Gender Gaps and Support for Traditional Gender Roles

There are two projects below. The first tests for significant differences in sample means between men and women on four variables of your own choosing. The second uses theComputecommand to explore attitudes toward abortion or traditional gender roles. You are urged to complete both projects. Follow the instructions in the "Using SPSS" demonstration presented earlier in this chapter to produce the output.

Project 1: Exploring the Gender Gap

In this enlightened age, with its heavy stress on gender equality, how many important differences persist between the sexes? In this project, you will select four dependent variables and test whether the genders are significantly different on the variables you select.

Step 1: Choosing Dependent Variables

Select four dependent variables from theGSS2012data set. Chooseonlyinterval-ratio variables or ordinal variables with three or more scores or categories. As you select variables, you might keep in mind the issues at the forefront of the debate over gender equality: income, education, and other measures of equality. Or you might choose variables that relate to lifestyle choices and patterns of everyday life: religiosity, TV viewing habits, or political ideas.

List your four dependent variables in the table below.

Variable

SPSS Name

What Exactly Does This Variable Measure?

1

2

3

4

Step 2: Stating Hypotheses

For each dependent variable, state a hypothesis about the difference you expect to find. For example, you might hypothesize that men will be more liberal or women will be more educated. Of course, you can hypothesize that there will be no significant difference between the genders. You can base your hypotheses on your own experiences or on the information about gender differences that you have acquired in your courses or from other sources.

Hypotheses:

1.

2.

3.

4.

Step 3: Getting the Output

Load theGSS2012data set and use theIndependent-Samples T Testto produce output. See the "Using SPSS" demonstration in this chapter for detailed instructions. Find the four dependent variables you selected in the list and click the top arrow in the middle of the window to move the variable names to theTest Variable(s)box.

Next, highlightsexand click the bottom arrow in the middle of the window to movesexto theGrouping Variablebox. Click theDefine Groupsbutton and type1in the first box (for males) and2in the second box (for females). ClickContinueto return to theIndependent-Samples T Testwindow and clickOK.

Step 4: Reading the Output

Remember that the first block of output ("Group Statistics") presents descriptive statistics, and the second block reports the test for significance. In the top row of the second block of output, find thetvalue, the degrees of freedom, and the "Sig. (2-tailed)," which, as you recall, is theexactprobability of getting the observed difference in sample means if only chance is operating.

Step 5: Recording Your Results

Record your results in the table below. Write the SPSS variable name in the first column and then record the descriptive statistics. Next, record the results of the test of significance, using the top row ("Equal Variance Assumed") of the second output block. Record thetscore, the degrees of freedom (df), and whether the difference is significant at the 0.05 level. If the value of "Sig. (2-tailed)" is less than 0.05, write YES in this column. If the value of "Sig. (2-tailed)" is more than 0.05, write NO in the column.

Dependent Variables

s

N

tscore

df

Significant?

Men

Women

Men

Women

Men

Women

Men

Women

Step 6: Interpreting Your Results

Summarize your findings. For each dependent variable, write

1. At least one sentence summarizing the test, in which you identify the variables being tested, the sample means for each group,N, thetscore, and the significance level. In the professional research literature, you might find the results reported as: "For a sample of 1417 respondents, there was no significant difference betweenthe average age of men (48.21) and the average age of women (48.12) (,,)."
2. A sentence relating to your hypotheses. Were they supported? How?

Project 2: Using the Compute Command to Explore Gender Differences

In this project, you will use theComputecommand, which was introduced inChapter 4, to construct a summary scale for either support for legal abortion or support for traditional gender roles. Do these attitudes vary significantly by gender? You will also choose a second independent variable, other than gender, to test for significant differences.

Step 1: Creating Summary Scales

To refresh your memory, we used theComputecommand inChapter 4to

list a summary scale (abscale) for attitudes toward abortion by adding the scores on two items (abanyandabpoor). Remember that, once created, a computed variable is added to the active file and can be used like any of the variables actually recorded in the file. If you did not save the data file withabscaleincluded, you can quickly recreate the variable by following the instructions inChapter 4.

As an alternative, you can also list a scale to measure support for traditional gender roles. The GSS data set supplied with this text includes two variables that measure gender attitudes. One of these (fefam) states, "It is much better for everyone involved if the man is the achiever outside the home and the woman takes care of the home and family." There are four possible responses to this item, ranging from "Strongly agree" (1) to "Strongly disagree" (4). Note that the lowest score (1) is most supportive of traditional gender roles.

The other item (fechld) states, "A working mother doesn't hurt the children." This item also has four possible responses and, again, the lowest scorestrongly disagree (1)is the most consistent with support of traditional gender roles.

The scores of the two items vary in the same direction (for both, the lowest score indicates the strongest support for traditional gender roles), so we summarize a scale by simply adding the two variables together. Follow the commands inChapter 4to add the scores offefamandfechldand create the scale, which we will callfescale. The computed variable will have a total of seven possible scores, with lower scores indicating more support for traditional gender roles and higher scores indicating less support.

Step 2: Stating Hypotheses

Choose one of the two scales and state hypotheses about what differences you expect to find between men and women. Which gender will be more supportive (have a lower average score onabscale) of legal abortion? Why? Will men or women be more supportive (have a lower average score onfescale) of traditional gender roles? Why?

Step 3: Getting and Interpreting the Output

Run theIndependent Samples T Testas before with your chosen scale as theTest Variableandsexas theGrouping Variable. See the instructions forProject 1above.

Step 4: Interpreting Your Results

Summarize your results as inProject 1, step 6. Was your hypothesis confirmed? How?

Step 5: Extending the Test by Selecting an Additional Independent Variable

What other independent variable besides gender might be related to attitudes toward abortion or traditional gender roles? Select another independent variable besidessex,and conduct an additionalttest with eitherabscaleorfescaleas the dependent variable. Remember that thettest requires that the independent variable haveonlytwo categories. For variables with more than two categories (religorracecen1, for example), you can meet this requirement by using theDefine Groupsbutton in theGrouping Variablesbox to select specific categories of a variable. You could, for example, compare Protestants and Catholics onreligby choosing scores of 1 (Protestants) and 2 (Catholics).

Step 6: Stating Hypotheses

State a hypothesis about what differences you expect to find between the categories of your independent variable. Which category will be more supportive of legal abortion or more supportive of traditional gender roles? Why?

Step 7: Getting and Interpreting the Output

Run theIndependent Samples T Testas before with the scale you selected as theTest Variableand your independent variable as theGrouping Variable.

Step 8: Interpreting Your Results

Summarize your results as inProject 1, step 6. Was your hypothesis confirmed? How?