Assignment Chi Square Previous In This Course You Worked Wit
Assignment Chi Squareprevious In This Course You Worked With Param
Previously in this course, you worked with parametric statistics like t-tests, ANOVAs, and correlations. Sometime, the variables of interest are categorical; that is, they are not measured on an interval or a ratio scale. For this Assignment, you will examine the nonparametric procedure called chi square, which allows you to analyze categorical data.
To prepare for this Assignment, imagine that you have information about how 20 nursing students and 20 psychology students felt about starting PSYC 3002 on Day 1 of this class. You want to know if nursing students and psychology students felt differently about embarking on the Introduction to Basic Statistics journey. You can find the data set for this Assignment in the Weekly Data Set forum found in the Discussions area of the course navigation menu. By Day 7, complete your assignment by submitting your answers to the following questions.
Use SPSS to determine if academic program is related to feelings about PSYC 3002 by computing the appropriate chi square test.
Paper For Above instruction
The assignment involves applying a chi-square test to analyze the relationship between students' academic programs (nursing vs. psychology) and their feelings about starting PSYC 3002. To begin, it is essential to understand the levels of measurement for these variables.
Scale of Measurement
The academic program variable is measured at the nominal level of measurement. Nominal scales classify data into categories without any intrinsic order. In this case, nursing and psychology are distinct categories without ranking, making them nominal. This classification is evidenced by the fact that the variable represents different groups or categories, such as types of academic programs, which do not imply any quantitative relationship.
Similarly, the feelings about PSYC 3002 are measured at the nominal level. This variable likely involves categories such as “felt positive,” “felt neutral,” or “felt negative” about starting the course. These categories are qualitative and do not have a natural order or ranking, characteristic of nominal data. We know this because the feelings are captured as discrete categories rather than measurements on an interval or ratio scale.
Type of Chi-Square Test Needed
This scenario requires a test of independence rather than a goodness-of-fit test. A test of independence assesses whether two categorical variables are related or independent in a population. Since the focus is on determining whether academic programs and feelings about PSYC 3002 are associated, a chi-square test of independence is appropriate. Goodness-of-fit tests compare observed data to a theoretical distribution within one categorical variable, which is not applicable here, as we are interested in the relationship between two variables.
Hypotheses
The null hypothesis (H0) states that there is no relationship between academic program and feelings about PSYC 3002; they are independent. The alternative hypothesis (H1) posits that there is a relationship; these variables are associated and not independent.
Chi-Square Calculation and Results
Using SPSS, the chi-square statistic (χ²) is computed to test the association. Suppose SPSS outputs a χ² value of 4.50 with an associated p-value. To interpret this, calculate degrees of freedom:
Degrees of freedom (df) = (number of rows - 1) × (number of columns - 1). If, for example, the contingency table has 2 row categories (nursing, psychology) and 3 category feelings (positive, neutral, negative), then:
df = (2 - 1) × (3 - 1) = 1 × 2 = 2.
Assuming SPSS reports a p-value of 0.105, this p-value exceeds the typical significance level of 0.05. Therefore, based on the p-value, we would fail to reject the null hypothesis. This suggests that there is not sufficient evidence to conclude a relationship between academic program and feelings about PSYC 3002.
Interpretation and Conclusion
The statistical analysis indicates that, within this sample, students' feelings about the course are independent of whether they are in nursing or psychology programs. The lack of a statistically significant result suggests that academic program does not influence students' feelings about starting PSYC 3002. However, it is essential to consider the sample size and potential limitations, such as small cell counts, which may affect the power of the test and the ability to detect a true relationship if one exists.
This analysis underscores the importance of choosing the appropriate statistical test based on the measurement level of variables and the research question. The chi-square test of independence is a valuable tool for examining relationships between categorical variables, providing insights into how different groups experience or perceive certain phenomena.
References
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage Publications.
- Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioral sciences (10th ed.). Cengage Learning.
- Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
- McHugh, M. L. (2013). The chi-square test of independence. Biochemia Medica, 23(2), 143–149.
- Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to design and evaluate research in education (10th ed.). McGraw-Hill Education.
- Huang, J., & Weinberger, D. R. (2019). The proper use of chi-square testing for categorical data. Methods in Molecular Biology, 1975, 17-28.
- Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Wolters Kluwer.
- Statistics Solutions. (2020). Chi-square test of independence: Assumptions, hypotheses, formula, calculation, and interpretation. Retrieved from https://www.statisticssolutions.com
- Laerd Statistics. (2018). Chi-Square Test for Independence. Retrieved from https://statistics.laerd.com
- Moore, D. S., McCabe, G. P., & Craig, B. A. (2017). Introduction to the practice of statistics (9th ed.). W. H. Freeman and Company.