STUDENT FINANCIAL SURVEY 20012002 METHODOLOGY C H A P

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Student Financial Survey 2001-2002 Methodology

C H A P T E R 1 — I N T R O D U C T I O N

1.3 METHODOLOGY


This study is designed to capture, from a random sample of post-secondary students across the country, baseline information about the financial situation of students as they begin a school year, and then a snapshot of their monthly income and expenditures across the school year.


Recruitment of the student panel for the study was conducted by telephone, based on a largely national, random sample. The sample of telephone numbers was drawn from a listing of all telephone numbers in the country, however, numbers located in areas more than 100 kilometres from an urban centre were excluded, in order to increase the incidence of finding post-secondary students. A sample of 2,100 students was recruited this way in September 2001. Contacts with over 48,000 telephone numbers were attempted in order to obtain the 2,100 cases in the panel. The incidence of finding post-secondary education students in the random sample was seven per cent (slightly higher than the five per cent average across the country).


The purpose of the study was introduced to potential respondents and the nature of participation

explained. The response rate to the recruitment was 70 per cent. The recruitment was conducted in both languages and the self-administered survey questionnaires were also available in both languages.


The survey information was intended to be collected using a self-administered approach, through the Internet. Over the course of the study it was decided that telephone follow-up data collection would also be required each month, to maintain the highest possible participation of the student panel from month to month over the school year. An initial baseline survey in October collected basic information about students’ education, financial status at the beginning of the school year and their socio-demographic characteristics (e.g., age, gender, region, etc.).


The survey required just over 15 minutes to complete over the Internet. Just over 1,100 surveys were completed online, and an additional 427 cases were collected by telephone for a total of 1,543 cases in the baseline. This is a response rate of 73 per cent (2,100 were originally recruited). The reference timeframe set for the baseline (i.e., the period for which students were to report financial information such as income received and expenditures prepaid towards the school year) was “over the summer months, ending just prior to the school year.”


Waves of the panel survey took place at the start of each month and continued for roughly two weeks. Typically, two in three cases were collected over the Internet and the remaining one in three were collected by telephone. Participation slowly eroded over the eight months of the school year, however, the study was able to retain roughly 60 per cent of the overall baseline sample until the end of the survey. Table 1 demonstrates the number of students participating at each wave and the response rate (from the baseline survey of 1,543). The two biggest drops in response rates (after the initial drop-off from recruitment to baseline) were in January and May, when five and ten per cent of the sample, respectively, were lost to attrition. In each new wave, students were asked to report their income and expenditures during the entire previous calendar month (i.e., income and expenses for September were reported in the October wave of the survey).


In addition to the basic questions asked each month in the follow-up survey, three sets of additional questions were posed. In January, students were asked to report the average grade they had received for the previous semester, as well as details about their employment during the first semester (including average hours worked). In March, students were asked about their assets, including cars, computers and electronics. In the last wave, in April, students were asked about the total amount that they received in government loans (as a final check of the information reported during the year), the new balance on their credit cards and whether or not they were graduating and, if not, what their intentions were for school next year.


Toward the end of the follow-up survey period, it became apparent that there was some confusion in the interpretation of the reporting base for providing expenditure figures for food, personal care, entertainment and clothing and jewellery. Some students reported a figure spent per month and some reported a figure spent per week. (Additional efforts were made to clarify the actual reporting base with each student.) Answers were obtained for over 80 per cent of the sample and a reporting base was attributed when the information was missing, based on the average amounts reported by other students in the same living conditions.


At the end of data collection a single database was built to hold all responses from baseline to final follow-up wave for each of the 1,543 students in the sample. The results were weighted by gender and region, as there was a slight under sampling of male students (by six per cent) and of students in Ontario and Quebec (by six and eight per cent, respectively).


A number of steps were taken to finalize the file before proceeding with the analysis and reporting of survey results. First, some coding was conducted in an attempt to categorize openended responses. Second, all continuous variable responses (including amounts of reported income and expenditure) were examined for outliers. This involved excluding responses that were quite far outside the central tendency of responses.


As shown in the table on participation rates by month, not all 1,543 students participated in each follow-up wave. Therefore, there were missing data for many student records. In order to rely on a common respondent pool for the purposes of examining monthly budgets, the analysis of financial data included only the students who completed at least four of the eight follow-up survey waves. This represented 1,257 of the 1,543 students in the baseline. For these 1,257 students, any missing data in the financial fields were attributed (or filled in) with the most likely response, which was typically arrived at by examining adjacent months. That is, if a student did not complete the March wave, we attributed the missing information on the basis of their February responses. In a few cases (such as for government loans in January and parental support in December) another approach was taken. In these cases, missing values were attributed on the basis of the average amount reported by a similar group of students

(i.e., same age, same living arrangements, same school type and status) for that individual month. This was done because there were spikes in the amounts reported in some types of income and expenditure for particular months.


The last step prior to the analysis of results was to create new variables in the database to calculate total values for the year and percentages of income and expenditure from specific sources (of all students and for each month’s income and expenditures).


Note to the Reader

A few issues should be noted about the reporting of results. The first is to advise that, in interpreting results, the reader always consider the base of students used to calculate financial data (e.g., amounts of income, expenditures and debt); in particular, whether figures are based on all students or only those students for whom the particular indicator is applicable. For example, the average reported amount of summer employment earnings across the entire survey is $3,500, however, when only those students who worked in the summer are considered, the average increases to $4,000. In most cases, the numbers reported in this document are calculated as an average (per student) based on the affected segment of the student pool. Monthly patterns of income and expenditures, however, were examined using a common base of all students.


The second element to be noted is that all dollar figures above $999 were rounded to the nearest $100.


Finally, many of the survey results differentiate on the basis of age. Unless controlled for, the age relationship can, in turn, generate findings that are a function of age. For example, an analysis of students’ use of credit cards by region shows that Quebec students have the lowest incidence of owning a card. However, younger students are also less likely to have credit cards and, since Quebec students are younger (as a result of the CEGEP system in that province), the regional difference in credit card ownership owes more to regional differences in the age distribution of the student population than to region itself. Where possible, the analyses controlled for age when examining results by other characteristics that are shown to be closely associated with age (e.g., marital status, dependents, living arrangements). The difficulty, however, is that the number of cases and general complexity of the data set made this type of control difficult in some instances. For example, the results in the financial chapter do not control for age in any way.


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