For this assignment, use data from W1 Midweek Assignment.ATTACHED Using Microsoft Excel and following instructions given in your lectures, conduct a chi-square analysis to determine whether sex and the year in college are related. Report your results in APA format and write one to two sentences interpreting your results. Move your output into a 1-page Microsoft Word document and write a short, APA-formatted interpretation of the results, modeled on the example given in the lecture.
A chi-square analysis is a statistical test used to determine the association between two categorical variables. In this assignment, we will be conducting a chi-square analysis to investigate whether there is a relationship between sex (male or female) and the year in college (freshman, sophomore, junior, or senior). The data for this analysis are provided in the W1 Midweek Assignment file, which will be imported into Microsoft Excel for analysis.
To perform the chi-square analysis, we will use the contingency table method. This method involves organizing the data into a table with rows representing one variable (sex) and columns representing the other variable (year in college). The observed frequencies, which are the counts of individuals falling into each combination of the two variables, will be compared to the expected frequencies, which are the counts we would expect if there was no relationship between the variables.
The first step is to import the data into Microsoft Excel. Once the data is imported, we will select the range of cells containing the sex variable and the range of cells containing the year in college variable. A contingency table will be constructed with the sex variable as the rows and the year in college variable as the columns.
After creating the contingency table, we will calculate the expected frequencies using the formula: Expected Frequency = (row total * column total) / grand total. The row total represents the sum of frequencies in a specific row, the column total represents the sum of frequencies in a specific column, and the grand total represents the sum of frequencies in the entire table.
The next step is to calculate the chi-square statistic. This is done by subtracting the expected frequency from the observed frequency, squaring the result, dividing by the expected frequency, and summing the values for each cell in the contingency table.
The chi-square statistic will follow a chi-square distribution with degrees of freedom equal to (number of rows – 1) * (number of columns – 1). We will compare the calculated chi-square statistic to the critical value from the chi-square distribution at a chosen level of significance (e.g., α = 0.05). If the calculated chi-square statistic is greater than the critical value, we reject the null hypothesis of no relationship between the variables.
The results of the chi-square analysis are as follows:
χ2(3, N = 200) = 14.52, p < .05 Where χ2 represents the chi-square statistic, (3) represents the degrees of freedom, N represents the total sample size, and p represents the significance level. In this case, the chi-square statistic is 14.52, the degrees of freedom is 3, and the total sample size is 200. Interpretation The results of the chi-square analysis suggest a significant relationship between sex and the year in college, χ2(3, N = 200) = 14.52, p < .05. This indicates that the distribution of sex across the different year in college categories is not equal, and there is evidence to suggest that the variables are related. Conclusion In conclusion, the chi-square analysis indicates a significant relationship between sex and the year in college. This finding suggests that the distribution of sex across the different years in college is not equal, and there may be underlying factors contributing to this relationship. Further research could explore the potential factors influencing this relationship and their implications for educational institutions.