"Age and sex differences in relation between frugality and self efficacy" - Here there are 2 Independent variables - age and sex , each having 2 levels (I intend to keep adolescents and young adults as the 2 age groups). There are 2 Dependent variables - frugality and self efficacy.
Objectives :
1. Are there any age differences in frugality and self efficacy?
2. Are there any sex differences?
3. What is the relation between frugality and self efficacy?
Sample :
N = 100 Male ( 50 - adolescents, 50 - young adults ) ; 100 Females (50 - adolescents , 50 young adults )
Data analysis : I would be greatly obliged if I could get the views of Fellow Researchers and Professors about the statistics that are best suited for this research.
I was wondering if MANOVA would be suitable.
For 1 and 2, you can use MANOVA or ANOVA. ANOVA accepts only one dependent variable and will show you effects of age and sex on that single variable. If you need to see effects of age and sex on both frugality and self effficacy together then you should use MANOVA.
For 3 you can have a correlation or regression analysis. If you need to control for age and sex when finding the relationship between frugality and self efficacy, then you need to make a regression analysis including age and sex too.
Hi,
I suggest the multivariate covariance generalized linear models (McGLMs). In this framework, you can easily deal with bivariate data (two response variables). As part of the model, you can assess the effect of your covariates (age and sex) in each response variable and compute the correlation between responses taken into account the covariate effects. You can find more details in this paper
http://onlinelibrary.wiley.com/doi/10.1111/rssc.12145/full
The mcglm (https://cran.r-project.org/web/packages/mcglm/index.html) package in R fits McGLMs using an interface similar to the glm function.
Even, if your response variables are not Gaussian, you still can use the mcglm package for binary, binomial, bounded, count and continuous ones, including the case of mixed types of response variables. If you decide by pursue in this direction I can provide more examples and assist you in the analysis.
All the best!
Hi Shikpa - Assuming your DVs ar measured in interval data: from your overview, this is a two-way Manova, that is, two IVs with two DVs - such an analysis will tell you whether there is any interaction effect between your tow IVs and the DVs, and will also tell you if there is a main (simple) effect for each of the IVs on each on the DVs. If there are significant results, as you only have 2 levlee for each factor, no post hoc tests will be necessary, just look at the eans to see which group is higher /lower.
Re point three - this is simply a correlation.
Good luck, Kate
Thanks a ton to @Mehmet Sinan Iyisoy, @Kathleen (Kate) Anne Moore and @Wagner Hugo Bonat for all your valuable suggestions. Honestly, this means a lot !
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