EXCEL PROJECT FOR DATA ANALYSIS

Initial data analysis The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:[25] Quality of data The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms, n: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable. . There are two ways to assess measurement: [NOTE: only one way seems to be listed] Analysis of homogeneity (internal consistency), which gives an indication of the reliability of a measurement instrument. During this analysis, one inspects the variances of the items and the scales, the Cronbach's α of the scales, and the change in the Cronbach's alpha when an item would be deleted from a scale n any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis phase. The characteristics of the data sample can be assessed by looking at: Basic statistics of important variables Scatter plots Correlations and associations Cross-tabulations[31] Final stage of the initial data analysis During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken. Also, the original plan for the main data analyses can and should be specified in more detail or rewritten. In order to do this, several decisions about the main data analyses can and should be made: In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method? In the case of missing data: should one neglect or impute the missing data; which imputation technique should be used? In the case of outliers: should one use robust analysis techniques? In case items do not fit the scale: should one adapt the measurement instrument by omitting items, or rather ensure comparability with other (uses of the) measurement instrument(s)? In the case of (too) small subgroups: should one drop the hypothesis about inter-group differences, or use small sample techniques, like exact tests or bootstrapping? In case the randomization procedure seems to be defective: can and should one calculate propensity scores and include them as covariates in the main analyses?[32] Analysis Several analyses can be used during the initial data analysis phase:[33] Univariate statistics (single variable) Bivariate associations (correlations) Graphical techniques (scatter plots)