When analyzing questionnaire data, choosing the right statistical methods is key to getting meaningful insights. Here’s how I usually approach it:
1. First, clean the data
2. Descriptive stats come next ; Simple stuff like frequencies, mean, standard deviation, and distribution patterns give a quick overview of the responses.
3. Reliability check is crucial so I usually go for Cronbach’s Alpha (≥0.7 is a good benchmark) to see if the questionnaire is internally consistent. Split-half reliability and test-retest reliability can also help.
4. Factor analysis helps a lot, If I’m dealing with multi-item scales, I use Exploratory Factor Analysis (EFA) to spot patterns and Confirmatory Factor Analysis (CFA) to validate them.
5. Depending on the research question, I use t-tests, ANOVA, chi-square tests, correlations, or even regression analysis to find relationships between variables.
6. Advanced techniques when needed (I didn't tried yet) – Structural Equation Modeling (SEM) is great for complex models, Item Response Theory (IRT) helps with assessing item difficulty, and cluster analysis can group similar respondents.
The approach really depends on the nature of the data and what we’re trying to find. Happy to discuss ....
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