Finding the relationship between Negative and Positive Z-Scores

Question

My advisor asked me to find the relationship between our research's dependent variables and participants who scored between 0 to 3 and 0 to -3 in Z-Score table. I recorded these participants into two different groups like participants 0-3 --> 1 and 0 to -3 -->2 and ran a correlation analysis. However, it did not work. Do you have any idea or suggestions to complete this analysis?

Answer 1

It appears that you are attempting to investigate the relationship between your research's dependent variables and participants who fall within specific Z-score ranges (0 to 3 and 0 to -3). However, using correlation analysis might not be the most appropriate statistical method for this specific research question.

Correlation analysis is typically used to assess the linear relationship between two continuous variables. It measures the strength and direction of a linear association between variables, and it's not designed for comparing groups or categories.

To explore the relationship between your dependent variables and participants within the specified Z-score ranges, you might consider the following approaches:

1. Group Comparison Tests: To compare the means or other summary statistics of your dependent variables between the two groups (participants with Z-scores between 0 to 3 and 0 to -3), you can use group comparison tests such as:
2. Independent Samples T-Test: Use this test if you have two groups and want to compare means.
3. ANOVA (Analysis of Variance): If you have more than two groups or subcategories within your Z-score ranges, ANOVA can help you assess whether there are significant differences in means among the groups.
4. Chi-Square Test: If your dependent variables are categorical or nominal, you can use a Chi-Square test to examine the association between Z-score groups and categorical variables.
5. Logistic Regression: If you want to predict the likelihood of an event or outcome based on Z-score group membership and other predictor variables, logistic regression can be suitable.
6. Non-parametric Tests: If your data does not meet the assumptions of parametric tests (e.g., normality or homogeneity of variance), consider non-parametric alternatives like the Mann-Whitney U-test (for two groups) or the Kruskal-Wallis test (for more than two groups).
7. Visualization: Creating appropriate visualizations, such as box plots, histograms, or bar charts, can help you explore and illustrate the differences between the Z-score groups effectively.

Before conducting any analysis, it's crucial to ensure that your data meets the assumptions of the chosen statistical test and that you appropriately preprocess the data if necessary (e.g., handling outliers or missing data).