Data sources, methods and definitions

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Data sources and methods

The first portion of this article, which profiles men and women aged 25 to 34 with a STEM university degree, is based on the 2011 National Household Survey.

This second portion of this paper uses the linked Youth in Transition Survey–Programme for International Student Assessment (YITS–PISA) data, which includes data from the Canadian component of the PISA 2000 survey (when survey respondents were aged 15), and longitudinal data from YITS (Cycle 6) up to age 25. Using these data allows for the linking of characteristics during adolescence with educational outcomes in young adulthood. The choice of a first university program can be identified from Cycle 2 (age 17) through to Cycle 6 (age 25).

In YITS, the respondent’s first university program is determined using Classification of Instructional Program (CIP) codes for the first main field of study or specialization. While a person’s first program type may not be his or her final program upon graduation (if he or she graduates), first program type does indicate a person’s initial interests out of high school. Switching programs does occur for some youth during their time in university; however, some past studies have found that the majority of youths’ first programs are those they remain in throughout five years of university.

This analysis is restricted to students who attended university—the sample under consideration does not include those whose first postsecondary education (PSE) program is in a non-university setting and those who do not go into a PSE program prior to age 25. The choice was made only to consider university-bound youth because of comparability challenges between programs at the university and non-university levels. For example, engineering programs are offered at both colleges and universities, but can be quite different with the former being oriented more toward practical job skills. (Students who only attended Quebec CEGEPs over the period are not included in the population of youth who went to university). In all analyses, the appropriate survey weights are utilized as well as the corresponding bootstrap weights.

In this study, the program-type measure recategorized the 13 primary groupings in Classification of Instructional Programs (CIP) 2000 into five categories. The five categories, informed from past literature,Note1 are the following:

  • social sciences (includes arts, education, humanities, social sciences and law)
  • business/management/public administration
  • science/math/computer science, engineering and agriculture
  • health, parks, recreation and fitness
  • other.

For the sake of parsimony, the five category titles have been shortened to the following:

  • social sciences
  • business
  • STEM
  • health
  • other.

Definitions

STEM programs

The analysis in the second part of this paper differs slightly from the standard STEM definition. Because the YITSPISA sample does not allow for a detailed disaggregation of CIP codes (due to sampling issues), the analysis in the second section is done at the level of primary groupings of CIP 2000, while the recommended standard (used in the first half of this report) was developed using lower levels of CIP 2011. However, population differences between the two definitions are very small overall. For more information on Statistics Canada’s recommended STEM groupings, see Variant of CIP 2011 – STEM groupings.Note2

PISA mathematics scores

Mathematical literacy is used in the current context to “indicate the ability to put mathematical knowledge and skills to functional use rather than just mastering them within a school curriculum”.Note3 In total, 32 mathematics questions were included in the PISA 2000 assessments. This study examines mean levels of mathematical ability from Cycle 1, when survey respondents were 15 years of age. Proficiency levels in mathematics were also created and are used to form a measure tapping into high levels of mathematical ability. In this study, youth defined as having “high” math ability are in the 4th proficiency level or higher (out of a maximum of 6 levels). Youth with “lower” levels of mathematical abilities are in the 3rd proficiency level or lower. Both the sample size and differences between proficiency levels were factored in the definition of “higher” and “lower” mathematical ability.

Other measures of mathematical ability considered in this study are high school marks in mathematics, which were measured at age 15 (Cycle 1), and self-rated mathematical ability, which were measured at age 17 (in Cycle 2). Self-rated mathematical ability was not measured in Cycle 1.

Multinomial logistic regression

Multinomial logistic regression results have been transformed from multinomial logits to average marginal effects for ease of interpretation. They can be interpreted as the effect of a one-unit change in any given explanatory variable on the probability of choosing each of the university programs, all else being equal.


Notes

  1. See Montmarquette et al. (2002).
  2. See Statistics Canada (2013).
  3. See Bussière et al. (2001), p. 86.
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