Goals Of Exercise: The Goal Of This Exercise Is To Introduce

Goals Of Exercisethe Goal Of This Exercise Is To Introduce Crosstabula

Goals of Exercise The goal of this exercise is to introduce crosstabulation as a statistical tool to explore relationships between variables. The exercise also gives you practice in using CROSSTABS in SDA. Part I—Relationships between Variables We’re going to use the Monitoring the Future (MTF) Survey of high school seniors for this exercise. The MTF survey is a multistage cluster sample of all high school seniors in the United States. The survey of seniors started in 1975 and has been done annually ever since.

To access the MTF 2017 survey follow the instructions in the Appendix. Your screen should look like Figure 9-1. Notice that a weight variable has already been entered in the WEIGHT box. This will weight the data so the sample better represents the population from which the sample was selected. Figure 9-1 MTF is an example of a social survey. The investigators selected a sample from the population of all high school seniors in the United States.

This particular survey was conducted in 2017 and is a relatively large sample of a little more than 12,000 seniors. In a survey, respondents answer questions used as data for analysis. These answers are called variables, which measure various concepts. Previously, univariate analyses examined variables one at a time; for example, calculating the percent of men and women in the sample. But how do we explore relationships between variables? For example, is sex related to binge drinking? (defined as having five or more drinks in a row.)

Crosstabulation is a statistical tool used to examine the relationship between nominal and ordinal variables, and is a key method in bivariate analysis. In survey research, it's important to understand the distinction between dependent and independent variables. The dependent variable is what you're trying to explain (e.g., binge drinking behavior), while the independent variable is a factor you believe influences the dependent variable (e.g., sex). Typically, the dependent variable is placed in the row, and the independent variable in the column within the crosstab table.

To perform the analysis, first locate the variable names for sex (v2150) and binge drinking (v2108). Access the codebook by double-clicking on relevant categories to find these variables. Enter v2150 in the column and v2108 in the row of the crosstabulation table. Then, select column percents for analysis since the independent variable is on the column. Run the table to generate the crosstabulation.

Interpreting the crosstab begins with examining whether column percents sum to 100% (which they should). Because they do, the focus is on comparison across the rows. For example, approximately 81% of men and 85% of women have never engaged in binge drinking, a small difference suggesting females are slightly less likely to binge drink than males. Given the large sample size, this difference is likely statistically significant, generally indicating a true difference in the population. Small sample sizes would complicate such inferences due to increased sampling error.

Part III – Now it’s Your Turn

Select two variables from the list below and compare men and women regarding their likelihood of engaging in each activity. Place the independent variable in the column and the dependent variable in the row. Ensure you request the appropriate percents for analysis, such as column or row percents based on your variable placement. The variables to choose from are:

  • How likely will they attend a technical or vocational school after high school (v2180)?
  • How likely will they graduate from a four-year college after high school (v2183)?
  • How likely will they attend graduate or professional school after high school (v2184)?
  • How likely will they serve in the armed services after high school (v2181)?

Analyze the percent differences between men and women for your chosen variables. Consider whether the differences are meaningful, and justify your reasoning based on the magnitude of the difference and sampling variability. This exercise introduces a more comprehensive understanding of relationships between demographic characteristics and behavioral or educational intentions through cross-tabulation analysis.

References

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