Researchers must carefully screen data before conducting statistical analysis to address issues that could impact results. This involves evaluating accuracy, assessing effects of missing or outlier data, and determining if the data fits assumptions. Specifically, researchers should check for errors, missing patterns, extreme outliers, normality, linear relationships, and equal variability across values. Addressing these quality issues through data screening allows researchers to have confidence in their analysis and conclusions drawn from the data.

1. Pre-Analysis
Data Screening

2. Epilogue
➢ Researchers must consider several important issues before he or she wishes
to subject data to multivariate analysis.
➢ Issues must be carefully considered, analyzed and addressed prior to the
actual statistical analysis; only after these quality assurance issues have been
examined can the researcher be confident that the main analysis will be an
honest one, which will eventually result in effective conclusions derived from
the data.

3. Why Screen Data?
1. To evaluate the accuracy of the data
2. To assess the effects of missing data
3. To assess the effects of extreme values on the analysis
4. To assess the adequacy of fit between the data and the assumptions of the
specific procedure.

4. Evaluating the Accuracy of Data
❑ The results of any statistical analysis are only as effective and reliable as the
data analyzed regardless of the data collected.
❑ If inaccurate data are used, the researcher will not be able to distinguish the
extent to which the results are valid simply by examining the output

5. Assessing Effects of Missing Data
❑ Why do we have missing data?
❑ Equipment failure
❑ Incomplete responses
❑ Data entry
❑ Even though errors do occur within research many researchers fail to
understand the gravity errors have on the overall outcome of the research.
❑ The best thing to do when a data set includes missing data is to examine it.
❑ Using data that are available, a researcher should conduct tests to see if
patterns exist in the missing data

6. Assessing Effects of Missing Data
❑ Delete cases or variables that have caused a problem if only a few has been
missing
❑ If the situation is where the missing values may be concentrated to only a few
variables, then deleting the entire variable from the data file may be an option
considering that it does not affect the overall analysis and results
❑ We estimate the missing values and then use these values during the main
analysis.
❑ Prior knowledge
❑ Calculate the mean
❑ Using a regression approach involves several independent variables are used
to develop an equation that can be used to predict the value on a dependent
variable
❑ Objective, yet unrealistic
❑ The predictors must be good ones!

7. Analyzing the Effects of Outliers
❑ Why do we have outliers?
❑ Data entry error
❑ The subject is not part of the population of interest
❑ The subject is not
❑ The problem with outliers is that they can distort the results of a statistical test.
This is due largely to the fact that many statistical procedures rely on squared
deviations from the mean
❑ A single outlier, if extreme enough, can cause the results of a statistical test to
be significant or insignificant depending on the extreme and can seriously
affect the values of correlation coefficients.
❑ Outliers can exist in both univariate and multivariate situations, among
dichotomous and continuous variables, and among independent variables as
well as dependent variables

8. Analyzing the Effects of Outliers
❑ Why do we have outliers?
❑ Data entry error
❑ The subject is not part of the population of interest
❑ The subject is not
❑ The problem with outliers is that they can distort the results of a statistical test.
This is due largely to the fact that many statistical procedures rely on squared
deviations from the mean
❑ A single outlier, if extreme enough, can cause the results of a statistical test to
be significant or insignificant depending on the extreme and can seriously
affect the values of correlation coefficients.
❑ Outliers can exist in both univariate and multivariate situations, among
dichotomous and continuous variables, and among independent variables as
well as dependent variables

9. Analyzing the Effects of Outliers
❑ Univariate outliers can also be detected through statistical methods by
standardizing all raw scores in the distribution.
❑ Multivariate outliers are more subtle and therefore, more difficult to identify

10. Assessing the Adequacy of Fit
❑ To meet the robustness, the following must be met:
❑ Normality
❑ Graphical methods such as histogram or a QQ plot.
❑ Kolmogorov-Smirnov/Shapiro-Wilk (must be > .05)
❑ Linearity
❑ Assessed by depicting the straight line relationship between data
❑ Creating a scatterplot for every variable
❑ The assumption of linearity is important in multivariate analyses due
to the fact that many if the analysis techniques are based on linear
combinations of variables
❑ Homoscedasticity
❑ Homoscedasticity is the assumption that the variable in scores for
one continuous variable is roughly the same as all the values of
another continuous variable.
❑ Levene’s test provides a test of the hypothesis that the samples
come from populations with the same variances (must be > .05)