1.0 Description
The Canadian Perspectives Survey Series (CPSS) is a set of short, online surveys beginning in March 2020 that will be used to collect information on the knowledge and behaviours of residents of the 10 Canadian provinces. All surveys in the series will be asked of Statistics Canada's probability panel. The probability panel for the CPSS is a new pilot project initiated in 2019. An important goal of the CPSS is to directly collect data from Canadians in a timely manner in order to inform policy makers and be responsive to emerging data needs. The CPSS is designed to produce data at a national level (excluding the territories).
The survey program is sponsored by Statistics Canada. Each survey in the CPSS is cross sectional. Participating in the probability panel and the subsequent surveys of the CPSS is voluntary.
The first survey of the CPSS is CPSS1 – Impacts of COVID-19. It was administered from March 29, 2020 until April 3, 2020.
Any question about the survey, the survey series, the data or its use should be directed to:
Statistics Canada
Client Services
Centre for Social Data Integration and Development
Telephone: 613-951-3321 or call toll-free 1-800-461-9050
Fax: 613-951-4527
E-mail: statcan.csdidclientservice-ciddsservicealaclientele.statcan@statcan.gc.ca
2.0 Survey methodology
Target and survey population
The target population for the Canadian Perspectives Survey Series (CPSS) is residents of the 10 Canadian provinces 15 years of age or older.
The frame for surveys of the CPSS is Statistics Canada's pilot probability panel. The probability panel was created by randomly selecting a subset of the Labour Force Survey (LFS) respondents. Therefore the survey population is that of the LFS, with the exception that full-time members of the Canadian Armed Forces are included. Excluded from the survey's coverage are: persons living on reserves and other Aboriginal settlements in the provinces; the institutionalized population, and households in extremely remote areas with very low population density. These groups together represent an exclusion of less than 2% of the Canadian population aged 15 and over.
The LFS sample is drawn from an area frame and is based on a stratified, multi-stage design that uses probability sampling. The LFS uses a rotating panel sample design. In the provinces, selected dwellings remain in the LFS sample for six consecutive months. Each month about one-sixth of the LFS sampled dwellings are in their first month of the survey, one-sixth are in their second month of the survey, and so on. These six independent samples are called rotation groups.
For the probability panel used for the CPSS, four rotation groups from the LFS were used from the provinces: the rotation groups answering the LFS for the last time in April, May, June and July of 2019. From these households, one person aged 15+ was selected at random to participate in the CPSS - Sign-Up. These individuals were invited to Sign-Up for the CPSS. Those agreeing to join the CPSS were asked to provide an email address. Participants from the Sign-Up that provided valid email addresses formed the probability panel. The participation rate to the panel was approximately 23%. The survey population for all surveys of the CPSS is the probability panel participants. Participants of the panel are 15 years or older as of July 31, 2019.
Sample Design and Size
The sample design for surveys of the CPSS is based on the sample design of the CPSS – Sign-Up, the method used to create the pilot probability panel. The raw sample for the CPSS – Sign-Up had 31, 896 randomly selected people aged 15+ from responding LFS households completing their last interview of the LFS in April to July of 2019. Of these people, 31,626 were in-scope at the time of collection for the CPSS - Sign-Up in January to March 2020. Of people agreeing to participate in the CPSS, that is, those joining the panel, 7,242 had a valid email address. All panel participants are invited to complete the surveys of the CPSS.
| Stages of the Sample | n |
|---|---|
| Raw sample for the CPSS – Sign-Up | 31,896 |
| In-scope Units from the CPSS – Sign-Up | 31,628 |
| Panelists for the CPSS (with valid email addresses) |
7,242 |
| Raw sample for surveys of the CPSS | 7,242 |
3.0 Data collection
CPSS – Sign-Up
The CPSS- Sign-Up survey used to create Statistics Canada's probability panel was conducted from January 15th, 2020 until March 15th, 2020. Initial contact was made through a mailed letter to the selected sample. The letter explained the purpose of the CPSS and invited respondents to go online, using their Secure Access Code to complete the Sign-Up form. Respondents opting out of joining the panel were asked their main reason for not participating. Those joining the panel were asked to verify basic demographic information and to provide a valid email address. Nonresponse follow-up for the CPSS-Sign-Up had a mixed mode approach. Additional mailed reminders were sent to encourage sampled people to respond. As well, email reminders (where an email address was available) and Computer Assisted Telephone Interview (CATI) nonresponse follow-up was conducted.
The application included a standard set of response codes to identify all possible outcomes. The application was tested prior to use to ensure that only valid question responses could be entered and that all question flows would be correctly followed. These measures ensured that the response data were already "clean" at the end of the collection process.
Interviewers followed a standard approach used for many StatCan surveys in order to introduce the agency. Selected persons were told that their participation in the survey was voluntary, and that their information would remain strictly confidential.
CPSS1 – Impacts of COVID-19
All participants to the pilot panel for the CPSS were sent an email invitation with a link to the survey CPSS1 – COVID 19 and a Secure Access Code to complete the survey online. Collection for the survey began on March 29, 2020. Reminder emails were sent on March 30th and April 1st. The application remained open until April 3rd, 2020.
3.1 Disclosure control
Statistics Canada is prohibited by law from releasing any data which would divulge information obtained under the Statistics Act that relates to any identifiable person, business or organization without the prior knowledge or the consent in writing of that person, business or organization. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.
4.0 Data quality
Survey errors come from a variety of different sources. They can be classified into two main categories: non-sampling errors and sampling errors.
4.1 Non-sampling errors
Non-sampling errors can be defined as errors arising during the course of virtually all survey activities, apart from sampling. They are present in both sample surveys and censuses (unlike sampling error, which is only present in sample surveys). Non-sampling errors arise primarily from the following sources: nonresponse, coverage, measurement and processing.
4.1.1 Nonresponse
Nonresponse errors result from a failure to collect complete information on all units in the selected sample.
Nonresponse produces errors in the survey estimates in two ways. Firstly, non-respondents often have different characteristics from respondents, which can result in biased survey estimates if nonresponse bias is not fully corrected through weighting. Secondly, it reduces the effective size of the sample, since fewer units than expected answered the survey. As a result, the sampling variance increases and the precision of the estimate decreases. The response rate is calculated as follows:
[ Responding units / (Selected units – out-of-scope units) ] x 100%
The following table summarize the response rates experienced for the CPSS1 – Impacts of COVID-19. Response rates are broken down into two stages. Table 4.1.1a shows the take-up rates to the panel in the CPSS- Sign-Up and Table 4.1.1b shows the collection response rates for the survey CPSS1 - Impacts of COVID-19.
| Stages of the Sample for the CPSS – Sign-Up | ||||
|---|---|---|---|---|
| Raw sample for the CPSS – Sign-Up | In-scope Units from the CPSS – Sign-Up | Panelists for the CPSS (with valid email addresses) |
Participation Rate to the Panel for CPSS | |
| n | 31,896 | 31,628 | 7,242 | 22.9% |
| Stages of the Sample for the CPSS1 – Impacts of COVID -19 | ||||
|---|---|---|---|---|
| Panelists for the CPSS (with valid email addresses) |
Respondents to CPSS1 – Impacts of COVID - 19 | Collection Response Rate to CPSS1 – Impacts of COVID-19 | Cumulative Response Rate | |
| n | 7,242 | 4,627 | 63.9% | 14.6% |
As shown in Table 4.1.1b, the collection response rate for the CPSS1 - Impacts of COVID-19 is 63.9%. However, when nonparticipation in the panel is factored in, the cumulative response rate to the survey is 14.6%. This cumulative response rate is lower than the typical response rates observed in social surveys conducted at Statistics Canada. This is due to the two stages of nonresponse (or participation) and other factors such as the single mode used for surveys of the CPSS (emailed survey invitations with a link to the survey for online self-completion), respondent fatigue from prior LFS response, the inability of the offline population to participate, etc.,.
Given the additional nonresponse experienced in the CPSS1 – Impacts of COVID-19 there is an increased risk of bias due to respondents being different than nonrespondents. For this reason, a small bias study was conducted. Please see Section 6.0 for the results of this validation.
4.1.2 Coverage errors
Coverage errors consist of omissions, erroneous inclusions, duplications and misclassifications of units in the survey frame. Since they affect every estimate produced by the survey, they are one of the most important type of error; in the case of a census they may be the main source of error. Coverage errors may cause a bias in the estimates and the effect can vary for different sub-groups of the population. This is a very difficult error to measure or quantify accurately.
For the CPSS, the population covered are those aged 15+ as of July 31, 2019. Since collection of the CPSS1 – Impacts of COVID-19 was conducted from March 29 – April 3 2020, there is an undercoverage of residents of the 10 provinces that turned 15 since July 31, 2019. There is also undercoverage of those without internet access. This undercoverage is greater amongst those age 65 years and older.
4.1.3 Measurement errors
Measurement errors (or sometime referred to as response errors) occur when the response provided differs from the real value; such errors may be attributable to the respondent, the questionnaire, the collection method or the respondent's record-keeping system. Such errors may be random or they may result in a systematic bias if they are not random. It is very costly to accurately measure the level of response error and very few surveys conduct a post-survey evaluation.
4.1.4 Processing errors
Processing error is the error associated with activities conducted once survey responses have been received. It includes all data handling activities after collection and prior to estimation. Like all other errors, they can be random in nature, and inflate the variance of the survey's estimates, or systematic, and introduce bias. It is difficult to obtain direct measures of processing errors and their impact on data quality especially since they are mixed in with other types of errors (nonresponse, measurement and coverage).
4.2 Sampling errors
Sampling error is defined as the error that results from estimating a population characteristic by measuring a portion of the population rather than the entire population. For probability sample surveys, methods exist to calculate sampling error. These methods derive directly from the sample design and method of estimation used by the survey.
The most commonly used measure to quantify sampling error is sampling variance. Sampling variance measures the extent to which the estimate of a characteristic from different possible samples of the same size and the same design differ from one another. For sample designs that use probability sampling, the magnitude of an estimate's sampling variance can be estimated.
Factors affecting the magnitude of the sampling variance for a given sample size include:
- The variability of the characteristic of interest in the population: the more variable the characteristic in the population, the larger the sampling variance.
- The size of the population: in general, the size of the population only has an impact on the sampling variance for small to moderate sized populations.
- The response rate: the sampling variance increases as the sample size decreases. Since non-respondents effectively decrease the size of the sample, nonresponse increases the sampling variance.
- The sample design and method of estimation: some sample designs are more efficient than others in the sense that, for the same sample size and method of estimation, one design can lead to smaller sampling variance than another.
The standard error of an estimator is the square root of its sampling variance. This measure is easier to interpret since it provides an indication of sampling error using the same scale as the estimate whereas the variance is based on squared differences.
The coefficient of variation (CV) is a relative measure of the sampling error. It is defined as the estimate of the standard error divided by the estimate itself, usually expressed as a percentage (10% instead of 0.1). It is very useful for measuring and comparing the sampling error of quantitative variables with large positive values. However, it is not recommended for estimates such as proportions, estimates of change or differences, and variables that can have negative values.
It is considered a best practice at Statistics Canada to report the sampling error of an estimate through its 95% confidence interval. The 95% confidence interval of an estimate means that if the survey were repeated over and over again, then 95% of the time (or 19 times out of 20), the confidence interval would cover the true population value.
5.0 Weighting
The principle behind estimation in a probability sample such as those of the CPSS, 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 2% sample of the population, each person in the sample represents 50 persons in the population. In the terminology used here, it can be said that each person has a weight of 50.
The weighting phase is a step that calculates, for each person, his or her associated sampling weight. This weight appears on the microdata file, and must be used to derive estimates representative of the target population from the survey. For example, if the number of individuals who smoke daily is to be estimated, it is done by selecting the records referring to those individuals in the sample having that characteristic and summing the weights entered on those records. The weighting phase is a step which calculates, for each record, what this number is. This section provides the details of the method used to calculate sampling weights for the CPSS1 – Impacts of COVID-19.
The weighting of the sample for the CPSS1 – Impacts of COVID-19 has multiple stages to reflect the stages of sampling, participation and response to get the final set of respondents. The following sections cover the weighting steps to first create the panel weights, then the weighting steps to create the survey weights for CPSS1 – Impacts of COVID-19.
5.1 Creating the Panel Weights
Four consecutive rotate-out samples of households from the LFS were the starting point to form the panel sample of the CPSS. Since households selected from the LFS samples are the starting point, the household weights from the LFS are the first step to calculating the panel weights.
5.1.1 Household weights
Calculation of the Household Design Weights – HHLD_W0, HHLD_W1
The initial panel weights are the LFS subweights (SUBWT). These are the LFS design weights adjusted for nonresponse but not yet calibrated to population control totals. These weights form the household design weight for the panel survey (HHLD_W0).
Since only four rotate-outs were used, instead of the six used in a complete LFS sample, these weights were adjusted by a factor of 6/4 to be representative. The weights after this adjustment were called HHLD_W1.
Calibration of the Household Weights – HHLD_W2
Calibration is a step to ensure that the sum of weights within a certain domain match projected demographic totals. The SUBWT from the LFS are not calibrated, thus HHLD_W1 are also not calibrated. The next step is to make sure the household weights add up to the control totals by household size. Calibration was performed on HHLD_W1 to match control totals by province and household size using the size groupings of 1, 2, or 3+.
5.1.2 Person Panel weights
Calculate Person Design Weights – PERS_W0
One person aged 15 or older per household was selected for the CPSS – Sign-Up, the survey used to create the probability panel. The design person weight is obtained by multiplying HHLD_W2 by the number of eligible people in the dwelling (i.e. number of people aged 15 years and over).
Removal of Out of Scope Units – PERS_W1
Some units were identified as being out-of-scope during the CPSS – Sign-Up. These units were given a weight of PERS_W1 = 0. For all other units, PERS_W1 = PERS_W0. Persons with a weight of 0 are subsequently removed from future weight adjustments.
Nonresponse/Nonparticipation Adjustment – PERS_W2
During collection of the CPSS – Sign-Up, a certain proportion of sampled units inevitably resulted in nonresponse or nonparticipation in the panel. Weights of the nonresponding/nonparticipating units were redistributed to participating units. Units that did not participate in the panel had their weights redistributed to the participating units with similar characteristics within response homogeneity groups (RHGs).
Many variables from the LFS were available to build the RHG (such as employment status, education level, household composition) as well as information from the LFS collection process itself. The model was specified by province, as the variables chosen in the model could differ from one province to the other.
The following variables were kept in the final logistic regression model: education_lvl (education level variable with 10 categories), nameissueflag (a flag created to identify respondents not providing a valid name), elg_hhldsize (number of eligible people for selection in the household) and age_grp (age group of the selected person). RHGs were formed within provinces. An adjustment factor was calculated within each response group as follows:
The weights of the respondents were multiplied by this factor to produce the PERS_W2 weights, adjusted for panel nonparticipation. The nonparticipating units were dropped from the panel.
5.2 Creating the CPSS1 – Impact of COVID-19 weights
Surveys of the CPSS start with the sample created from the panel participants. The panel is comprised of 7,242 individuals, each with the nonresponse adjusted weight of PERS_W2.
Calculation of the Design Weights – COVID_W0, COVID_W1
The design weight is the person weight adjusted for nonresponse calculated for the panel participants (PERS_W2). No out-of-scope units were identified during the survey collection of CPSS1 – Impacts of COVID-19. Since all units were in-scope, COVID_W1=COVID_W0 and no units were dropped.
Nonresponse Adjustment – COVID_W2
Given that the sample for CPSS was formed by people having agreed to participate in a web panel, the response rates to the survey were relatively high. Additionally, the panel was designed to produce estimates at a national level, so sample sizes by province were not overly large. As a result, nonresponse was fairly uniform in many provinces. This resulted in having only one RHG in each of the Atlantic Provinces, as well as in Saskatchewan. For the other provinces, the RHGs were formed by education level and/or age group. An adjustment factor was calculated within each response group as follows:
The weights of the respondents were multiplied by this factor to produce the COVID_W2 weights, adjusted for survey response. The nonresponding units were dropped from the survey.
Trimming of Large Weights – COVID_W2_TRIMMED
Some weights were particularly large. In order to try to mitigate their effect on the variance, the largest weights were trimmed, using a technique called Winsorization, which identifies units with more influential weights. In total, 16 weights were trimmed from Quebec, Ontario, Alberta and British Columbia.
Calibration of Person-Level Weights – COVID_W3
Control totals were computed using LFS demography projection data. The control totals were by age group and sex by province. Since there were very few respondents in some categories (especially in the Atlantic Provinces), some collapsing was necessary. The impact of the collapsing is that demographic totals calculated by the sum of weights do not match the projected control totals for some age * sex * province groupings. Differences between the weighted sums produced from the survey and the control totals is called the slippage rate. Section 6.0 contains more information on the slippage rates for the CPSS1- Impacts of COVID-19.
5.3 Bootstrap Weights
Bootstrap weights were created for the panel and the CPSS1- Impacts of COVID-19 survey respondents. The LFS bootstrap weights were the initial weights and all weight adjustments applied to the survey weights were also applied to the bootstrap weights.
6.0 Quality of the CPSS and Survey Verifications
The probability panel created for the CPSS is a pilot project started in 2019 by Statistics Canada. While the panel offers the ability to collect data quickly, by leveraging a set of respondents that have previously agreed to participate in multiple short online surveys, and for whom an email address is available to expedite survey collection, some aspects of the CPSS design put the resulting data at a greater risk of bias. The participation rate to the panel is lower than typically experienced in social surveys conducted by Statistics Canada which increases the potential nonresponse bias. Furthermore, since the surveys of the CPSS are all self-complete online surveys, people without internet access do not have the means to participate in the CPSS and therefore are not covered.
When the unweighted panel was compared to the original sample targeted to join the panel, in particular there was an underrepresentation of those aged 15-24, those aged 65 and older, and those with less than a high school degree. These differences were expected due to the nature of the panel and the experience of international examples of probability panels. Using LFS responding households as the frame for the panel was by design in order to leverage the available LFS information to correct for the underrepresentation and overrepresentation experienced in the panel. The nonresponse adjustments performed in the weighting adjustments of the panel and the survey respondents utilised the available information to ensure the weights of nonresponding/nonparticipating units went to similar responding units. Furthermore, calibration to age and sex totals helped to adjust for the underrepresentation by age group.
Table 6.1 shows the slippage rates by certain domains post-calibration of CPSS1 – Impact of COVID-19. The slippage rate is calculated by comparing the sum of weights in the domain to that of the control total based off of demographic projections. A positive slippage rate means the sample has an over-count for that domain. A negative slippage rate means the survey has an under-count for that domain. Based on the results shown in Table 6.1, it is recommended to only use the data at the geographical levels where there is 0 slippage.
Furthermore, for analysis by sex, only proportions should be used, not totals. For example, when reporting excellent health by sex, it can be reported as:
X% of women are in excellent health as compared to Y% of men that are in excellent health.
However, as the total counts vary slightly from projected total demographic counts by sex, it is not recommended to say:
5 million women are in excellent health as compared to 6 million men that are in excellent health. It should also not be stated as 1 million more men are in excellent health than women.
(Numbers used in this example are only for illustrative purposes).
| Area | Domain | n | Slippage Rate |
|---|---|---|---|
| Geography | Canada* | 4627 | 0% |
| Prince Edward Island | 141 | -7.6% | |
| Newfoundland and Labrador | 253 | 3.2% | |
| Nova Scotia | 117 | 3.1% | |
| New Brunswick | 215 | 0.6% | |
| Quebec | 790 | 0% | |
| Ontario | 1352 | 0% | |
| Manitoba | 351 | 0% | |
| Saskatchewan | 310 | 0% | |
| Alberta | 519 | 0% | |
| British Columbia | 579 | 0% | |
| Age Group | All* | 4627 | 0% |
| 15-24 | 244 | 0% | |
| 25-34 | 646 | 0% | |
| 35-44 | 795 | 0% | |
| 45-54 | 737 | 0% | |
| 55-64 | 1000 | 0% | |
| 65+ | 1205 | 0% | |
| Sex | All* | 4627 | 0% |
| Male | 2155 | 2.1% | |
| Female | 2472 | -2.1% | |
| *Based on the 10 provinces; the territories are excluded. | |||
After the collection of CPSS1 – Impacts of COVID-19, a small bias study was conducted to assess the potential bias due to the lower response rates and the undercoverage of the population not online. The LFS data was used to produce weighted estimates for the in-scope sample targeted to join the probability panel (using the weights and sample from PERS_W1). The same data was used to produce weighted estimates based on the set of respondents from the CPSS1 survey and the weights COVID_W3. The two set of estimates were compared and are shown in Table 6.2. The significant differences are highlighted.
| Subject | Recoded variables from 2019 LFS | Estimate for in-scope population (n=31,628) |
Estimate for W1 of CPSS - Impacts of COVID-19 (n=4,627) |
% Point Difference |
|---|---|---|---|---|
| Education | Less than High School | 15.5% | 13.8% | -1.7% |
| High School no higher certification | 25.9% | 26.9% | 1.0% | |
| Post-secondary certification | 58.6% | 59.4% | 0.7% | |
| Labour Force Status | Employed | 61.2% | 62.7% | 1.6% |
| Unemployed | 3.4% | 3.7% | 0.3% | |
| Not in Labour Force | 35.3% | 33.4% | -1.9% | |
| Country of Birth | Canada* | 71.7% | 76.3% | 4.6% |
| Marital Status | Married/Common-law* | 60.4% | 63.1% | 2.7% |
| Divorced, separated, widowed* | 12.8% | 9.7% | -3.1% | |
| Single, never married | 26.9% | 27.3% | 0.4% | |
| Kids | Presence of children* | 31.7% | 34.6% | 3.0% |
| Sex | Male | 48.0% | 48.3% | 0.3% |
| Female | 52.0% | 51.7% | -0.3% | |
| Household Size | Single person | 14.4% | 13.9% | -0.5% |
| Two person HH | 34.8% | 35.9% | 1.1% | |
| Three or more people | 18.4% | 18.0% | -0.3% | |
| Eligible people for panel | One eligible person aged 15+ | 15.9% | 15.6% | -0.3% |
| Two eligible people* | 49.3% | 51.7% | 2.4% | |
| Three or more eligible people | 34.8% | 32.7% | -2.1% | |
| Dwelling | Apartment | 12.1% | 11.1% | -1.0% |
| Rented* | 24.8% | 21.0% | -3.8% | |
| *Estimates that are significantly different at α= 5%. | ||||
While many estimates do not show significant change, the significant differences show that some bias remains in the CPSS1- Impacts of COVID-19. There is an underrepresentation of those that are divorced/separated/widowed and those that rent. And there is an overrepresentation of people born in Canada, those that are married, those with kids in the household, and those where there were two eligible participants for the panel. These small differences should be kept in mind when using the CPSS1 –Impacts of COVID-19 survey data.