My DV is count, obtained from psychological questionnaire (14 questions with options ”Yes”/”No”, each yes is 1 point, thus overall result is count of yeses).
I’ve checked that Poisson distribution isn’t exactly what I’ve got (data is a bit overdispressed) and NB fits better (in comparison to Poisson – log likelihood is closer to 0 and AIC/BIC values are lower. Deviance/df = 1,153; Pearson Chi-square/df = 0,96 – which seem fair enough to me). Although 14 is obviously a bound, no one scored more than 8, so I think it should not be a problem (also all groups had same questionnaire, so the same bound).
I have 4 groups (smallest n=28, biggest n=35), which i need to compare. My hypotesis has a form of planned orthogonal contrast (-3,1,1,1 and 0,1,1,-2). In the best scenario, I would love to also include some covariates in the model, that’s why I mentioned „Ancova”.
I’ve spent last week reading stats coursebooks and articles about count data – I've finished with impression that regression is a fair option. But stats teacher I asked about it said she had no idea what I am speaking about and that I should not contrive, just use Kruks-Wallis.
So I thought that maybe I misunderstood something and that's why I wanted to ask 3 questions:
- Assuming that distribution of the data is in fact negative binomial, is negative binomial regression an appropriate method to test an alternative hypotesis that groups differ?
- Assuming that distribution of the data is in fact negative binomial, should negative binomial regression be considered a better fitted test for such comparison than Kruks-Wallis? (more powerfull?)
- [If answer for 2 former questions was „yes-ish”] Is it right to implement planned orthogonal contrasts (-3,1,1,1 and 0,1,1,-2) by including 2 dummy variables coded for groups just like this (-3,1,1,1 and 0,1,1,-2) as covariates? I’m using SPSS.
- DV describes one’s Locus of Control
GENLIN LOC WITH Contrast1 Contrast2 /MODEL Contrast1 Contrast2 INTERCEPT=YES DISTRIBUTION=NEGBIN(MLE) LINK=LOG /CRITERIA METHOD=FISHER(1) SCALE=1 COVB=MODEL MAXITERATIONS=100 MAXSTEPHALVING=5 PCONVERGE=1E-006(ABSOLUTE) SINGULAR=1E-012 ANALYSISTYPE=3(WALD) CILEVEL=95 CITYPE=WALD LIKELIHOOD=FULL /MISSING CLASSMISSING=EXCLUDE /PRINT CPS DESCRIPTIVES MODELINFO FIT SUMMARY SOLUTION (EXPONENTIATED) /SAVE MEANPRED DEVIANCERESID.