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Analyzing Evaluation Data

Any statistician will indicate that the statistical analysis of any research should be planned at the same time as the project is designed. While you may have access to in-house statisticians to help you, an understanding of basic and various statistical methods is essential. The following resources provide information on analyzing evaluation data.

Analyzing Likert Data
A Journal of Extension article by Boone and Boone, West Virginia University, on the correct analysis of Likert scale data. 2012.

Strategies for Generalizing Findings in Survey Research
A Journal of Extension article by Radhakrishna and Doamekpor, The Pennsylvania State University, on strategies to enhance the external validity of your study. 2008.

Simple Statistics for Correlating Survey Responses
A Journal of Extension article by Hollingsworth, et. al, focusing on the rank-sum test to determine if there is a statistically significant association between categorical survey responses provided for two different survey questions. 2011.

Cronbach's Alpha: A Tool for Assessing the Reliability of Scales
A Journal of Extension article by Santos, Texas A&M, on the use of the ALPHA option of the PROC CORR procedure from SAS® to assess and improve upon the reliability of variables derived from summated scales. 1999.

Handbook of Biological Statistics
This on-line training guide by John H. McDonald, University of Delaware, presents how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. Great overview and specific information on statistical analyses, and examples with step by step instruction make this very hands on and understandable. 2014.

Using R-project for Free Statistical Analysis in Extension Research
This Journal of Extension article by Salvatore S. Mangiafico, Rutgers University, focuses on R-project, a free, cross-platform, powerful computing environment capable of performing statistical analyses using a graphical user interface. An overview of R-Platform and example of its use is included. The author indicates the platform may be difficult to use by beginners. 2013.

An R Companion for the Handbook of Biological Statistics
This guide by Salvatore Mangiafico, Rutgers Cooperative Extension, is intended to be a supplement for The Handbook of Biological Statistics. It provides code for the R statistical language for the examples given in the Handbook. Included are chi-square, G-test, descriptive statistics, t-test, anova, non-parametric analyses, regression, logistic regression, non-linear regression, post-hoc tests, multiple comparisons. R is free, cross-platform software.

Analyze Data
This on-line training guide by The Pell Institute for the Study of Opportunity in Higher Education, the Institute for Higher Education Policy, and Pathways to College Network, focuses on analyses of quantitative and qualitative data. Part of a on-line training series titled "Evaluation Toolkit"; see Training Materials section for details.