Now that you have completed your data collection and are armed with your raw data, the data management and analysis can begin! Data management is an important step to successfully completing your results chapter. In many quantitative studies, composite scoring and assessing reliability are key steps in data management and analysis process.
Composite scoring involves combining the items that represent a variable to create a score, or data point, for that variable. For example, say we have a survey consisting of 15 questions to measure satisfaction. The survey breaks satisfaction down into three domains: mental, physical, and emotional. Each domain consists of five questions. To create a composite score for each of the domains, we will have to combine the respective five items to create a score for each domain, and we can combine the 15 items to make an overall satisfaction score. In most cases, you should combine the scores according to the instructions from the instrument developers. Most scores are calculated by either taking the sum of the items or the mean of the items included in each domain.
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Once you calculate the composite score, you can move forward with conducting a reliability analysis. The reliability analysis will allow you to assess how well the items work together to assess the variable of interest in your sample. Researchers commonly calculate the Cronbach’s alpha to evaluate the reliability of the items comprising a composite score. This statistic allows you to make a statement regarding the acceptability of the combination of items to represent your variable. Cronbach’s alphas of at least 0.7 indicate that the combination of items has acceptable reliability (George & Mallery, 2016).
Once you create your composite score and conduct your reliability analysis you are ready to proceed with data analysis!
George, D. & Mallery, P. (2016). SPSS for Windows step by step: a simple guide and reference, 11.0 update (14th ed.). Boston, MA: Allyn and Bacon.