---
title: "Data analysis"
chunk: 4/5
source: "https://en.wikipedia.org/wiki/Data_analysis"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:43:31.459045+00:00"
instance: "kb-cron"
---

=== 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:

==== 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), normal imputation is needed.

Analysis of extreme observations: outlying observations in the data are analyzed to see if they seem to disturb the distribution.
Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.
Test for common-method variance. The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.

==== Quality of measurements ====
The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature.
There are two ways to assess measurement quality:

Confirmatory factor analysis
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

==== Initial transformations ====
After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase.
Possible transformations of variables are:

Square root transformation (if the distribution differs moderately from normal)
Log-transformation (if the distribution differs substantially from normal)
Inverse transformation (if the distribution differs severely from normal)
Make categorical (ordinal / dichotomous) (if the distribution differs severely from normal, and no transformations help)

==== Did the implementation of the study fulfill the intentions of the research design? ====
One should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups.  If the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in the sample.Other possible data distortions that should be checked are:

dropout (this should be identified during the initial data analysis phase)
Item non-response (whether this is random or not should be assessed during the initial data analysis phase)
Treatment quality (using manipulation checks).

==== Characteristics of data sample ====
In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the size of the subgroup 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

==== 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?

==== Analysis ====
Several analyses can be used during the initial data analysis phase:

Univariate statistics (single variable)
Bivariate associations (correlations)
Graphical techniques (scatter plots)
It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:

Nominal and ordinal variables
Frequency counts (numbers and percentages)
Associations
circumambulations (crosstabulations)
hierarchical loglinear analysis (restricted to a maximum of 8 variables)
loglinear analysis (to identify relevant/important variables and possible confounders)
Exact tests or bootstrapping (in case subgroups are small)
Computation of new variables
Continuous variables
Distribution
Statistics (M, SD, variance, skewness, kurtosis)
Stem-and-leaf displays
Box plots

=== Main data analysis ===
In the main analysis phase, analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.