I am researching the relationship between social media and the sexual behaviour of secondary school students. One of my null hypotheses is that "There is no moderating impact of sex in the relationship between social media and sexual behaviour"
What method of analysis can I use? and how do I go about it
1. Data Collection&Ethical Considerations
2. Operationalize Variables
Social Media Usage: Frequency, duration, types of platforms used, etc.
Sexual Behavior: This could include attitudes, knowledge, activities, etc.
Sex (Gender): Male, Female, Other (if applicable).
3. Statistical Analysis
Moderation Analysis: This involves testing whether the relationship between two variables (social media and sexual behavior) changes across levels of a third variable (sex).
Methods:
Multiple Regression Analysis: Include interaction terms between social media usage and sex.
ANOVA (Analysis of Variance): If your data is categorical, to test the differences among group means.
Software: Use statistical software like SPSS, R, or Python for the analysis.
4. Interpretation
Significance of Interaction Term: If the interaction term in your regression model (social media usage * sex) is statistically significant, it suggests that the impact of social media on sexual behavior differs by sex.
Thank you Yuna Yao for your response. Someone mentioned using Fisher-Z statistics. Can it still be used? If yes, how do I go about it?
The Fisher-Z statistical method can be used to study the null hypothesis you proposed. In this case, your research focuses on the relationship between social media use and sexual behavior among secondary school students, with a particular consideration of gender as a potential moderating variable. The Fisher-Z method can help you assess differences in the correlation between social media use and sexual behavior across different gender groups.
First, you need to calculate the correlation coefficients between social media use and sexual behavior separately for male and female student groups. This can be done through correlation analysis, such as using Pearson's correlation coefficient.
Then, use the Fisher-Z transformation to convert these correlation coefficients into Z-scores. This step can be completed using the formula previously mentioned, which involves applying the following transformation to each correlation coefficient:Z=1/2ln[(1+r)/(1-r)]
Here, r represents the correlation coefficient, and ln is the natural logarithm.
After obtaining the Z-scores for the two gender groups, you can use a statistical test (like a z-test) to compare these scores. This can help you determine whether the impact of social media on sexual behavior shows a significant difference between genders. If the test results show a significant difference between the two Z-scores, it suggests that gender moderates the relationship between social media use and sexual behavior. If there is no significant difference, it supports your null hypothesis that gender does not moderate this relationship.
So,the Fisher-Z statistical method is suitable for your research needs, as it can help determine whether gender plays a moderating role in the relationship between social media use and sexual behavior.
Thank you so much Yuna Yao Your suggestion is not different from that suggested by Pallant in her statistical book. Could you please suggest more published articles that could be used to support the use of Fisher-Z statistics?
You are right. My suggestions are not different from those in reference books. Based on your response this time, I believe you have already read many journal articles. Perhaps when someone send articles to you, you might say that the articles I recommend are no different from what you have found yourself. Therefore, please clarify what problems you have encountered in your research process. Sending vague, general questions is a waste of time and effort for both parties. Thank you for your feedback, which lets me know that my help is not so needed. I wish you success in your work!
The answers sound more like AI generated answers, but this is just my humble opinion....
A test for differences between correlations is not suggested (i.e. subgroup analysis), since you lose power. With an moderation analysis within multiple regression you can test the whole sample together. Please find a comprehensive introduction to moderation (and other techniques) in Hayes (2022).
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.
Thank you so much @Rainer Duesing for your response and recommendation. I will get the book to study moderation analysis better.