Introduction

*This image was created using napkin.ai; however, the concept, design direction, and creative vision were conceived by Dr. Knight and Chris Cardenas

There are a variety of methods for designing research studies that use statistics to document human behavior, attitudes, or emotions. In this chapter, we will discuss a variety of flexible and common methods.

We begin with an overview correlational designs, which help researchers explore the strength and direction of relationships between variables. You’ll learn about the limits of correlation—particularly that it cannot establish causality—and the importance of identifying and controlling for confounding variables.

Next, the chapter turns to  survey research, one of the most common methods of data collection used for correlational designs. You’ll explore different types of surveys, such as cross-sectional and longitudinal studies, and learn about the unique challenges of collecting reliable data through self-administered questionnaires. We’ll also examine issues like response rates, nonresponse bias, and the impact of attrition in longitudinal studies.

Finally, we explore additional non-experimental quantitative methods, including naturalistic and structured observation. Each approach can yield meaningful data when used appropriately and aligned with a well-defined research question.

These data collection techniques and designs are essential tools for exploring real-world behavior, generating hypotheses, and informing future research.

* AI was used to help organize my thoughts and suggest clarifying sentences, but all ideas and final writing are entirely my own.