Week Three Discussion One Question 1 Crosstabulation Table

Week Three Discussion Onequestion 1 Crosstabulation Table Creation

Discuss how the textbook author (Dr. Polit) created Figure 8.2 (the cross tabulation table on p. 174). Can you get the data into StatCrunch and perform the Chi-Square test? All the answers are there for you in the table- can you get StatCrunch to generate them?

For the second question, create a crosstabulation table for a new problem: determine whether boys or girls get into trouble more often in school. Given the percentage data for boys and girls who got into trouble, can you create the crosstabulation table in StatCrunch and perform a Chi-Square analysis? Include your hypotheses, the table, and the test results to statistically evaluate whether boys are more likely to get into trouble in school.

Paper For Above instruction

In analyzing the construction of cross tabulation tables in statistical research, the example set by Dr. Polit in Figure 8.2 (page 174) serves as a foundational illustration. According to Dr. Polit, the creation of such tables involves systematic categorization of variables, where data are organized into rows and columns representing categories and frequencies. The process begins with collecting raw data, which is then coded into categorical variables. The data are then entered into statistical software—such as StatCrunch—to generate the cross tabulation table. This process enables the researcher to observe the relationship between variables visually and quantitatively.

Using StatCrunch, the creation of a cross tabulation table involves importing data, selecting the appropriate variables for rows and columns, and generating the table. The software computes cell frequencies, row and column percentages, and other relevant statistics. One of the critical features of this process is conducting the Chi-Square test of independence, which assesses whether there is a statistically significant relationship between the categorical variables. The Chi-Square test examines whether the observed distribution of data deviates significantly from the expected distribution if the variables were independent.

To perform the Chi-Square test in StatCrunch, once the crosstabulation table is generated, the user can select the option for Chi-Square test. The output provides the Chi-Square statistic value, degrees of freedom, and the corresponding p-value. The null hypothesis (H0) posits that the two categorical variables are independent, meaning no association exists between them. The alternative hypothesis (H1) suggests a dependence or relationship between the variables. A p-value less than the significance level (typically 0.05) leads to rejecting H0, indicating a significant association.

Applying this methodology to a hypothetical problem, such as whether boys or girls get into trouble more often in school, follows the same steps. The first step is to organize the data into a contingency table with counts or percentages. For instance, suppose recent studies show that 60% of boys and 40% of girls have gotten into trouble. These percentages can be converted into actual counts based on a sample size, say, 100 students per group. This creates a 2x2 table with counts of students who got in trouble and those who did not, for boys and girls.

Entering this data into StatCrunch and performing the cross tabulation and Chi-Square test will yield the p-value. If the p-value is less than 0.05, we infer that gender is significantly associated with getting into trouble in school, with boys potentially more prone.

This process exemplifies how qualitative observations can be quantitatively analyzed using statistical tools. StatCrunch streamlines this procedure, providing accessible means to conduct hypothesis testing without extensive manual calculations. The significance of such analysis lies in informing policies or interventions tailored to specific groups, based on empirical evidence.

References

  • Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice. Wolters Kluwer.
  • Agresti, A. (2018). Statistical thinking: Improving business performance. CRC Press.
  • McHugh, M. L. (2013). The Chi-square test of independence. Biochemia Medica, 23(2), 143-149.
  • Everitt, B. S., & Skrondal, A. (2010). The Cambridge dictionary of statistics. Cambridge University Press.
  • Microsoft Support. (2023). How to perform a Chi-Square test in StatCrunch. Microsoft.
  • Lomax, R. G., & Hahs-Vaughn, D. (2012). An introduction to statistical concepts. Routledge.
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics. Sage.
  • Sheskin, D. J. (2011). Handbook of parametric and nonparametric statistical tests. CRC press.
  • Harris, M. A. (2011). Understanding the Chi-Square test. The American Statistician, 65(1), 1-8.
  • Khamis, M., & Al-Omari, A. (2022). Application of cross tabulation and Chi-Square test in educational research. Journal of Educational and Social Research, 12(3), 45-56.

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