Social Statistics

Preference for Affirmative Action Policies

Professor A. Krieger

American Public University

Introduction

There has been a longstanding standard of institutional injustice throughout America’s history that has devastated and crippled entire groups of people. As a means to help rectify the past, and level the playing field for future success, affirmative action plans were enacted and aimed at “providing equal protection and equal access to members of these groups that have suffered the injustices and indignities of discrimination” (Cloud, 2006, p. 35). According to Department of Labor statistics, blacks are almost twice as likely as whites to be unemployed and when employed, make substantially less than their white counterparts (ACLU, 2012).

Many critics argue that affirmative action policies are still needed to remedy and compensate minorities for centuries of being disenfranchised while others think that enough preferential treatment has occurred. Some concede that these policies and practices lower standards of accountability needed to push employees to perform better, and are condescending to minorities who may be looked at as recipients of a system of preferential treatment as opposed to the reflection of hard work and personal achievements. Consequently, when minorities benefit from this perceived preferential treatment they must work twice as hard for the respect of their colleagues and peers (Messerli, 2010).

The purpose of this study is to see if gender influences a person’s preference to affirmative action policies.

General Social Survey

The General Social Survey (GSS) is a national survey that includes the responses of 2,044 adult Americans selected as a representative sample. The representative sample means that the data collected by these individuals is representative of the American population of adults 18 years and older. Although there is always the possibility of sampling error, the sampling error in this data set is plus or minus only a few percentage points (Babbie et al., 2013). Data from this survey began collection in 1972 and has collected data on more than 5,000 topics (variables) since. The GSS data were collected in face-to-face household interviews (Babbie, et al., 2013). The data for this research comes from the 2006 GSS dataset.

Variables

Here is the corresponding question for AFFRMACT that was pulled from the survey verbatim:

Literal Question

153. A. Some people say that because of past discrimination, blacks should be given preference in hiring and promotion. Others say that such preference in hiring and promotion of blacks is wrong because it discriminates against whites. What about your opinion -- are you for or against preferential hiring and promotion of blacks?

IF FAVORS: A. Do you favor preference in hiring and promotion strongly or not strongly?

IF OPPOSES: B. Do you oppose preference in hiring and promotion strongly or not strongly?

Descriptive Text

AFFRMACT AA, and AB are recoded into a single variable, AFFRMACT. Hand Card AL contained responses 1-5.

Summary Statistics

The nominal variable SEX will be used as (one of) the independent variable(s).

Summary Statistics

Frequencies and Histograms

The histogram below shows the data on the variable preference towards affirmative action policies.  A histogram is a form of displaying data graphically and is particularly useful when displaying data with a relatively large number of categories (Aron, et al., 2014). The data in the histograms graphically presents the information about the variable.

A frequency distribution is a numeric display of the number of times (frequency) and relative percentage of times each value of a variable occurs in a given sample (Aron, et al., 2014).  Below is the frequency table for the AFFRMACT. From this table I can determine that 188 (9.9%) people “strongly support”, 136 (7.1%) “support”, 524 (27.6%) “oppose”, and 1055 (55.4%) “strongly oppose” affirmative action policies.

Crosstabulation

A crosstabulation is a matrix that shows the distribution of one variable for each category variable. Crosstabs offer the researcher a ‘snapshot’ of possible correlations between two variables of interest. In this case, for instance, is gender (SEX) related to or associated with the preference towards affirmative action policies (AFFRMACT)?

The crosstabs suggest that women are a little more likely than men to support affirmative action policies, but knowing the respondent’s gender does not help us enough in determining whether they support affirmative action policies. In reading across the first row, we see that women (11%) are more likely than men (8%) to strongly support affirmative action policies.

Research Questions

Research Question: Is there a difference among people when it comes to showing support for affirmative action policies?

H1- A person’s gender will influence their preference towards affirmative action policies.

H0 - A person’s gender will not influence their preference towards affirmative action policies.

Independent Variable- Gender

Dependent Variable- Preference to affirmative action policies

Correlations

Correlations refers to a relationship between two or more variables.  Correlational studies search for relationships but cannot prove that one variable causes a change in another variable. In other words, correlation does not equal causation. Researchers use the correlation coefficient as a measure of correlation strength. This number can range between -1.00 to +1.00.

Gamma is a test to measure the strength of association between ordinal variables or in the case where there is a mixture of nominal and ordinal variables. In this study, one’s gender is a nominal variable but the preference to affirmative action policies is ordinal. As such, gamma was used to determine the correlation strength here. The gamma measure is -.076, or 7% and has been identified as a weak and uninteresting negative association.

Because the relationship between one’s gender and their preference to affirmative action policies was determined to be “weak and uninteresting” based on the gamma measure, race will be investigated. I ran the same test but replaced SEX with RACE.

Results focusing on RACE as an independent variable showed a stronger relationship. The gamma measure determined that the relationship between one’s race and their preference for affirmative action (-.377 or 37%) is evidence of a strong association and extremely interesting. This can also be interpreted as “if I were to try to attempt to guess one’s preference for affirmative action policies, I would be 37% more likely to guess right by relying on one’s race”. As such, the research hypothesis for the test of significance will be rewritten as follows:

H1- A person’s race will influence their preference towards affirmative action policies.

H0 - A person’s race will not influence their preference towards affirmative action policies.

Independent Variable- race

Dependent Variable- Preference to affirmative action policies

Hypothesis Testing

Hypothesis testing is an efficient approach using systematic procedures to determine whether the results of a study support the hypothesis of the research. Researchers determine their hypothesis with their population in mind. There are five steps to hypothesis testing:

1. Restate the question as a research hypothesis and a null hypothesis about the populations;

2. Determine the characteristics of the comparison distribution;

3. Determine the cutoff sample score on the comparison distribution at which the null hypothesis should be rejected

4. Determine your sample’s score on the comparison distribution;

5. Decide whether to reject the null hypothesis.

Tests of Significance

Chi-Square

The Chi-square test is a test of significance which is typically appropriate for two nominal variables, ordinal variables, or a mix between the two (Babbie et al., 2013). This test is one of the most widely used and estimates the probability that the association between variables is a result of random chance or sampling error by comparing the actual or observed distribution of responses with the distribution of responses we would expect if there were absolutely no association between two variables.

The significance level of .000 (for the Chi-Square value of 210.656) is less than the .05 cutoff. Thus, we reject the null hypothesis. This means that we can conclude that the preference for affirmative action policies is different according to one’s race.

Conclusion

Affirmative action has broken down the barriers of inequality in a resistant two-tiered society, workforce, and school system. There certainly have been changes made. More minorities are enrolled in programs of higher education, employed in leadership positions, and belong to the middle-class as do many nonminorities (ACLU, 2012). Diversity is desirable and will not always occur if left to chance and individuals, workers or students, starting at a disadvantage need a boost. Minorities often start out their lives at a slight disadvantage than their nonminority counterparts in both education and employment. Historically, they commonly come from lower-income families and have fewer opportunities than whites (Messerli, 2010; ACLU, 2012), yet may be every bit as capable as white employees and students, but may not reflect such qualifications on paper.

Unfortunately, there are negative social perceptions surrounding affirmative action policies. A society cannot be color-blind and still consider race when making decisions. There is no association, correlation, or causation found between race and ability for job or school performance. This connection is part, if not the main reason why affirmative action policies were conceived. To then use it to catapult a minority into a position that he/she may not be prepared for or capable of goes against the very foundation and essence of the act.

Credentialism and meritocracy may be overshadowed by the minority status of an individual; the most capable candidate is no longer the most qualified when race and gender are considerations. Some concede that affirmative action policies lead to reverse discrimination. Contrary to most stereotypes, many minorities fall into middle or upper class while many whites live in poverty. Finally, these policies and practices are seen to lower standards of accountability needed to push students or employees to perform better. Many see them as condescending to minorities who may be looked at as recipients of a system of preferential treatment as opposed to the reflection of hard work and personal achievements. When minorities benefit from this perceived preferential treatment they must work twice as hard for the respect of their colleagues, classmates, and peers (Messerli, 2010).

Based on the statistical analysis of the data found in the General Social Survey, there is a relationship between one’s race and their preference to affirmative action policies. Researchers can use this data in their exploration into the current state of society and affirmative action policies. Perhaps follow-up research can focus on perceptions of affirmative action policies beyond one’s support or why the support is or is not there. Moving forward, researchers can also focus on any perceptions that may emerge based on one’s own racial make-up or designation to explain the support or lack thereof.

References