Question

State at least two research problems or question that requires the use of the Chi-Square Technique

Answer 1

The Chi-Square test is a statistical procedure used by researchers to examine the differences between categorical variables in the same population.

For example, imagine that a research group is interested in whether or not education level and marital status are related for all people in the U.S.

After collecting a simple random sample of 500 U.S. citizens, and administering a survey to this sample, the researchers could first manually observe the frequency distribution of marital status and education category within their sample.

Market researchers use the Chi-Square test when they find themselves in one of the following situations:

• They need to estimate how closely an observed distribution matches an expected distribution. This is referred to as a “goodness-of-fit” test. 

• They need to estimate whether two random variables are independent.

  

The Chi-Square test is most useful when analyzing cross tabulations of survey response data.

Because cross tabulations reveal the frequency and percentage of responses to questions by various segments or categories of respondents (gender, profession, education level, etc.), the Chi-Square test informs researchers about whether or not there is a statistically significant difference between how the various segments or categories answered a given question.

The chi-square (x2) is a test of significance for categorical variables. Significance tests let the researcher know what the probability is that a given sample estimate actually mirrors the entire population. The chi-square can be used as a goodness-of-fit test, in univariate analysis, or as a test of independence, in bivariate analysis. The latter is the most generally used. In this case, the test measures the significance of the relationship between two categorical variables, representing the first step toward bivariate analysis.