 11 3 10 3 8 0 4 6 15 ke e ke ke ke ek khe ek ke ke e ke ke ek ke ec e koe e ee 13 4 13 0 12 6 12 2 11 8 11 4 11 0 10 0 7 7 4 5 16 Kc e kc e ke e ke e ke KEKE e ee ke e
40. for all civilian household members 15 years 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 d
41. households with a regular user from home in 2003 an estimated 4 4 million 6596 had a high speed link to the Internet through either a cable or telephone connection This was up from 5696 a year earlier e Atthe same time the proportion of households that had a low speed connection fell from 44 in 2002 to 35 last year Internet service providers have increased their expenditures on high speed infrastructure in a competitive battle to provide subscribers with a wider range of online services e Ofthe estimated 4 4 million households with high speed connection the majority 61 had a link through cable The remaining 39 had a high speed telephone connection also known as a digital subscriber line or DSL e However the number of DSL connections increased nearly 30 in 2003 compared with a gain of only 2196 for cable This may be an indication of price competitiveness of DSLs over cable connections or increased accessibility of households to high speed telephone infrastructure within their neighbourhood e More and more households were using the Internet to search for medical or health related information or to use online banking services However fewer reported downloading music e Just under 38 of regular users from home reported downloading music in 2003 down from a high of 4896 in 2001 This may be the result of a highly publicized campaign by the music industry against downloading music for free Statistics Canada Catalog
42. is prompted through message screens on the computer to modify the information However for some questions interviewers have the option 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 be
43. 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 to account for the use of a four 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 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 Statistics Canada Catalogue no 56M0002GIE Household Internet Use
44. probability of selecting the person to whom the record refers In the example of a 296 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 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 ra
45. tnmen nnn n intrans tasas nnmnnn nanna 10 4 1 Labour Force Survey Concepts and Definitions seen 10 4 2 Household Internet Use Survey Concepts and Definitions suus 11 4 3 Labour Force Survey Variable Definitions sssssseeeeeenens 13 Survey Methodology neeeeeeeeeeeei eee eeseeee ense enn ne nnn natn insana nass nenne ennnen nenne ennn annene 17 5 1 Population Coverage 2n reed ER da ee ed naa 17 5 2 Sample 17 5 2 1 Primary Stratification eb re eeu che aad 17 5 2 2 Ibi cc 17 5 2 3 Secondary Stratificati N RE 18 5 2 4 Cluster Delineation and Selection ccccccceeeceeseeeeeeeeeeeaeeeeseeseeeeessaeessaeeenaees 18 5 2 5 Dwelling SelectlOr 2 Lei ttt lent atem Ron da beeen Rx ke dave 19 5 2 6 Person SelectiOM TETTE EO 19 5 3 SEE 19 5 4 Sample ROTA ON n 19 5 5 Modifications to the Labour Force Survey Design for the Household Internet Use Survey ssssssssssssseseseeese ener enne enne eniin nnns entente nnns tn ns senten 20 5 6 Sample Size by Province for the Household Internet Use 20 PrirgelsppnaEeceec c 21 6 1 Interviewing for the Labour Force Survey cc ccccceceeeeeeeeeneeceeeeeeeaeeeee
46. 0 TRAER eee 11 1 11 0 10 7 10 4 9 4 9 1 8 7 7 9 6 2 3 6 85 joie wees 10 8 10 6 10 3 10 1 9 1 8 8 8 4 7 7 6 0 3 4 90 paid ar 10 5 10 3 10 1 9 8 8 9 8 5 8 2 7 5 5 8 3 4 95 VH E Ca 10 1 9 8 9 5 8 6 8 3 8 0 7 3 5 7 3 3 100 bails Se 9 8 9 5 9 3 8 4 8 1 7 8 7 1 5 5 3 2 125 p HC c d 8 8 8 5 8 3 7 5 7 8 7 0 6 4 4 9 2 8 150 iiie dM d 8 0 7 8 7 6 6 9 6 6 6 4 5 8 4 5 2 6 200 VS CN 6 9 6 7 6 6 5 9 5 7 5 5 5 0 3 9 2 2 250 panes pans munis 6 0 5 9 5 3 5 1 4 9 4 5 3 5 2 0 300 p OC cl 5 5 5 4 4 9 4 7 4 5 4 1 3 2 1 8 350 id THU 5 1 5 0 4 5 4 3 4 2 3 8 2 9 1 7 400 ipud CM Nt ERTER 4 8 4 6 4 2 4 1 3 9 3 6 2 8 1 6 450 Mbit di do E PUER 4 4 4 4 0 3 8 3 7 3 4 2 6 1 5 500 kkkk ck e x kkkk k kkkkk kkkkk 4 3 8 3 6 3 5 3 2 2 5 1 4 750 kc v x kkkkk kc x xXx kkkkk kc x x kk ke xe 3 1 3 0 2 8 2 6 2 0 1 2 1000 kkkk k kk e x kkkkk kk x x kk e x kkkkk kkkkk 27 26 25 2 2 1 7 1 1500 kc v x kk e x kc e x kk x x kk e x kkkkk kkkkk kkkk k 2 1 2 0 1 8 1 4 0 8 2000 kc e x kk e x ck e x kk e x kk e x kkkkk kkkkk kkkk ok e x kc x x 1 6 1 2 0 7 3000 kkkkk kkkkk kkkkk kkkkk kkkkk kkkkk kkkk kkkkk kkkkk kkkkk kkkkk 1 0 6 4000 kkkk kkkkk kkkkk kk e x kkkkk kkkkk kkkkk ck x x x ok e x ok e x ok e x ck e x 0 5 Note For correct usage of these tables please refer to the microdata documentation 2 Using Rule 3 the standard error of a difference X is o y Ra a where X is estimate 1 Quebec x is estimate 2 Ontario and a
47. 3 8 2 2 65 KKK KKK KKK KK KKK KKK RK KKK ek e e KKK KKK e He He e e He He e e He He e e He ke ee e ke kek ke 5 8 5 6 5 4 5 2 4 7 3 7 2 1 70 FE HE E E de KK KK ke de de He RK de He KKK deck de e He He de e He RK de He He de de He He de de He ke dede He ke keke k kkk k 5 6 5 4 5 2 5 0 4 6 3 5 2 0 75 FEE E E E e He He de e He ke de de He He de e He He de de He He de e He He de e He He de e He He de e He He e e He He e e He He e e He He e e He k eke e k kek ke 5 2 5 0 4 8 4 4 3 4 2 0 80 POO RK KKK RIK KK RK O k RK k e KK KKK RIK kede de k kede de k kede de k kde de k kdk k k kk k 5 0 4 9 4 7 4 3 3 3 1 9 85 4 9 4 7 4 5 4 1 3 2 1 9 90 4 6 4 4 4 0 3 1 1 8 95 4 5 4 3 3 9 3 0 1 8 100 KKK KKK KKK ek OOO KK RK KKK IK KK RK KKK KK RK KKK ek k d k kde d k kde de k kde de k kde de k kde de k kok k k kk k 4 4 4 2 3 8 3 0 1 7 125 3 4 2 6 1 5 150
48. 35 8 34 6 33 3 32 0 29 2 8 ele koe eee 38 5 38 3 37 7 36 7 35 6 34 6 33 5 32 3 31 2 29 9 27 3 9 HERE KE RE 36 3 36 1 35 5 34 6 33 6 32 6 31 6 30 5 29 4 28 2 25 8 10 34 4 34 2 33 7 32 8 31 9 30 9 29 9 28 9 27 9 26 8 24 5 11 32 8 32 6 32 1 31 3 30 4 29 5 28 6 27 6 26 6 25 5 23 3 12 Seek eee 31 4 31 3 30 8 29 9 29 1 28 2 27 3 26 4 25 5 24 5 22 3 13 HERE KE RE 30 2 30 0 29 6 28 8 28 0 27 1 26 3 25 4 24 5 23 5 21 4 14 REARS 29 1 28 9 28 5 27 7 26 9 26 1 25 3 24 5 23 6 22 6 20 7 15 28 1 28 0 27 5 26 8 26 0 25 3 24 5 23 6 22 8 21 9 20 0 16 Seek eoe 27 2 27 1 26 6 25 9 25 2 24 5 23 7 22 9 22 0 21 2 19 3 17 3okckeielekekelokeioedekdek 26 3 25 9 25 2 24 5 23 7 23 0 22 2 21 4 20 5 18 8 18 MR CK CR ee ee e eee deco 25 5 25 1 24 5 23 8 23 1 22 3 21 6 20 8 20 0 18 2 19 24 8 24 5 23 8 23 1 22 4 21 7 21 0 20 2 19 4 17 7 20 Seele eee eek 24 2 23 8 23 2 22 5 21 9 21 2 20 5 19 7 18 9 17 3 21 23 6 23 3 22 6 22 0 21 3 20 7 20 0 19 2 18 5 16 9 22 Mee lee dee ee eee e dee 23 1 22 7 22 1 21 5 20 9 20 2 19 5 18 8 18 1 16 5 23 22 6 22 2 21 6 21 0 20 4 19 7 19 1 18 4 17 7 16 1 24 Seek eee ee Kee deieee 22 1 21 8 21 2 20 6 20 0 19 3 18 7 18 0 17 3 15 8 25 21 7 21 3 20 7 20 2 19 6 18 9 18 3 17 6 16 9 15 5 30 19 8 19 5 18 9 18 4 17 9 17 3 16 7 16 1 15 5 14 1 35 See e koe oe Kee doe eee 18 0 17 5 17 0 16 5 16 0 15 5 14 9 14 3 13 1 40 ERREREERAAEA EA REE E
49. 7 1 4 1 20 HERE ee oleo oe EERE HH 12 3 12 0 11 6 11 3 10 9 10 5 10 2 9 8 8 9 6 9 4 0 21 Ne dede 12 0 11 7 11 3 11 0 10 7 10 3 9 9 9 5 8 7 6 7 3 9 22 Weed je hehehe e ese hehe hee he hee eee eee 11 7 11 4 11 1 10 7 10 4 10 1 9 7 9 3 8 5 6 6 3 8 23 eiecti EERE EEE 11 5 11 2 10 8 10 5 10 2 9 8 9 5 9 1 8 3 6 4 3 7 24 desc descen ee oe oe ee deseo 11 2 10 9 10 6 10 3 10 0 9 6 9 3 8 9 8 1 6 3 3 6 25 ee RE RRR REE AEE 11 0 10 7 10 4 10 1 9 8 9 4 9 1 8 7 8 0 6 2 3 6 30 10 0 9 8 9 5 9 2 8 9 8 6 8 3 8 0 43 3 5 6 3 3 35 eei desee ee ele woe ee oeeieke 9 3 9 0 8 8 8 5 8 3 8 0 7 7 7 4 6 7 5 2 3 0 40 8 7 8 5 8 2 8 0 7 7 7 5 7 2 6 9 6 3 4 9 2 8 45 Wee ede Je hehehe ese ehe ERR ERG RN 8 2 8 0 7 7 7 5 Tad 7 0 6 8 6 5 5 9 4 6 2 7 50 7 6 7 3 7 1 6 9 6 7 6 4 6 2 5 6 4 4 2 5 55 KKK e ke e ee ke e ke e ke e ke e ke e ke e ke e ke e ke e e e ke e e e 7 2 7 0 6 8 6 6 6 4 6 1 5 9 5 4 4 2 2 4 60 KKK e ke e ke e ke e ke e ee ke e ke e ke e ke e ke e ke e ke e e e e e 6 9 6 7 6 5 6 3 6 1 5 9 5 6 5 1 4 0 2 3 65 6 6 6 4 6 3 6 1 5 8 5 6 5 4 4 9 3 8 2 2 70 6 4 6 2 6 0 5 8 5 6 5 4 5 2 4 8 3 7 2 1 75 KKK e ke ke eek ke hee ke ke hee ke ke ke ee ke eee ke eee e eoe 6 2 6 0 5 8 5 6 5 4 5 2 5 0 4 6 3 6 2 1 80
50. 8 16 9 25 0 24 1 23 2 22 3 20 4 15 8 23 5 22 7 21 9 21 0 19 2 14 9 22 3 21 6 20 8 20 0 18 2 14 1 21 3 20 6 19 8 19 0 17 4 13 5 20 4 19 7 19 0 18 2 16 6 12 9 19 6 18 9 18 2 17 5 16 0 12 4 18 9 18 2 17 6 16 9 15 4 11 9 18 2 17 6 17 0 16 3 14 9 11 5 17 6 17 1 16 4 15 8 14 4 11 2 17 1 16 5 15 9 15 3 14 0 10 8 16 6 16 1 15 5 14 9 13 6 10 5 16 2 15 6 15 1 14 5 13 2 10 2 15 8 15 3 14 7 14 1 12 9 10 0 15 4 14 9 14 3 13 8 12 6 9 7 15 1 14 5 14 0 13 5 12 3 9 5 14 7 14 2 13 7 13 2 12 0 9 3 14 4 13 9 13 4 12 9 11 8 9 1 14 1 13 6 13 1 12 6 11 5 8 9 12 9 12 5 12 0 11 5 10 5 8 2 11 9 11 5 11 1 10 7 9 7 7 5 11 2 10 8 10 4 10 0 9 1 7 1 10 5 10 2 9 8 9 4 8 6 6 7 10 0 9 6 9 3 8 9 8 2 6 3 9 5 9 2 8 9 8 5 7 8 6 0 9 1 8 8 8 5 8 2 7 4 5 8 8 8 8 5 8 2 7 8 7 1 5 5 8 4 8 2 7 9 7 5 6 9 5 3 8 2 7 9 7 6 7 3 6 7 5 2 7 9 7 6 7 3 7 1 6 4 5 0 7 7 7 4 7 1 6 8 6 3 4 8 7 4 7 2 6 9 6 7 6 1 4 7 7 2 7 0 6 7 6 5 5 9 4 6 7 1 6 8 6 6 6 3 5 8 4 5 6 3 6 1 5 9 5 6 5 2 4 0 5 8 5 6 5 4 5 2 4 7 3 6 5 0 4 8 4 6 4 5 4 1 3 2 4 5 4 3 4 2 4 0 3 6 2 8 4 1 3 9 3 8 3 6 3 3 2 6 3 8 3 6 3 5 3 4 3 1 2 4 3 5 3 4 3 3 3 2 2 9 2 2 3 3 3 2 3 1 3 0 2 7 2 1 3 2 3 1 2 9 2 8 2 6 2 0 2 3 2 1 1 6 1 8 1 4 please refer to the microdata documentation Statistics Canada Catalogue no 56M0002GIE O0 1o No MN QJ i UI Oy OO H2 U OY Oy OO xo O H INN U UI OY O HP S IN U iS UI OY O 0 HU OPR ON Oy HP UI UI o ON OO O O O HP H H H H H H N N N N N
51. 8 5 7 4 KKK ke ke hee ke ke he ek ke hee ke ke eee ke eee ke eee e ee 14 7 14 3 13 9 13 4 13 0 12 5 12 0 11 0 8 5 4 9 5 13 2 12 8 12 4 12 0 11 6 11 2 10 7 9 8 7 6 4 4 6 ke e he e e e he eee He e he He ke e he e He e he ke ke e he ke ke k e ke ke k e kk k k k 11 7 11 3 11 0 10 6 10 2 9 8 8 9 6 9 4 0 7 ke e He e e he he e He e ke ke e e ke e He e ke ke ce e ke ke k e k k k e kk kk k 10 8 10 5 10 1 9 8 9 4 9 1 8 3 6 4 3 7 8 10 1 9 8 9 5 9 2 8 8 8 5 7 8 6 0 3 5 9 9 2 8 9 8 6 8 3 8 0 7 3 5 7 3 3 10 KKK KKK KKK KK He He de KKK RK RK KKK e e EKER RAKE e He e de He KEK EKER 8 8 8 5 8 2 7 9 7 6 6 9 5 4 3 1 11 8 1 7 8 7 5 7 2 6 6 5 1 3 0 12 7 8 7 5 7 2 6 9 6 3 4 9 2 8 13 7 4 7 2 6 9 6 7 6 1 4 7 2 7 14 KKK E e KK KKK KKK ek RK RK RIK ek RK de e He RK KARR KEKE e He He e e He He e e He k eke e k kek k 6 9 6 7 6 4 5 9 4 5 2 6 15 KR KKK KKK KR RK KKK ek RR k e de de KKK e de de k e d
52. 8 7 17 1 13 2 7 6 6 Wee side Je hehe de e ese ehe dede RENAE I 21 5 20 9 20 3 19 7 19 1 18 4 17 8 17 1 15 6 12 1 7 0 7 19 9 19 4 18 8 18 2 17 7 17 1 16 4 15 8 14 4 11 2 6 5 8 Ades EARLE eee RAE EEN 18 6 18 1 17 6 17 1 16 5 16 0 15 4 14 8 13 5 10 5 6 0 9 NICK ek jekekoee ee LER ARREARS 17 5 17 1 16 6 16 1 15 6 15 1 14 5 13 9 12 7 9 9 5 7 10 Vedeeide e ee ehe dee ee de eee eee 16 6 16 2 15 7 15 3 14 8 14 3 13 8 13 2 12 1 9 3 5 4 11 15 9 15 4 15 0 14 6 14 1 13 6 13 1 12 6 11 5 8 9 5 1 12 woe ee eode 15 2 14 8 14 4 13 9 13 5 13 0 12 6 12 1 11 0 8 5 4 9 13 14 6 14 2 13 8 13 4 13 0 12 5 12 1 11 6 10 6 8 2 4 7 14 AN 14 1 13 7 13 3 12 9 12 5 12 1 11 6 11 2 10 2 7 9 4 6 15 13 2 12 8 12 5 19 1 11 7 11 2 10 8 9 9 7 6 4 4 16 12 8 12 4 12 1 11 7 11 3 10 9 10 5 9 5 7 4 4 3 17 c e ke e e e e e e e e e ke e e e ke e ke e ke ee e k e ke e k k e k 12 4 12 1 11 7 11 3 11 0 10 6 10 1 9 3 7 2 4 1 18 KKK ke ke he ek ke he ek ke hee ke ke he ee ke eee ke eee KIRK 12 1 11 7 11 4 11 0 10 6 10 3 9 9 9 0 7 0 4 0 19 11 7 11 4 11 1 10 7 10 4 10 0 9 6 8 8 6 8 3 9 20 KR e ek ke he e ke ke he ek ke hee ke ke hee ke ke ee ke ee e e ke 11 4 11 1 10 8 10
53. E 24 Quebec 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 households 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 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 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide 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 wil
54. He de e He He de e He He e e He He e e He He e e He He e e He ke e e He ke keke ke 6 8 6 6 6 4 6 2 5 9 5 4 4 2 70 FE E e He E de e He ek RK de de He He de RK He de e He He e e He He de e He He de de He He de e He ke e de e kekek k 6 6 6 4 6 2 5 9 5 7 5 2 4 0 75 6 2 5 9 5 7 5 5 5 0 3 9 80 6 0 5 8 5 5 5 3 4 9 3 8 85 5 8 5 6 5 4 5 2 4 7 3 7 90 KKK KKK KKK ek ke KKK RK RK KKK KK RK He He de de RRR KKK e de He KKK ke ee e k kek k 5 6 5 4 5 2 5 0 4 6 3 6 95 5 3 5 1 4 9 4 5 3 5 100 5 1 5 0 4 8 4 4 3 4 125 KKK KKK KK KK che khe ke ek ke eek KK ek de ke e de ke ek RK ke e ke ek e de de ke de de de ke e de KARR de de He k e de de k kek K 4 4 4 3 3 9 3 0 150 KK KKK KKK KK RK KKK KK RK RRR ek RK ke ke k ek de d k kede de k kede de KKK IK ede de k kede de k ede de k ede de k kede k k kk k k kk k kkk k 3 6 2 8 200 KR KKK IKK KKK KKK KK RK ke ke ek ek k k e h k kee de k RK k ede de k
55. K KKK KK RK KKK KK de de He KKK RK RRR RK KK de de He ke e KEKE KERR KK 7 5 7 2 7 0 6 7 6 1 4 7 2 7 45 Foi 6 8 6 6 6 3 5 8 4 5 2 6 50 6 5 6 2 6 0 5 5 4 2 2 4 55 KKK E e ek KKK KKK KK RK RK eek ek RRR KKK RK KKK KKK ERR KKK EKER KKK RK EKER 6 2 6 0 5 7 5 2 4 0 2 3 60 5 7 5 5 5 0 3 9 2 2 65 5 5 5 3 4 8 3 7 2 1 70 5 1 4 6 3 6 2 1 75 KKK KK IKK ek KKK KKK RK RK KKK KKK RIK KK koe eek ek RK koe eek RK ke e de de k e de de k e de de k e de de k e de de k keke k k kk k 4 9 4 5 3 5 2 0 80 POOO OOO OOO O KKK ek ek ke eek ek ke KKK ek RK RK kede d KK RK KKK IK ke de de k e de de k e de de k ede de k kede k k kk k k kk k kkk k 4 3 3 4 1 9 85 PAO OOO O ek RK KKK ek k KKK KKK RK k kede de k ede de k e de de k e KKK e de de k e de de k e de de k e RIK e de de k e de He k ede
56. KK RRR KK 17 6 17 2 DEE EEEE EE KKK KEK 16 8 16 5 KKK KK RRR KR RR KK 16 1 14 7 13 6 12 8 KKK KKK KKK IKI KKK KERRIER IK ke e eee e e ke e e ce ke e e e e ee e e X kkkkkkkkkkkkkkkkkkkkkkkk e ke eee e eee ke e ce e ke e e e ee e v x A kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e e ee e e ce ke ke e e e ee e e X kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e e eee e ce e ee e e ec e v x A kkkkkkkkkkkkkkkkkkkkkk kkk ke e eee e e e ke e e ce e e e e e ec e e X kkkkkkkkkkkkkkkkkkkkkkkk ke e eee ke eee ke e e e ee e e ee e v x A 79 56 45 39 35 32 30 28 26 25 24 22 22 21 20 19 19 18 18 17 17 16 16 16 15 14 13 12 11 11 10 7 OO OO o wo OLUN NOAA UI xo N Oy 0 U OO UJ to UI N O o0 O H Ui H O i Ul J N UI 05 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk ke e ee e e eee e eee e ee ke e e e ke e ce e e ee e e x ke e ee e e eee e eee e ec ke e e ce ke e ce e e ek v v x X kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk ke e ee ke e eee e ee e eee e ee ke e ee ke e ce ke e e e ee e e X e ke e eee e eee e eee e eee e eee e eee e e ce e e ck e e ec v e x X kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkk
57. KKK e ke e ke e ke e ke e ke ke e ke e ke e ke e ke e ke e ke e ke e e e 6 0 5 8 5 6 5 5 5 3 5 1 4 9 4 5 3 5 2 0 85 KKK ke ke he ek ke he ek ke he ek ke ke ee ke ke ee ke ee e e ke 5 8 5 6 5 5 5 3 5 1 4 9 4 7 4 3 3 3 1 9 90 5 6 5 5 5 3 5 1 5 0 4 8 4 6 4 2 3 3 1 9 95 5 3 5 2 5 0 4 8 4 7 4 5 4 1 3 2 1 8 100 5 2 5 0 4 9 4 7 4 5 4 4 4 0 3 1 1 8 125 ke e He ee e he e e ce He e he He ke e hee He e hee k e k ke ke k e k ke k e kk k k e 4 6 4 5 4 4 4 2 4 1 3 9 3 6 2 8 1 6 150 e He e e He e He He e He e He He e He He e He e He He e He He ke He e e He ke ke He ke ke ke ke ke ke ke ke k kk kk k k k 4 1 4 0 3 9 3 7 3 6 3 3 2 5 1 5 200 FEE e He e Fe e He He de e He ke de e He He de e He He de e He He de e He He e e He He e e He He e e He He e e He He e e He k ee e k kek ke 3 5 3 3 3 2 3 1 2 8 2 2 1 3 250 3 0 2 9 2 8 2 5 2 0 1 1 300 KKK KK IKK KR KKK KK KEK RK KARR RK RK KKK KK RK KKK KK e de de ke de KKK RK KKK KEKE KKK EKER REE 2 6 2 5 2 3 1 8 1 0 350 KKK KKK KKK RK RIK KK ke ke ke ek RK RK RIK KK RK RIK KKK h k kede ke ek k kede de deck ke ede de k ede de k kede k k kk k k kk k 2 3 2 1 1 7 1 0 400
58. N N Q U U U U U UQ i A A UI UI UI UI UI UI UI OY OY OY OY O J OO COO o to Numerator of Percentage 000 WODAIHUEPWNHE Note For correct usage of these tables Approximate Sampling Variability Tables Manitoba and Saskatchewan 0 1 1 0 2 0 oko ck v KKK 56 6 56 3 oko kv x x x 40 0 39 8 oko c kv x x x 32 7 32 5 KKKKKKKK 28 3 28 1 okck c v v x x 25 3 25 2 oko cx kv x x x 23 1 23 0 oko c kv x x x 21 4 21 3 KKKKKKKK 20 0 19 9 ke ke e e e ke e e e ee e v x x 18 8 kkkkkkkkkkkkkkkk 17 8 ke ke ce e e e e e e ee e v x X 17 0 kkkkkkkkkkkkkkkk 16 3 KKKKKKKKKKKKKKKK 15 6 kkkkkkkkkkkkkkkk 15 0 ke ke ce e ke e e e e ec e v X 14 5 kkkkkkkkkkkkkkkk 14 1 ke e eee ke e ee ke e e e ee e e e e e v x A kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e ee ke e e ce ke e e e e ee e v X kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e e ee e e ce e ke e e e ee v x A kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e eee e e e ke e e e e ce v e X kkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e e ee e e ce ke e e e e ee e v X ke e eee e ee ke e ce ce ke e ce e e ee v v n X ke ke eee e eee e e e e ee e e ee v x A 5 55 39 32 27 24 22 20 19 18 17 16 16 15 14 14 13 13 13 12 12 12 11 11 11 11 10 9 8 Household Internet Use Survey 2003 User Guide Household Internet Use Survey 0 1 SE kkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
59. RE 16 9 16 4 15 9 15 5 15 0 14 5 13 9 13 4 12 2 45 Mee lee dede e e eoe e dee e eee eee 15 9 15 5 15 0 14 6 14 1 13 6 13 1 12 6 11 5 50 ERR ER 15 1 14 7 14 3 13 8 13 4 12 9 12 5 12 0 10 9 55 See eee eek eee e eek dedeee 14 4 14 0 13 6 13 2 12 8 12 3 11 9 11 4 10 4 60 RARER ERERAEE EA EERE RRR ERE 13 8 13 4 13 0 12 6 12 2 11 8 11 4 10 9 10 0 65 13 2 12 9 12 5 12 1 11 7 11 3 10 9 10 5 9 6 70 12 7 12 4 12 1 11 7 11 3 10 9 10 5 10 1 9 2 75 Melee ee dece 12 3 12 0 11 6 11 3 10 9 10 6 10 2 9 8 8 9 80 Wokecdeiciekck EKER EA ERERARER ERE 11 9 11 6 11 3 10 9 10 6 10 2 9 9 9 5 8 6 85 e e e e ee e e e e e e e e e ec e oe e ke e e ce e 11 3 10 9 10 6 10 3 9 9 9 6 9 2 8 4 90 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 10 9 10 6 10 3 10 0 9 6 9 3 8 9 8 2 95 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 10 6 10 3 10 0 9 7 9 4 9 0 8 7 7 9 100 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 10 4 10 1 9 8 9 5 9 1 8 8 8 5 7 71 125 e e ke e ee e e e ee e kk kkk e e e e 9 3 9 0 8 7 8 5 8 2 7 9 7 6 6 9 150 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 8 5 8 2 8 0 7 71 7 5 7 2 6 9 6 3 200 e e e e e e e e e e he e hee e e e he oe e e e e e e e e e e e e e ec e e e kkk 7 1 6 9 6 7 6 5 6 2 6 0 5 5 250 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 6 2 6 0 5 8 5 6 5 4 4 9 300 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 5 6 5 5 5 3 5 1 4 9 4 5 350 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 5 1 4 9 4 7 4 5 4 1 400 e e k e k k ee k k k
60. User Guide Guidelines for Tabulation Analysis and Release eene 33 9 1 Rounding Guidelines erattu e PR E Resa RENATA ERR MESE eB RRA nMEE ER ee qa 33 9 2 Sample Weighting Guidelines for Tabulation sseseeeneneene 34 9 3 Definitions of Types of Estimates Categorical and Quantitative 34 9 3 1 Categorical Estimates sessi eee entere nnne nes 34 9 3 2 Quantitative Estimates sessssssssssseseeeeee eee eene nnn entrent 34 9 3 3 Tabulation of Categorical Estimates ssssessseseeeee 35 9 3 4 Tabulation of Quantitative Estimates ssssssseeeenene 35 9 4 Guidelines for Statistical Analysis sess 36 9 5 Coefficient of Variation Release Guidelines sse 37 9 6 Release Cut off s for the Household Internet Use 39 Approximate Sampling Variability Tables eeeeeeeeeeeee eren nennen nnns 40 10 1 Howto Use the Coefficient of Variation Tables for Categorical Estimates 41 10 1 1 Examples of Using the Coefficient of Variation Tables for Categorical 42 10 2 Howto Use the Coefficient of Variation Tables to Obtain Confidence Limits 48 10 2 1 Example of Using the Coefficient of
61. Variation Tables to Obtain Confidence Hier eR 49 10 3 Howto Use the Coefficient of Variation Tables to Do a 49 10 3 1 Example of Using the Coefficient of Variation Tables to Do a T test 50 10 4 Coefficients of Variation for Quantitative Estimates 50 10 5 Coefficient of Variation 80 65 51 Ll M 65 11 1 Weighting Procedures for the Labour Force 5 65 11 2 Weighting Procedures for the Household Internet Use 66 PUCCIMNMEICnM 68 12 1 The Labour Force Survey Questionnaire sess 68 12 2 The Household Internet Use Survey Questionnaire sssssssseee 68 Record Layout with Univariate Frequencies eeeeeeeeeeeee eee nnnnnnnnnnnnnnn 69 Statistics Canada Catalogue no 56M0002GIE 5 Household Internet Use Survey 2003 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 pote
62. When supplementary survey records do not match to host survey records they must be dropped since a weight cannot be derived for them Conversely 1 579 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 t
63. ad a new job to start within four weeks from the reference week and were available for work 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 2 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 ii 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 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide Not in the Labour Force Persons not in the labo
64. 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 sections of the questionnaire was used in the score and distance functions to find the donor Records that were manually investigated as possible outliers as described in the section above were excluded from acting as donors during the imputation of electronic commerce variables Only those respondents who were usual users of the Internet from any location were eligible for the e commerce questions In total 6196 of the HIUS r
65. ample of Using the Coefficient of Variation Tables to Do a T test Let us suppose that the user wishes to test at a 596 level of significance 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 Xi X gt 0383 0556 0173 _ L 14 8 0 0117 0 0117 f Since t 14 8 is less than 2 it must be concluded that there is a 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
66. and this estimate is found to have a standard error of 0 00360 Then the coefficient of variation of the estimate is calculated as E X 100 1 296 0 309 There is more information on the calculation of coefficient of variation in Chapter 10 0 32 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 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 8 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 rounding 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 r
67. as 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 person 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 24 4 Forthe HIUS a record was deemed a respondent either complete or partial if a YES response had been obtained to question LUQO2 or to question NUQ01 or failing either of these conditions then a YES or NO response had been given for question NUQO03 Otherwise the record was classified as a non respondent Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide 7 7 Suppression of Confidential Informat
68. as two numbers one below the estimate and one above the estimate as 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 Ch CI x ih Ga a where is the determined coefficient of variation of X and Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide t 1ifa 6896 confidence interval is desired t 1 6 if a 9096 confidence interval is desired t 2if a 95 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 1259 or expressed as a proportion 0 125 t 2 4 0 0 040 expressed as a proportion is
69. at first refuse to participate in the LFS a letter is sent from the Regional Office to the 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 Statistics Canada Catalogue no 56M0002GIE 21 Household Internet Use Survey 2003 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 the 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 2004 HIUS was administered only as a computer assisted teleph
70. clusters the apartment 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 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 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
71. conducted for the seventh time in January 2004 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 2003 survey repeats the detailed module on electronic commerce introduced in 1999 The 2003 survey results showed that e After surging during the late 1990s the growth in Internet use among Canadian households has levelled off e The number of Canadian households surfing the Internet continued to grow in 2003 However growth rates remained relatively stable largely because the majority of households were already plugged in e Anestimated 7 9 million 64 of the 12 3 million Canadian households had at least one member who used the Internet regularly in 2008 either from home work school a public library or another location e Households with high income members active in the labour force those with children still living at home and people with higher levels of education have been in the forefront of Internet adoption e Internet use was highest at home About 6 7 million households had at least one member who regularly used the Internet from home a gain of 7 since 2002 These households accounted for nearly 5596 of the total up from 5196 in 2002 Ofthe nearly 6 7 million
72. crown corporation or a government owned public establishment such as a school or a hospital 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 Statistics Canada Catalogue no 56M0002GIE 15 Household Internet Use Survey 2003 User Guide 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
73. ct usage of these tables please refer to the microdata documentation Statistics Canada Catalogue no 56M0002GIE 90 0 m A m m HD HBbrH B HBAH PbHPNENPNPXSPXSN NN N N N U U QU U i AAAA AAAA UI UI UI UI UI OY O O N OO COO to 57 HH QU OY OO O O H N N U i Ul O 9 O O H 4 OY O O H N U i5 Oy OO O H U UI J O U O O Ui H to to UO oc Numerator of Percentage 000 WDAIHUEPWNHE Note For correct usage of these tables Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2003 Approximate Sampling Variability Tables Saskatchewan 0 1 1 05 2 05 RRR KKK 52 6 52 4 37 2 37 0 KKK KKK 30 4 30 2 DEEE EEE EEEE EE EEE 26 2 23 4 21 4 19 8 KKK KKK KIRK KKK e He e He RIK kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e e ee e e ce e ke e e e ee e v x A kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e e ee ke e ce e ke e e e e e e v x A kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e ee ke e e e ke ee e e ee e v X kkkkkkkkkkkkkkkkkkkkkkkk ke ke eee ke e ee e e e e ke e e e ee e v x A kkkkkkkkkkkkkkkkkkkkkkkk ke e eee e eee e e ce ke e e e e ee e e X 5 51 36 29 25 23 21 19 18 17 16 15 14 14 13 13 12 12 12 11 O NU 00 0O 0 00 0 IDOU I2 2 0000 UO kkkkkkkkkkkkkkkkkkkkkkkk
74. d 5 6 describe how the HIUS departed from the basic LFS design in January 2004 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 fo
75. de k KKK IK de de k e de KE de de k kede de k kk k kkk k 4 5 3 5 2 0 25 KK KKK IKK KK KKK KK KK RK KKK ek ek RK KKK ek RK KKK ek k e RK kede d k kede d KKK IK kede d k e de de k e de de k e de de k ede d k kdk kk kk k kkk k 4 4 3 4 2 0 30 KK KKK KKK KK RRR KK KK RK KKK ek RK RK ke eek KKK RIK ek ek koe ke ek RK KKK ek ek RK RIK KK RK RAK IKK de k ede d k ek k kk k kkk k kkk k 3 1 1 8 35 2 9 1 7 40 KKK KK IKK KK KKK KKK RRR KKK RK ke ke ke ek RK KKK IK RK RK KIRK RK KKK IK ek RIKI KKK RK KKK IK RRR IK KKK RK k de k k k dek k k kk k kkk k kkk k 1 6 45 1 5 Note For correct usage of these tables please refer to the microdata documentation 52 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2003 Approximate Sampling Variability Tables Nova Scotia Numerator of Estimated Percentage Percentage 000 0 15 1 05 2 05 5 05 10 05 15 05 20 05 25 05 30 05 35 05 40 0 50 0 70 0 1 NC de dee 61 2 60 9 60 0 58 4 56 7 55 0 53 3 51 5 49 6 47 7 43 5 33 7 2 HERE Se eee 43 3 43
76. dee ee deve 20 1 19 8 19 2 18 7 18 1 17 6 17 0 16 3 15 7 14 3 11 1 6 4 23 19 6 19 3 18 8 18 3 17 7 17 2 16 6 16 0 15 4 14 0 10 9 6 3 24 dei desee e ee EEN 18 9 18 4 17 9 17 4 16 8 16 2 15 6 15 0 13 7 10 6 6 1 25 jede dew jesse LEER EEE RAEN 18 5 18 0 17 5 17 0 16 5 15 9 15 3 14 7 13 4 10 4 6 0 30 decide 16 9 16 5 16 0 15 5 15 0 14 5 14 0 13 4 12 3 9 5 5 5 35 15 7 15 2 14 8 14 4 13 9 13 4 13 0 12 4 11 4 8 8 5 1 40 ARES ERA ERE EEE 14 7 14 3 13 9 13 4 13 0 12 6 12 1 11 6 10 6 8 2 4 8 45 HEEL ese ehe dede hehe ee deje eee 13 8 13 4 13 1 12 7 12 3 11 9 11 4 11 0 10 0 7 8 4 5 50 13 1 12 8 12 4 12 0 11 6 11 2 10 8 10 4 9 5 7 4 4 3 55 ENS 12 5 12 2 11 8 11 5 11 1 10 7 10 3 9 9 9 1 7 0 4 1 60 ce kc e ke e ke e ke e ke e ke e ke e ke e ke e ke e ke e ke e ke e ke e e e 11 6 11 3 11 0 10 6 10 3 9 9 9 5 8 7 6 7 3 9 65 KKK ek ke he ek ke he ek ke he ek ke hee ke ee ke ee e e ee 11 2 10 9 10 5 10 2 9 9 9 5 9 1 8 3 6 5 3 7 70 KR KKK ke ke ek khe ek khe ek ke hee kc ke e ke ke e e e ke 10 8 10 5 10 2 9 8 9 5 9 2 8 8 8 0 6 2 3 6 75 KKK e ke ke eek ke he ek ke he ek ke ke ee ke eee ke eee e ee 10 4 10 1 9 8 9 5 9 2 8 9 8 5 7 8 6 0 3 5 80 ce kc e ke e ke e ke e ke e ke e ke e ke e ke e ke e ke e ke e ke e ke e e e 10 1 9 8 9 5 9 2 8 9 8 6 8 2 7 5 5 8 3 4 85 9 8 9 5 9 2 8 9 8 6 8 3 8 0 7 3 5 6 3 3 90
77. dered 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 5 Bachelors degree and 6 University degree or certificate above bachelors degree Statistics Canada Catalogue 56M0002GIE Household Internet Use Survey 2003 User Guide 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 HEDUCL 2 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 MEMOO 05 12 MEM13 15 MEM16 17 MEM13 17 MEM18
78. ding 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 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 Rule3 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 xy is where x is estimate 1 2s is estimate 2 and a and are the coefficients of variation of X and X respectively The coefficient of variation of d is given by c d This formula is accurate for the difference between separate and uncorrelated characteristics but is only approximate otherwise Statistics Canada Catalogue no 56M0002GIE 41 Household Internet Use Survey 2003 User Guide Rule 4 Estimates of Ratios In the case where the numerator i
79. e He e e He ke keke e 5 7 5 5 5 3 400 e e e e ke e e He He e e He He e e e He e e He He e e He He e e e ke ke ke e 5 3 5 1 5 0 450 e e e e e e e He He de e He He e e He He e e He He e de He He e de He He keke e 5 0 4 8 4 7 500 4 6 4 5 750 3 6 1 000 KR KKK KKK KK KKK KK KK RK KKK KKK RK KKK KK RK ke dede de k ke de de KKK RK RRR KK 1 500 2 000 3 000 4 000 Note For correct usage of these tables 56 please refer to the microdata documentation 50 0 HHPHHHHHBHHBHHH H HH HH PNNNNN NN N NU U QU i Ul A O O HP H N U i UI OY O COANN O0 OO o tO O HP HF N U i O TON Ul o WU O0 O U tio Ul J tio IN OY O Oy i H 1o HP OY OO P i ONAN O i OO O0 IN P THF 490 00 00 10 U xo 0 IN LWA UI OO NN N OU QU QJ i iS UI UI OY 1 OO OO OO wo WW 1 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk k kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
80. e de ke e de de ke e de He ke e de KKK de de ke ede ke k kk k k kk k 6 7 6 5 6 2 5 7 4 4 2 5 16 6 5 6 2 6 0 5 5 4 2 2 5 17 6 1 5 8 5 3 4 1 2 4 18 KR KKK IKK ek ke KKK ek RK KKK ek RK ke e de ke ke de de k e ke ek e de de ke e de de k e RK KKK He k de de de ke e de de k eee 5 9 5 7 5 2 4 0 2 3 19 5 7 5 5 5 0 3 9 2 2 20 5 4 4 9 3 8 2 2 21 5 2 4 8 3 7 2 1 22 KKK KKK KKK ek KKK KKK RK ke ke ek ek KKK KKK RK KKK IK ek d ke ede RIKI ek e de de k KKK k e de de k KKK EKER RK k kk k kkk k 4 7 3 6 2 1 23 KK KKK KKK KK KKK KKK RK KKK ek ek RK koe ke ek RK KKK IK ek ek KIRK KKK KK RK RK KKK KK KKK KKK EKER KKK k kk k kkk k 4 6 3 5 2 0 24 KK KKK KKK KK KKK KKK RK KKK ek ek KKK KKK RK ke ke ek RK RIK KKK RK k e de de k e de
81. e 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 private organizations conducted over computer mediated networks The goods and services are ordered ov
82. e 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 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 were eight Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide 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
83. ed 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 2003 the HIUS used a population projection based on the 1996 Census of population The 1997 and 1998 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 Statistics Canada Catalogue no 56M0002GIE 11 Household Internet Use Survey 2003 User Guide Regular user Households with at least one person that uses the Internet in a typical month regardless of whether that use was from home work school a public library or some other location These
84. ede de k e de de k e de de k e de de k e de de k e de de k e de k kekk k kkk k kkk k 2 4 250 PAO ek ke khe KK RK ke ke ek ek O KK RK KKK ek ek ek e ke k kde d k kede de k kede de k ede de k ke de de k e de de k ede de k kede de k kede k k kk k kkk k kkk k 2 1 300 PAO KKK KKK KKK RK KKK ek RK RK ke k k RK k e d k kde d k kede dk ede de k ede de k ede de k e de de k e RK de de de he ke de de de ke de ke k kkk k kkk k Note For correct usage of these tables please refer to the microdata documentation Statistics Canada Catalogue no 56M0002GIE 90 0 BPR w o HDnp bH B bH BAHB PbHPNPNPKSPXNSPKPXSP PSN NN N N U U U U i d ds ds uS ui aS UI UI UI UI UI OY OY O J OO oO 53 HN 4 Oy 10 O H H N N U i UCA O 0 HP U OY 1 0 O HP H N 5 UI O X0 O N AOON Ui to 5 10 DOUM Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2003 Approximate Sampling Variability Tables New Brunswick Numerator of Estimated Percentage Percentage 000 0 15 1 05 2 05 5 05 10 05 15 05 20 05 25 05 30 05 35 05 40 0 50 0 70 0 90 05 1 dex 53 7 53 4 52 6 51 2 49 8 48 3 46 7 45 2 43 5 41 8 38 2 29 6 17 1 2 HERE 38 0 37 8 37 2 36 2 35 2 34 1 33 0 31 9 30 8 29 6 27 0 20 9 12 1 3 HRERRRRARERER EEE 30 8 30 4 29 6 28 7 27 9 27 0 26 1 25 1 24 1 22 0 17 1 9 9 4 dese 26 7 26 3 25 6 24 9 24 1 23 4 22 6 21 8 20 9 19 1 14 8 8 5 5 eoe 23 9 23 5 22 9 22 3 21 6 20 9 20 2 19 5 1
85. ee at 1 800 263 1136 Statistics Canada Science Innovation and Electronic Information Division Household Internet Use Survey Microdata User s Guide 2003 Published by authority of the Minister responsible for Statistics Canada Minister of Industry 2004 All rights reserved No part of this publication may be reproduced stored in a retrieval system or transmitted in any form or by any means electronic mechanical photocopying recording or otherwise without prior written permission from Licence Services Marketing Division Statistics Canada Ottawa Ontario Canada K1A OT6 September 2004 Catalogue no 56M0002GIE ISSN 1712 3704 Frequency Annual Ottawa La version fran aise de cette publication est disponible sur demande n 56M0002GIF au catalogue Note of appreciation Canada owes the success of its statistical system to a long standing partnership between Statistics Canada the citizens of Canada its businesses governments and other institutions Accurate and timely statistical information could not be produced without their continued cooperation and goodwill Household Internet Use Survey 2003 User Guide Table of Contents 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 Introduction elc 6 arete Cogolla Mc 7 Aa EDD 9 Concepts and Definitions leeeeeeeeeeeeeeiees sisse ee eneeee enne
86. eeseeeeeseeeetaeeneeeeee 21 6 2 Supervision and Quality Control sssssesseesseeeerennene enne 21 6 3 Non response to the Labour Force Survey sssssssssssseeneennernnns 21 6 4 Data Collection Modifications for Household Internet Use Survey 22 6 5 Non response to the Household Internet Use 22 D ta PROCESSING faeries nie reino eene eiie utet i eect equis crx sade seme du eee Ee cheat deter ede 23 7 1 b irWe lm ERR 23 7 2 zi p E 23 7 8 Coding of Open ended Questions sesssssssssssessssseeeeenneene nennen 23 7 4 TR 24 7 5 Creation of Derived Variables sss enne nennen nentes 24 7 6 p EE 24 7 7 Suppression of Confidential Information sssssssssesssseeeeeee eene 25 Data Quality DM PD 26 8 1 Response Rales RE item 26 8 2 S NMEY murem MM M EE 26 8 2 1 The Frame ONE D NOE UOTE 27 8 2 2 BERE EMT 27 8 2 3 Data ed dtr dedere aaa aS 28 8 2 4 INON FESPONSC ET 28 8 241 ImputatiOh uite car dad ete 29 8 2 5 Measurement of Sampling Error 31 Statistics Canada Catalogue no 56M0002GIE 9 0 10 0 11 0 12 0 13 0 Household Internet Use Survey 2003
87. el Rule 4 in Section 10 1 and Example 4 in Section 10 1 1 explains the correct procedure to be used for ratios Province and Region Newfoundland and Labrador Prince Edward Island Nova Scotia New Brunswick Quebec Ontario Manitoba Saskatchewan Alberta British Columbia Atlantic Provinces Prairie Provinces Manitoba and Saskatchewan Canada Acceptable CV Marginal CV 0 0 to 16 5 16 6 to 33 3 3 3300 Statistics Canada Catalogue no 56M0002GIE Unacceptable CV 33 396 under 2 700 under 900 39 Household Internet Use Survey 2003 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 pe
88. en answered For this type of error a non response or not stated code was assigned to the item 7 3 Coding of Open ended Questions 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 Statistics Canada Catalogue no 56M0002GIE 23 Household Internet Use Survey 2003 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
89. er these networks but the payment and ultimate delivery of the goods or services may be conducted on line or off line Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 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 nternet 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 browsing the Internet is a commonly used phrase which refers to the activity
90. eristics 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 1 to 3 times per month Less than once per month Q What is your best estimate of the total income before taxes and 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 Between 10 000 14 999 Between 15 000 19 999 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 Y where X is an estimate of surveyed population quantity total and Y is an estimate the number of households in the surveyed population contributing to that total quantity Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide An examp
91. esign efficient for estimating month to 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 Statistics Canada Catalogue no 56M0002GIE 19 Household Internet Use Survey 2003 User Guide 20 5 5 Modifications to the Labour Force Survey Design for the Household Internet Use Survey The HIUS used four of the six rotation groups in the January 2004 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 1 295 Prince Edward Island 869 Nova Scotia 2 011 New Brunswick 1 898 Quebec 6 581 Ontario 10 031 Manitoba 2 532 Saskatchewan 2 583 Alberta 3 540 British Columbia 3 334 Canada 34 674 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Surv
92. espondents were eligible for the e commerce section Of those eligible 6 0 needed at least one of the e commerce fields to be imputed 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 Statistics Canada Catalogue no 56M0002GIE 31 Household Internet Use Survey 2003 User Guide 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 example suppose that based upon January 2003 results 2002 reference year 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
93. etained 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 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 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 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 In instances where due to technical or other limitations a rounding technique other than normal 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 reas
94. ey 2003 User Guide 6 0 Data Collection Data collection for the Labour Force Survey 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 8596 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 infor
95. he survey or not enough information was collected in the interview Total non response was handled by adjusting the weight of households that responded 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 With the exception of income and electronic commerce e commerce items item non response was very low for the HIUS Most questions had non response rates which were less than 1 0 Prior to imputation but during the processing of survey responses for electronic commerce variables several suspiciously large values were identified using several outlier detection methods The automated reports generated for these records were subjected to a manual review by a team of subject matter experts and survey methodologists A few values that appeared to be the result of reporting errors or of data capture errors were manually corrected during this review Values that were highly inconsistent with other reported information were set to missing and were treated by means of donor imputation described below The remaining values were deemed plausible but were winsorized so that their weighted contributions to the estimates were reduced Altogether about a dozen records were identified as needing Statistics Canada Catalogue no 56M0002GIE
96. iS UI UI UI Oy HJ OO 0 1 0 WO 0 35 0 PRPRPRPRPRPRPRPRPRPRPRPRPRPENNNNNNNNNNNWWWW iS iiS UI OY OO O O H H N N U 5 UI OY O O0 OO 10 0 O O H H N U iS AOAN O0 O H i O O UI H U Q9 00 O NM Ui O0 NM J 4 U O O N UI O PH 5 J N O P ANN 5 O 5 0 N O0 P 0 N O UI N O 0 O HP 5 O 0 O J N O 1 0 O m NNUA 4 uS uS UI UI Oy J OO 10 WWW O 40 0 PRPRPRPRPRPRPRPRPRPRPRPRPRPRPRENNNNNNNNNNWWWW BBO 0 COORPRPNNWHRUATTDDADWDMWOWOOKRFPKHKFNWHRBHAIWDONUDOWWOF O MN OM wo HU OY O UI H HP Oy 10 HP 4 OLUN DNAN ON OY OO U Oy O iS OVO i O OY QU HP O o HP U O Ul J 0 O N OPA N NM UU Q s d uS UI UI Oy 1 OO OO WO Percentage 000 0 15 1 05 2 05 5 05 10 05 15 05 20 05 1 111 5 111 0 110 4 108 7 105 8 102 8 99 8 2 78 8 78 5 78 1 76 9 74 8 72 7 70 5 3 64 4 64 1 63 8 62 8 61 1 59 4 57 6 4 55 7 55 5 55 2 54 4 52 9 51 4 49 9 5 WORK dede 49 6 49 4 48 6 47 3 46 0 44 6 6 ERAN 45 3 45 1 44 4 43 2 42 0 40 7 7 FERRER EE 41 9 41 7 41 1 40 0 38 9 37 7 8 PERE ENN 39 2 39 0 38 4 37 4 36 4 35 3 9 37 0 36 8 36 2 35 3 34 3 33 3 10 HERE RNA 35 1 34 9 34 4 33 5 32 5 31 5 11 FERRE EEE 33 5 33 3 32 8 31 9 31 0 30 1 12 HERKEN HH 32 0 31 9 31 4 30 5 29 7 28 8 13 WI ek dew dex 30 8 30 6 30 2 29 3 28 5 27 7 14 HERA 29 7 29 5 29 1 28 3 27 5 26 7 15 FERRER NE 28 7 28 5 28 1 27 3 26 6 25 8 16 EEN ee 27 7 27 6 27 2 26 5 25 7 24 9 17 ERR dew dex 26 9 26 8 26 4 25 7 24 9 24 2 18 HERG 26 2 26 0 25 6 24 9 24 2 23 5 19 25 5
97. interest by the final weight for each record then summing this quantity over all records of interest For example using the January 2003 data 2002 reference year 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 XIY the numerator x is calculated as for a quantitative estimate and the denominator 7 is 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 Statistics Canada Catalogue no 56M0002GIE 35 Household Internet Use Survey 2003 User Guide 36 Y final weights of all records with CMQ02 1 then divide estimate a by estimate b to obtain X Y a estimate the total number of orders X as described above b estimate the number of households in this category by summing the 9 4 Guidelines for Statistical Analysis The Household Internet Use Survey is based upon a complex sample design
98. ion 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 the 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 Statistics Canada Catalogue no 56M0002GIE 25 Household Internet Use Survey 2003 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 2004 Household Household Household HIUS Response Rate Response Rate Response Rate Responding for Full LFS for LFS Rotations for HIUS Households 3 4 5 and 6 January 2004 96 98 98 amp 897 578
99. iring 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 A nearest neighbour imputation procedure was used to find donors from which data were transferred to the records 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 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 Statistics Canada Catalogue no 56M0002GIE 29 Household Internet Use
100. k 31 8 31 6 31 1 30 3 29 4 28 6 27 7 26 7 14 e de e de de deke ke 30 6 30 5 30 0 29 2 28 4 27 5 26 7 25 7 15 RRKKKKKE 29 6 29 4 29 0 28 2 27 4 26 6 25 7 24 9 16 28 6 28 5 28 1 27 3 26 5 25 7 24 9 24 1 17 RRKKKKKE 27 8 27 6 27 2 26 5 25 7 25 0 24 2 23 4 18 e de ke de ke keke e 27 0 26 9 26 5 25 7 25 0 24 3 23 5 22 7 19 RRKKKKKE 26 3 26 2 25 7 25 1 24 4 23 6 22 9 22 1 20 25 6 25 5 25 1 24 4 23 7 23 0 22 3 21 5 21 25 0 24 9 24 5 23 8 23 2 22 5 21 8 21 0 22 e de e de de deke ke 24 4 24 3 23 9 23 3 22 6 22 0 21 3 20 5 23 RRKKKKKK 23 9 23 8 23 4 22 8 22 1 21 5 20 8 20 1 24 23 4 23 3 22 9 22 3 21 7 21 0 20 4 19 7 25 22 9 22 8 22 4 21 8 21 2 20 6 19 9 19 3 30 kikki kkk 20 9 20 8 20 5 19 9 19 4 18 8 18 2 17 6 35 e he he e e He he He 19 3 19 0 18 5 17 9 17 4 16 9 16 3 40 e He he He e He he He e Ae e He He ke He Ke 18 0 17 7 17 3 16 8 16 3 15 8 15 2 45 e e he e he e he He e e e Ae e ke e Ke 17 0 16 7 16 3 15 8 15 4 14 9 14 4 50 e He he He e He he Ae e Ae de Ae He ke e Ke 16 1 15 9 15 4 15 0 14 6 14 1 13 6 55 e de he e e He he He e e he He e ie e e 15 4 15 1 14 7 14 3 13 9 13 4 13 0 60 e he he He e He he He e Ae e Ae He ke He Ke 14 7 14 5 14 1 13 7 13 3 12 9 12 4 65 13 9 13 5 13 2 12 8 12 4 11 9 70 13 4 13 1 12 7 12 3 11 9 11 5 75 K kk kk kk kk kk He He KKK KK KKK KKK 13 0 12 6 12 3 11 9 11 5 11 1 80
101. k kkkk ok e x x kkkk kkkk kkkk kkkk kk e x kkkk 0 6 0 3 8000 kkkk kkkk kkkk ok e x x kkkk kkkk kkkk kkkk kkkkkk kkkk 0 6 0 3 9000 kkkk kkkk kkkk kc e x kkkk kkkk kkkk kkkk kkkkkk kkkk kkkk 0 3 10000 kkkk kkkk kkkk kc e x kkkk kkkk kkkk kkkk kkkkkk kx x kkkk 0 3 Note For correct usage of these tables please refer to the microdata documentation 4 So the approximate coefficient of variation 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 Statistics Canada Catalogue no 56M0002GIE 43 Household Internet Use Survey 2003 User Guide Example 2 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 NUQO3 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 numerato
102. kde RARER ERE 23 5 23 1 22 5 21 9 21 2 20 5 19 8 19 1 18 4 16 8 13 0 8 NICK LEARNER eee ee 22 0 21 6 21 0 20 5 19 8 19 2 18 6 17 9 17 2 15 7 12 1 9 NICK ek dekekooe eese 20 4 19 8 19 3 18 7 18 1 17 5 16 9 16 2 14 8 11 5 10 ede Se ehe he e ede ee dede 19 3 18 8 18 3 17 7 17 2 16 6 16 0 15 4 14 0 10 9 11 18 4 17 9 17 4 16 9 16 4 15 8 15 3 14 7 13 4 10 4 12 oe ee eese 17 7 17 2 16 7 16 2 15 7 15 2 14 6 14 0 12 8 9 9 13 dee EERE 17 0 16 5 16 0 15 6 15 1 14 6 14 0 13 5 12 3 9 5 14 ede ee dee he ERG RENE 16 3 15 9 15 5 15 0 14 5 14 0 13 5 13 0 11 9 9 2 15 15 8 15 4 14 9 14 5 14 0 13 6 13 1 12 5 11 5 8 9 16 deis desee ke eee ee ee deese 15 3 14 9 14 5 14 0 13 6 13 1 12 6 12 1 11 1 8 6 17 RENEE 14 8 14 4 14 0 13 6 13 2 12 7 12 3 11 8 10 8 8 3 18 Mese seid Se ehe she e ee ede dede ehe de eee eee 14 4 14 0 13 6 13 2 12 8 12 4 11 9 11 5 10 5 8 1 19 elei deiedeteeseeededescieiede 14 0 13 7 13 3 12 9 12 5 12 0 11 6 11 1 10 2 7 9 20 13 7 13 3 12 9 12 5 12 1 11 7 11 3 10 9 9 9 7 7 21 Ne dedes ode eeiodeieiek 13 3 13 0 12 6 12 2 11 9 11 5 11 0 10 6 9 7 7 5 22 s ke e he ee e he ee e he he ke He e e ke ke He he ke k e k k k k k k 12 7 12 3 12 0 11 6 11 2 10 8 10 4 9 5 7 3 23 c e ke e e e e e ee ke e ee ke e e e ke e e e ke e k kk kk kk k 12 4 12 1 11 7 11 3 10 9 10 5 10 1 9 3 7 2 24
103. ke e ke e ke KAKA KK KEK 13 0 12 6 12 2 11 9 11 5 11 0 10 6 9 7 7 5 4 3 17 KKK e ke ke ke e ke ke ke ek khe ek kc ke e ke ke ke koe e ke 12 6 12 2 11 9 11 5 11 1 10 7 10 3 9 4 7 3 4 2 18 12 2 11 9 11 5 11 2 10 8 10 4 10 0 9 1 7 1 4 1 19 KR e e ke ke ke e ke ke he ek ke ke ek ke ke e ek ke ee ke ke e e ee 11 9 11 6 11 2 10 9 10 5 10 1 9 7 8 9 6 9 4 0 20 11 3 11 0 10 6 10 2 9 9 9 5 8 7 6 7 3 9 21 He He e He He e He He e He e He He e He He e He e ke He e ke He e ke ke ke ke ke ke ke k kek kk k k k 11 0 10 7 10 3 10 0 9 6 9 3 8 4 6 5 3 8 22 ke e He ee He e ee ce He e he He ke e e ke He ke ke ke ke ke k ke ke k k ke k k k kk k k k 10 8 10 4 10 1 9 8 9 4 9 0 8 3 6 4 3 7 23 10 5 10 2 9 9 9 6 9 2 8 8 8 1 6 3 3 6 24 KKK e ke e ke e ke e ee ke e ee ee ee ke e ee ke e hee ke e ke e ke e ke ke ee 10 3 10 0 9 7 9 4 9 0 8 7 7 9 6 1 3 5 25 10 1 9 8 9 5 9 2 8 8 8 5 7 7 6 0 3 5 30 8 9 8 7 8 4 8 1 7 7 7 1 5 5 3 2 35 8 3 8 0 7 7 7 5 7 2 6 5 5 1 2 9 40 KR KKK KKK KK R
104. kes the estimate releasable with no qualifications 10 2 How to Use the 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 95 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 difference 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
105. kk c ke e eee e eee e eee e ee ke e ee ke e e e e ee e v X kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk e ke ke ee ee eee e eee ke e e e ee e e ke e e e ee e v x A kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk ke e eee e eee e eee e ee ke e ce e ke e ce e e ec e v x kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk c ke e ee e e eee e e eee e e e ke e ce e ee e ee v x A kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk e ke e ee e e eee e eee e ee oe e ee ke e ee ke e e e ee e v X kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkk ke e ee e e ee ke e eee e ee ke e eee e eee e e e e e e ke ke e e e ee e e X kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk please refer to the microdata documentation 10 0 H HHHHHHHHHH HBHBGHBp H HB H NN N NU U UI OPn H bBHBHBNN NM UU Ui UL UL O 1 0 t0 O N J H OW o OY OY Oy J J J OO MW O NM 5 J O QU OY O
106. kk ke e x ok e e x x kkkkkk 4 8 4 4 4 2 4 1 3 7 2 9 1 450 kk e x ck e x kc e x kkkkkk ok ke x 4 6 4 1 4 0 3 8 3 5 27 1 6 500 kk e x ck e x kkkkk kkkkkk kkkkkk kc ke x x 3 9 3 8 3 6 3 3 2 6 1 5 750 kk e x ck e x kc e x kkkkkk ke e x x kkkkkk 3 3 1 3 0 2 7 2 1 1 2 1000 kkkkk kkkkk kkkkk kkkkkk kkkkkk kkkkkk kkkkkk 2 7 2 2 3 1 8 1 1500 kk e x kk e x kkkkk kkkkkk kkkkkk ck v e kkkkkk kk e x x ck kv e 1 9 1 5 0 9 2000 kk e x kk e x ck e x kk e x x kkkkkk kk kv v dk ke v x kkkkkk kk kv x kkkkkk 1 3 0 7 Note For correct usage of these tables please refer to the microdata documentation Statistics Canada Catalogue no 56M0002GIE 45 Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2002 Approximate Sampling Variability Tables Ontario Estimated Percentage Numerator of Percentage 900 0 1 1 0 2 096 5 0 10 096 15 0 30 0 35 0 40 0 50 0 70 0 90 0 1 100 5 100 0 99 5 98 0 95 4 84 1 1 1 77 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 58 0 57 8 57 5 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 PERNE 44 7 44 5 43 8 2 7 1 5 7 6 6 3 34 8 1 8 24 6 14 2 60 Saa e 12 8 12 7 12 3 12 0 10 9 10 5 10 1 9 2 7 1 4 1 65 agi ue 12 3 12 2 11 8 11 5 10 4 10 1 9 7 8 8 6 8 3 9 70 ER eee 11 9 11 7 11 4 11 1 10 1 9 7 9 3 8 5 6 6 3 8 75 PUMA eee 11 5 11 3 11 0 10 7 9 7 9 4 9 0 8 2 6 4 3 7 8
107. kkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk ke e eee e eee e ee ke e ee ke e ce e ke e e e e ee e e X kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk e ke e ee e e eee e eee ke e e e ke e ce e ee e e ee e v x A e ke e ee e e eee e eee e ee ke e ce ke e ce e e ee e v x ke e eee e eee e oec e e ee ke e ce e ke ce e e e v v x X kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk ee e e eee e ee e e eee e eee ee e ee ke e e ce ke e ce e e e kv e n X kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk c ke e ee e e ee ke e eee e eee e eee e ee ke e ee ke e ee ke e ee ke e ke e e e ee e e x kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 4 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkk
108. kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 10 000 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk Note For correct usage of these tables please refer to the microdata documentation 64 Statistics Canada Catalogue no 56M0002GIE WWE i UI UI OY H OO O N Ui UI Oy OO to IH U OLU i UAN OO 1 0 HA j IN i OY 10 INN UI O O H O O iH U Ul ONM OO HP Ul i o iS Oy i 9 IN 00 O O O O O O O O O O H H H H H H H N N N N UJ Q QU Q U UJ U AAAA dS UI UI OY OY OY Oy 1 OO OO OO Oo WW Household Internet Use Survey 2003 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 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 population Each record must be weighted by the inverse of the
109. kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 62 10 0 HHHHHH H HH H H H H H H HH NN N N N NN N N UO U UO i Ui 1 OOOHN UU U UO O O I 1 0 OQ 0 O O H N O is DNN 5 WOTONBHDONHDOKRMWOUNFRFPUADAFUOWANAWOTDHRWWUDWNHA HATA 2 0 OY OY 1 1 00 OO OO OO 5 15 0 75 53 43 37 33 30 28 26 25 23 22 21 20 20 19 18 18 17 17 16 16 16 15 15 15 13 12 11 11 10 10 4 X Ui OY O J J J O0 OO OO 1 1 WOWRAUTONBHAOWAIRFPANOAATAOWAIOBONAINOBRFPATTIOHKFPABRADABHEDND 20 0 72 51 42 36 32 29 27 25 24 23 22 21 20 19 18 18 17 17 16 16 15 15 15 14 14 13 12 11 10 10 o J J J OO OO OO o OY xo IN OY N O Ui UJ UI J ANOR OO U x0 UI UJ U Oy xo N MOLWN IN 1 INN O UI I9 O O H U 00 OY O Oy UI HP Estimated Percentage 25 0 30 0 35 0 40 0 50 0 70 0 90 0 70 6 68 2 65 7 63 1 57 6 44 6 25 49 9 48 2 46 5 44 6 40 8 31 6 18 40 8 39 4 37 9 36 5 33 3 25 8 14 35 3 34 1 32 9 31 6 28 8 22 3 12 31 6 30 5 29 4 28 2 25 8 20 0 11 28 8 27 8 26 8 25 8 23 5 18 2 10 26 7 25 8 24 8 23 9 21
110. kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 70 0 61 43 35 30 27 24 23 21 20 19 18 17 16 16 15 15 14 14 14 13 13 13 12 12 12 11 10 9 H4 OY 30 IN FH U UI to U O Ui H Q i OY O HP U Oy ON Oy HP J U NN NS UI J O QU J OR QOO QJ OO QU 10 Oy i WUA OY PP i000 01 UC IN S 9 8 8 7 7 7 7 6 6 6 6 6 5 5 4 3 3 3 3 2 2 2 1 1 1 1 90 0 PRPRPRPRPRPRERENNW CORPFRNWAUNIO AH DANDNWDOKFWHAAIDWDWOONUONUAHAATIWDORFNPAHAWOWHDOHKRKFPNBUTORPWHWOFRFRANANAAUWHRAD SL OW O O O O H H H H H H N N N N U U Q QU QJ UJ AAAA uS UI UI UT OY OY 1 1 1 OO OO OO OO wowo Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide Household Internet Use Survey 2003 Approximate Sampling Variability Tables Manitoba Numerator of Estimated Percentage Percentage 000 0 15 1 05 2 05 5 05 10 05 15 05 20 05 25 05 30 05 35 05 40 0 50 0 70 0 1 ERR ERNE 62 4 62 1 61 2 59 5 57 8 56 1 54 3 52 5 50 6 48 6 44 4 34 4 2 ede ede Se eee 44 1 43 9 43 2 42 1 40 9 39 7 38 4 37 4 35 8 34 4 31 4 24 3 3 wdedeiedesede 36 0 35 9 35 3 34 4 33 4 32 4 31 4 30 3 29 2 28 1 25 6 19 8 4 Suv ERNE 31 2 31 1 30 6 29 8 28 9 28 1 27 2 26 2 25 3 24 3 22 2 17 2 5 NOR Kdeee eode eee 27 8 27 3 26 6 25 9 25 1 24 3 23 5 22 6 21 7 19 8 15 4 6 Mese ede je hehehe e dee ee dee 25 4 25 0 24 3 23 6 22 9 22 2 21 4 20 7 19 8 18 1 14 0 7 jede
111. kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk 58 please refer to the microdata documentation 10 0 50 35 29 25 22 20 19 17 16 15 15 14 13 13 13 12 12 11 11 11 11 10 10 10 10 9 8 NONUAIONUANUOKHUOUrFPWOAHNOUBRFRFOUN 5 Estimated Percentage 15 0 48 34 28 24 21 19 18 17 16 15 14 14 13 13 12 12 11 11 11 10 10 10 10 10 9 6 OY J 1 OO DOW 1 IN 10 O0 O N i AON UI O0 NM OY O Ui HP J AWUN AR 10 00 BN UO 20 0 47 33 27 23 21 19 17 16 15 15 14 13 13 12 12 11 11 11 10 10 10 10 5 J OO OO b O40 H4 HUI O O UI J HU O ONU 0 N O H J U O O0 J X0 0 IN WW UW 25 05 45 8 44 32 4 31 26 5 25 22 9 22 20 5 19 18 7 18 17 3 16 16 2 15 15 3 14 14 5 14 13 8 13 13 2 12 12 7 12 12 2 11 11 8 11 11 5 11 11 1 10 10 8 10 10 5 10 10 2 10 0 9 8 9 6 9 4 9 2 8 4 7 7 7 2 6 8 6 5 6 2 5 9 5 7 5 5 5 3 5 1 5 0 4 8 4 7 2JO is 000000 DH WW iJ a uU UI UI UI Ul OY OY O J 30 0 4 35 0 HHHHHIHHHHHBHH N NU COPRRPNNWHAUDIOFP BON OWFUHWAORFWUAWAOBAINAUTORPWUArFRPWAIOKhOWOUNnNrRF BREWOOD m Q9 ui a uS UI
112. l be determined only on the basis of 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 rounded weighted 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 Statistics Canada Catalogue no 56M0002GIE 37 Household Internet Use Survey 2003 User Guide 38 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 0 to 16 5 No warning is required Estimates have a sample size of 30 or more and high coefficients of variation in the range of 16 696 to 33 396 Estimates should be flagged with the letter M or some similar identifier They should be accompanied by a warning to caution subsequen
113. lds in each group increased just over 11 e Also the higher the level of education in the household the more likely it is to have an Internet connection from home Nearly 7796 of households with someone with a university degree were connected from home e In contrast only about 12 of households in which the highest level of educational attainment was less than high school were connected from home However households with high school attainment grew fastest e Internet use from home increased in most provinces in 2003 The highest rates of use were in British Columbia Ontario and Alberta where roughly 6 out of every 10 households were connected to the Internet at home All the other provinces had rates of Internet use from home that were below the national average of 55 e Some of the biggest proportional increases occurred in the Atlantic provinces In Nova Scotia for example the proportion of households connected to the Internet from home increased from 46 in 2002 to nearly 53 last year The gain in New Brunswick was from 37 to nearly 43 e Ofthe 7 9 million households in census metropolitan areas about 58 or 4 6 million were connected to the Internet from home in 2003 just above the national average This was an increase from 55 in 2002 e In 2003 809 000 households indicated that a member of the household either used the Internet infrequently or had pulled the plug entirely The size of this group had remained constant for
114. le 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 LLLI 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 0 then C dividing estimate a by estimate b ix Y 9 3 4 Tabulation of Quantitative Estimates Estimates of quantities can be obtained from the microdata file by multiplying the value of the variable of
115. mation about all household members is usually obtained from one knowledgeable household member 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
116. me 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 example 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 2004 8 2 2 Data Collection Interviewer training consisted of reading the HIUS Procedures Manual practicing with the HIUS training cases on the 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 week of January 18 to February 7 2004 Statistics Canada Catalogue no 56M0002GIE 27 Household Internet Use Survey 2003 User Guide 28 8 2 3 Data Processing During processing of the data 97 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
117. nd a 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 46 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide V 0 383X0 027 P 0 556 0 010 0 0001069 0 0000309 0 0117 3 The coefficient of variation of d is given by oj d 0 0117 0 173 0 068 4 So the approximate coefficient of variation of the difference between the estimates is 6 8 which 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 numbe
118. ntial The Household Internet Use Survey HIUS was conducted for the seventh time in January 2004 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 e commerce from home were provided With 2003 data users can study the growth of e commerce by tracking orders purchases or use of Internet that influence acquisition 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 http 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 6 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide 2 0 Background The 2003 Household Internet Use Survey HIUS was
119. 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 households single family households without unmarried children under the age of
120. 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 HIUS questionnaire was used in January 2004 to collect the information for the supplementary survey The file HIUS2003 QuestE pdf contains the English questionnaire 68 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide 13 0 Record Layout with Univariate Frequencies See HIUS2003_CdBk pdf for the record layout with univariate counts Statistics Canada Catalogue no 56M0002GIE 69
121. on for such differences in the publication or release document s Under no circumstances are unrounded estimates to be published or otherwise released by users Unrounded estimates imply greater precision than actually exists Statistics Canada Catalogue no 56M0002GIE 33 Household Internet Use Survey 2003 User Guide 34 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 microdata file for the HIUS 9 3 1 Categorical Estimates Categorical estimates are estimates of the number or percentage of the surveyed population possessing certain charact
122. one interview 6 5 Non response to the Household Internet Use Survey For households responding to the LFS the next stage of data collection was to administer the Household Internet Use Survey In total 34 674 households were eligible for the supplementary survey the HIUS interview was completed for 23 113 of these households for a response rate of 66 7 More detailed information on response rates is presented in Chapter 8 0 Data Quality 22 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 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
123. r 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 percentage closest to it 15 096 4 The figure at the intersection of the row and column used namely 4 0 is the coefficient of variation to be used 5 So the approximate coefficient of variation of the estimate is 4 0 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 HUQ11 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 HUQ11 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 2 796 and the CV of the estimate for households in Ontario as 1
124. r 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 2 Refer to 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 CV 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 CV table namely 1 0 5 So the approximate coefficient of variation of the ratio estimate is given by Rule 4 which is 2 2 where and are the coefficients of variation of X and X respectively Statistics Canada Catalogue no 56M0002GIE 47 Household Internet Use Survey 2003 User Guide 48 That is 0 027 0 010 40 000729 0 0001 0 029 6 The obtained ratio of Quebec households 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 9 which ma
125. r the EIERs 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 3 Adetailed 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 Statistics Canada Catalogue no 56M0002GIE 17 Household Internet Use Survey 2003 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 a
126. rcentile 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 Variability Tables for the Household Internet Use Survey HIUS Province and Region Design Effect Sample Size Population Gmda 6 2m 3n3 wen 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 f 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 40 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 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 App
127. reas of provinces 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
128. 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 response 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 smaller CMAs and rural areas have been recoded to 00 while the larger 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 w
129. roximate 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 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 rea
130. rviewed 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 groups 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 census metropolitan areas rotation groups household and economic family size Weights are also adjusted so that estimates of the previous
131. s Canada Catalogue no 56M0002GIE 9 Household Internet Use Survey 2003 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 a b 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 a were on temporary layoff during the reference week with an expectation of recall and were available for work or b were without work had actively looked for work in the past four weeks and were available for work or C h
132. s 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 used 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 error 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 R That is the standard error of a ratio R 1X is D 2 2 a where and are the coefficients of variation of X and X respectively The coefficient of variation R is given by c 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
133. t users about the high levels of error associated with the estimates Estimates have a sample size of less than 30 or very high coefficients of variation in excess of 33 396 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 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 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 lev
134. te 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 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 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 Statistics Canada Catalogue no 56M0002GIE 65 Household Internet Use Survey 2003 User Guide 66 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 inte
135. tematically will contribute to biases in the survey estimates Considerable time and effort was made to reduce non sampling errors in the survey Quality assurance 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 596 this impact was minimal The quality of the sampling variables in the frame was very high The HIUS sample consisted of four 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 10 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 fra
136. the coefficient of variation of this estimate as determined from the tables CI 0 125 2 0 125 0 040 0 125 2 0 125 0 040 CI x 0 125 0 010 0 125 0 010 CI 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 pe be o X X If is between 2 and 2 then no conclusion about the difference between the ex characteristics is justified at the 596 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 Statistics Canada Catalogue no 56M0002GIE 49 Household Internet Use Survey 2003 User Guide 10 3 1 Ex
137. 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 the 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 unacceptable making the proportion not releasable then the coefficient of variation of the corresponding quantitative estimate will also be unacceptable making the quantitative estimate not 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 50 Statistics Canada Catalogue no 56M0002GIE Household Internet Use Survey 2003 User Guide 10 5 Coefficient of Variation Tables Household Internet Use Survey 2003 Approximate Sampling Variability Tables Newfoundland and Labrador Numerator of Estimated Percentage Percentage 000 0 15 1 05 2 05 5 05 10 05 15 05 20 05 25 05 30 05 35 05 40 0 50 0 70 0 90 0 1 eode
138. ue no 56M0002GIE 7 Household Internet Use Survey 2003 User Guide e Almost two thirds 65 of households had at least one member who used the Internet to search for medical or health related information compared with 6196 in 2001 This was the third most popular use after e mail and general browsing e About 57 of households using the Internet at home had someone who accessed online banking services well above the proportion of 4496 in 2001 the biggest proportional gain of any use This growth may indicate consumers are becoming more confident in the Internet s security aspects e The survey divided households into four equal groups based on income each representing 25 of the income spectrum from highest to lowest In 2003 8296 of households in the highest income group had a member who used the Internet from home This was more than double the proportion of 3396 among these households five years earlier However the strongest growth 41396 was observed in the second income quartile households with income between 24 001 and 43 999 e Rates of Internet use still varied substantially across family types with children still a key factor Single family households with unmarried children under the age of 18 had the highest rate of Internet use from home last year about 7396 e However growth rates in Internet use from home were strongest among single family households without children and one person households The number of househo
139. ur 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 Survey Concepts and Definitions All households Household count 12 297 814 The HIUS is a sample survey weight
140. users are identified by a household responding yes to the question GUQO02 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 GUQO03 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 In 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 GUQO02 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 in a typical month from any location In other words a household that does not presently use the Internet regularly but did use it regularly in the past Never user A household responding no to th

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