The final chapter of the textbook covers logistic regression, a statistical test used when the dependent variable is dichotomous or binary.OLS regression should not be used when the dependent variable is binary.The first discussion focuses on the limitations of OLS in this situation.The logit equation is presented and then steps for conducting a logistic regression in the R Commander are explained.Interpretation of the logistic regression output using odds ratios, percent change in odds, and predicted probabilities is discussed.Applied examples are used to better illustrate when to use logistic regression.

The chi-square test is used when both the dependent and independent variables are measured at the nominal level.The first step to running a chi-square test is to construct a contingency table.Students are instructed on how to do so by hand and with the R Commander.Assumptions of the chi-square test follow.Running the chi-square test in the R Commander is then discussed along with interpretation and statistical significance.The chapter concludes with limitations of the chi-square test.

Central tendency describes the typical value of a variable.Measures of central tendency by level of measurement are covered including the mean, median, and mode.Appropriate use of each measure by level of measurement is the central theme of the chapter.The chapter shows how to find these measures of central tendency by hand and in the R Commander with detailed instructions and steps.Skewed distributions and outliers of data are also covered, as is the relationship between the mean and median in these cases.

Best practices in data acquisition and entry are the central theme of this chapter.Correct entry of variables and data in spreadsheets like Excel is discussed along with common problems of data entry that may prevent software from reading and analyzing data correctly. Typical practices of entering data for nominal, ordinal, and interval variables give the student information on how to enter data in Excel for these variables.The purpose of codebooks and composing them to match data are discussed.Different types of data including cross-sectional, time-series, and panel are presented to the student.Finally, common sources of public administration data are listed and described.

The introductory chapter introduces students to contemporary issues in public administration research like Covid-19, environmental problems, social equity, public service motivation, and general challenges in public service.These contemporary issues and challenges have been identified by the National Academy of Public Administration.The chapter discusses how data can be manipulated to tell a particular side of a story. Therefore, data and research ethics are also covered. Students are introduced to ethics in human subjects research and associated best practices.

When the effect of one independent variable on the dependent variable is conditional upon values of another dependent variable, we have an interactive relationship.If the effect of one variable on the dependent variable changes across various values of a second independent variable, we have an interactive relationship.This chapter provides examples of interactive relationships and how to model them using an interaction term in a linear regression.Attention is given to how to interpret interaction terms in linear regression and statistical significance for both interactions with interval level variables and dummy variables.Marginal effects graphs are illustrated to further explain interactive relationships.

Dispersion describes the spread of the data or how it varies from its mean.The chapter begins with the calculation of the variance and then the more important standard deviation, along with their interpretation.Students learn how to calculate these measures by hand and in the R Commander.Other measures of dispersion like skewness and kurtosis are described.The range and interquartile range are also calculated using the R Commander software for ordinal variables.

The chapter compares and contrasts various statistics software including R, STATA, SPSS, and the R Commander.Detailed instructions on how to download R, R Studio, and the R Commander package are provided so that students can use the R Commander for the remainder of the book.Uploading data into the R Commander and basic data recoding are discussed.

The chapter begins with an applied example describing the limitations of bivariate regression and the need to include multiple independent variables in a regression model to explain the dependent variable.The logic of multivariate regression is discussed as it compares to bivariate regression.Running a multivariate regression in the R Commander and interpretation of the results are the main foci of the chapter, with particular attention to the beta coefficients, y-intercept, and adjusted R-squared.Generating the multivariate regression equation from the R Commander output is covered, along with engaging in prediction using this equation.Finally, interpretation of dummy independent variables in a multivariate regression is covered.

The chapter covers the use of ordinal dependent variables like Likert scale measures for research hypotheses.The Wilcoxon Rank Sum test is described using a public administration example.Students learn how to conduct the rank sum test by hand and with the R Commander.Interpretation and statistical significance are the foci of the R Commander output.The Wilcoxon Signed Rank test is explained as is how it differs from the Rank Sum Test.Instructions for conducting and interpreting the Signed Rank test in the R Commander are included.

Students are instructed on how to create the most common graphs in public administration research and data visualization.Correct data setup for each graph is illustrated through the use of datasets in the Companion Site.Graphs include bar graphs, histograms, line graphs, boxplots, and scatterplots.Steps to produce these graphs in R Commander are covered using public administration examples and datasets.