Help with data analysis plan for dissertation

#1
Any help would be much appreciated :)

For my research project I'm hoping to do a moderation analysis and will be looking at if Variable M moderates the relationship between Variable X and Variable Y. All variables are continuous (scale) and were measured at only 1 time point.
My 3 hypotheses are:
1. Variable X predicts Variable Y
2. Both Variable X and Variable M predict Variable Y
3. Variable X and M interact to predict Variable Y (moderation)

I'm planning to use Hayes' PROCESS Macro to run the moderation analysis on SPSS but just wanted to know if there's anything I needed to do beforehand i.e run a multiple regression to check assumptions? Spent a good deal of time looking through Andy Field's book but am still a bit confused! :confused:
 

staassis

Active Member
#2
To the best of my knowledge, the PROCESS add-on assumes normally distributed residuals. If they are not normally distributed and the sample size is modest, you are better off estimating various models with interactions using standard SPSS functionality (standard SPSS edition). In many "linear" and "non-linear" sub-menus there, you will see the "Bootstrap" button. This will lead to more accurate statistical inference.

In general, be cautious about statistical software which was not developed by statistics experts but rather by social and medical scientists.
 
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hlsmith

Less is more. Stay pure. Stay poor.
#3
To me, moderation represents interactions not mediation. Are you referencing moderation or mediation or moderated-mediation? You really don't need a macro or anything special to examine moderation on the multiplicative scale. When examining for it on the additive scale a little extra coding my be necessary.
 
#4
Data analysis acts as a road-map for your research, and it is the most crucial task for any dissertation, and it involves a research question, research design, collection of data, analysing with a suitable method, inferring the conclusions. The most important out of all the above is the data collection and the analysis. Thus, more attention is needed before collecting the data because whatever the data you have at hand only gives you the inference, and it should be a valid one. Once the data is collected, the part is to understand the nature of data (continuous or discrete or categorical) and the level of measurements either it is a nominal interval, ordinal, or ratio scale. The next stage is to understand the data through the descriptive statistics by using appropriate tables and figures. Then, the next step is to select the appropriate statistical methodology to test the hypothesis and draw a valid and reliable conclusion. Note that the data analysis plan can vary from one field to other, so a lot of attention is required before conducting any statistical analysis.