This chapter discusses the evolution of the critical quantitative paradigm with an emphasis on extending this approach to new populations and new methods. Along with this extension of critical quantitative work, however, come continued challenges and tensions for researchers. This chapter recaps and responds to each chapter in the volume, and…

This chapter uses a critical quantitative approach to study models and measures of civic engagement for Latina/o college students. The chapter describes the importance of a critical quantitative approach to study civic engagement of Latina/o college students, then uses Hurtado et al.'s (Hurtado, S., 2012) model to examine the civic engagement…

A supportive campus culture is critical to institutionalizing civic engagement and instilling the principles of active citizenship. This chapter explores a model that quantitatively measures the impact of the campus environment on civic engagement outcomes.

This article is designed to give the reader a conceptual, nontechnical overview of estimation and model fit issues in multilevel modeling (MLM). The process of MLM generally involves fitting a series of multilevel models that increase in complexity. When conducting multilevel analyses, it is important to balance the need for complexity and the…

Multilevel modeling (MLM), also referred to as hierarchical linear modeling (HLM) or mixed models, provides a powerful analytical framework through which to study colleges and universities and their impact on students. Due to the natural hierarchical structure of data obtained from students or faculty in colleges and universities, MLM offers many…

Multilevel modeling offers researchers a rich array of tools that can be used for a variety of purposes, such as analyzing specific institutional issues, looking for macro-level trends, and helping to shape and inform educational policy. One of the more complex multilevel modeling tools available to institutional researchers is cross-classified…

The sophisticated analytical techniques available to institutional researchers give them an array of procedures to estimate a causal effect using observational data. But as many quantitative researchers have discovered, access to a wider selection of statistical tools does not necessarily ensure construction of a better analytical model. Moreover,…

Multilevel modeling provides several advantages over traditional ordinary least squares regression analysis; however, reporting results to stakeholders can be challenging. This article suggests some strategies for presenting complex, multilevel data and statistical results to institutional and higher education decision makers. The article is…

Understanding the suitability of multilevel modeling in the context of institutional research (IR) may ease the doubts of some IR professionals. However, the need for training on how to conduct and report multilevel modeling analysis remains. A major roadblock hindering the proliferation of multilevel modeling in IR is the perception that…

Most traditional models, frameworks, and findings that apply to the majority of students and faculty do not adequately apply to important subpopulations. The recommendations here will help researchers become more sensitive to the nuances among various educational subgroups, and to pay more attention to outliers.

Among the varied analytical challenges institutional researchers face, examining faculty pay may be one of the most vexing. Although the literature on faculty compensation analysis dates back to the 1970s (Loeb and Ferber, 1971; Gordon, Morton, and Braden, 1974; Scott, 1977; Braskamp and Johnson, 1978; McLaughlin, Smart, and Montgomery, 1978),…

The ultimate goal of the institutional researcher is not always to test a research hypothesis, but more often simply to find an appropriate model to gain an understanding of the underlying characteristics and interrelationships of the data. Exploratory data analysis provides a means of accomplishing this. (Author)

The data collected in higher education research are not always quantitative or continuous. Statistical methods using the log-linear model provide the institutional researcher with a powerful set of tools for addressing research questions when data are categorical. (Author/MSE)

Regression analysis is one of the most frequently used statistical techniques in institutional research. Principles of least squares, model building, residual analysis, influence statistics, and multi-collinearity are described and illustrated. (Author/MSE)

Path analysis and linear structural relations (LISREL) provide the institutional researcher with some extremely powerful statistical tools. However, they must be applied and interpreted carefully with a full understanding of their limitations and the statistical assumptions on which they are based. (Author)