Participation and Activity Limitation Survey (PALS)

Detailed information for 2006

Statistics Canada is conducting a survey on Canadians (adults and children), whose day-to-day activities may be limited because of a condition or health problem. Survey results will help to identify difficulties and barriers these Canadians may face.

Data release - December 3, 2007

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

Description

PALS is a post-censal survey because it uses the census as a sampling frame to identify its target population. For example: the 2006 Census questionnaire included two general questions on activity limitations. The 2006 PALS respondents were selected through the use of the census information on age, geography and the responses to these two general questions.

The Health and Activity Limitation Survey (HALS -- see the "Other reference periods" sidebar) was conducted by Statistics Canada about persons with disabilities in 1986 and 1991. In 2001 the Health and Activity Limitation Survey was renamed the Participation and Activity Limitation Survey. The new name reflected the fact that the new survey would focus on the participation of persons with activity limitations.

The data collected by the survey are used to plan services and programs required by persons with disabilities to participate fully in our society. PALS is funded by Human Resources and Social Development Canada (HRSDC).

Subjects

• Disability
• Equity and inclusion
• Health
• Society and community

Data sources and methodology

Target population

The PALS survey population consists of all persons who answered "yes" to either of the Census questions on activity limitations and who were living in Canada at the time of the Census.

The population covered by the survey was persons living in private and some collective households in the 10 provinces and the three territories. The population living on First Nations reserves is excluded as well as the residents of institutional collectives.

In addition, individuals living on military bases, Canadian Armed Forces vessels, merchant vessels and coast guard vessels, as well as campgrounds and parks were excluded for operational reasons. The target population of PALS is the subset of the surveyed population that also reported disabilities in PALS.

Instrument design

The adults and children's questionnaires were developed based on the review of the 2001 PALS questionnaires and the 1991 Health and Activity Limitation Survey (HALS) questionnaires and an input consultation with client (that is, HRSDC), federal and provincial governments and community associations.

A pilot test was conducted in the spring of 2006 to test the content of the questionnaires. The questionnaires were tested in both official languages.

Sampling

This is a sample survey with a cross-sectional design.

Sample and stratification design

The sample design used for PALS 2006 is a two-phase stratified design based on the 2006 Census. In Phase 1, the census itself, the long form was systematically distributed to approximately every fifth household across Canada. Phase 2 involved the selection of individuals who reported an activity limitation during Phase 1 based on various characteristics defining the strata.

The strata were defined such as to guarantee large enough samples in the domain estimates and to optimize the sample allocation. Therefore, since one of the survey objectives was to allow for statistical profile dissemination of individuals with a disability by province/territory and of various age groups in the population, these domain estimates were considered in the development of the strata. For the provinces, the domain estimates considered were made through the intersection of the province and the following age groups: younger than 15 years old, 15 to 24 years old, 25 to 44 years old, 45 to 64 years old, 65 to 74 years old and 75 years and over. For the territories, the domain estimates were made for children by combining the three territories and for adults by separating the three territories. Furthermore, for a more optimal sample allocation, the severity of the respondent's disability was also included as a stratification variable. Individuals who are severely limited answered "yes, often" at least once to the filter questions in the census. Mildly limited individuals answered "yes, sometimes" at least once to the filter questions in the census but never answered "yes, often." The last variable considered in constructing the strata was probability of selection in Phase 1. Including this variable in the stratification therefore made it possible to minimize the variability of the initial weight of individuals selected in the same domain and therefore to make sample allocation more optimal.

Sample allocation method

Sample distribution was performed in a way that, for each domain, a minimum proportion with a maximum coefficient of variation (CV) of 16.6% (16.6% corresponds to the upper limit of a CV in order to be able to effectively qualify the corresponding estimate) could be estimated. Among children aged 0 to 14 years, the minimum proportion to estimate was set at 8.5%. Among adults aged 15 to 64 years, this proportion was set at 9%, and for adults aged 65 and older, the proportion was set at 11%.

Sample size

The total size of the PALS 2006 sample is around 47,500: 8,500 children (persons under 15 years of age) and 39,000 adults (15 years of age and over).

Data sources

Data collection for this reference period: 2006-10-30 to 2007-02-28

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

The interviews were conducted by telephone with the interviewers completing a computer-assisted questionnaire.

Interviews by proxy were allowed. In some special cases, face-to-face interviews were carried out. The interviews for the children's questionnaire were conducted with the parent or guardian of the child.

View the Questionnaire(s) and reporting guide(s).

Error detection

The first phase of error detection will be done during the data collection. At that stage, the interviewer's supervisors will review the completed questionnaires. Observed inconsistencies will be discussed with the interviewer who conducted the interview and the respondent will be called back if required.

The second phase of error detection will be conducted during data processing which will be made up of many steps. The first step will be the data validation where, among other actions, multiple responses will be blanked out and processed with the other missing responses. The second step of the data processing will be the editing.

Edit rules will be developed to identify and correct inconsistencies between responses within each section of the adults and children's questionnaires. For most of the situations corresponding to inconsistencies an automated correction was specified, as discussed in the IMPUTATION Section. Once this step is completed, a macro verification will be done by analyzing frequency distributions to identify anomalies, for example missing categories or unusually large frequencies.

Imputation

For PALS, a valid response will be deterministically imputed for the missing responses if sufficient information is available in the related questions. Otherwise, it will be coded to "Not stated". In addition, the questions that were not to be asked will be coded to "Valid skip". If a question with a missing answer (coded to "Not stated") should have been used to determine if subsequent questions were to be asked, these subsequent questions will be set to "Not asked", because it was not possible to determine whether or not they should have been asked.

However, non-response will not be permitted for the demographic information required for weighting, namely the age and sex of the respondent. This information will be asked at the beginning of the interview to make sure the selected person has been reached. These two variables will be imputed from the census if they were missing or invalid. In particular, an age will be considered invalid if it was not consistent with the questionnaire used.

Estimation

In a sample survey, each respondent represents not only himself/herself, but also other persons that were not sampled. Consequently, a weight is associated to each respondent to indicate the number of persons that this respondent represents. This weight must be used for all estimations.

The weight is calculated in a three-stage process. The FIRST stage is the assignment of an initial weight based on the sampling design. The initial weight is the inverse of the inclusion probability. For the 2006 PALS, the initial weight is the product of the Census weight and the subsampling weight (the inverse of the sampling fraction in the second phase).

The SECOND stage of the weighting process is the adjustment for non-response. More specifically, two adjustments are done since there are two groups of non respondents with very different characteristics: persons who were not contacted and persons who were contacted but did not respond. Weights are first adjusted for non-contacts and then for non-response. As the adjustment method is the same for both types of non-respondents, it is described here only for non-response. With the help of a logistic regression model, we estimate the response probability for an individual (respondent or non respondent to the PALS) based on his characteristics (also called explanatory variables). Many explanatory variables are available since we have access to all the Census long form information each individual. We then divide individuals in ten classes based on the size of the their predicted response probability. The inverse of the weighted response rate in a class is used as the weighting adjustment factor for that class and the initial weights of the respondents within the class are adjusted accordingly. Note that separate models are used for adults and children.

The THIRD stage of the weighting adjustment is the post-stratification. This adjustment ensures that the sum of the final weights for the respondents is equal to the population counts obtained from the census. This adjustment is done for groups (called post-strata) defined by the combinations of different variables for which this adjustment is important for the survey (province, gender, age group and severity of the limitation reported in the census). The weights corrected for non-response are then adjusted using the ratio of the census count to the sample count for each post-stratum.

Since estimates are obtained from a sample as opposed to a census, estimates will vary from sample to sample (sampling error). In order to provide estimates of sampling error for statistics with PALS data, the bootstrap method is used. This method, which is a resampling method, consists of selecting M subsamples (with replacement) from the main sample. Each subsample is then weighted by calculating the initial weights and applying to them the same adjustments we applied to the main sample weights, i.e. adjustments for non-response and post-stratification. The sampling error is measured and estimated by the bootstrap variance which is the empirical variance of the desired statistic calculated from the main sample and the M bootstrap subsamples.

To be able to produce disability rates and that, at relatively detailed levels, a complementary file was produced containing individuals who did not report an activity limitation to the 2006 Census, and this file is available to the users. The global sample size for this complementary file was set to 131,010 comprising 107,400 adults and 23,610 children. Hence to produce disability rates, one must use this complementary file as well as the main adult or children file containing the answers to the PALS.

Quality evaluation

Two types of error occur in surveys, namely sampling and non-sampling errors. As opposed to the sampling error, non-sampling errors are not explained by sample-to-sample variability. These errors can occur at any step of the survey process and actions will be taken to reduce them to a minimum. Following is a description of measures that will be put in place for that purpose.

A pilot test was conducted seven months before the survey to evaluate all the survey process, from the questionnaire content to the data processing.

High response rates are essential for quality data. To reduce the number of non-response cases, the interviewers were all trained by Statistics Canada's staff, provided with detailed Interviewer Manuals, and were under the direction of interviewer supervisors. Refusals were followed up by senior interviewers to encourage respondents to participate in the survey.

In addition, some measures were taken to identify and correct errors that could result from misinterpretation of a question by the respondent or from a wrong flow followed in the questionnaire. The questionnaires were first be reviewed by the interviewer's supervisor. A detailed set of edit rules were then used during data processing to identify and correct any inconsistencies between the responses provided. These edit rules were exhaustively tested before being applied to the data.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. 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.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

This methodology does not apply to this survey.

Data accuracy

The data accuracy measure used for each table produced is the estimated standard error of the estimate (sampling error measure), which is the square root of the estimated sampling variance of the estimate. However, the estimated standard error is usually expressed relative to the estimate to which it pertains, and the resulting measure is the estimated coefficient of variation (CV).

The estimated CV is obtained by dividing the estimated standard error of the estimate by the estimate itself and is expressed as a percentage of the estimate. For PALS, all estimated CVs will be obtained using the bootstrap method described in the ESTIMATION Section.