Pages: 95

No chapter abstract available.

Pages: 23

Understanding research methods is about much more than memorizing research designs and strategies. Instead, learning research methods means learning how to have a conversation with experts, often via journal articles. In the beginning, you just listen. But as your skills improve, you can start to meaningfully participate. In this chapter, we explore the hidden philosophies, beliefs, and values underlying research methods. You’ll learn to better understand the assumptions that you and other researchers bring to research – a necessary step to truly communicating.

Pages: 26

The most important part of research is arguably the literature review, in which you learn about what scientific conversations have previously and are currently taking place. Only by learning about these conversations can you meaningfully participate in them. In this chapter, you’ll learn how to set meaningful and effective goals for conducting your own literature reviews, including exploring the relationship between theory and evidence, identifying research gaps, effectively using search technology, and understanding the structure of technical writing, all aimed at helping you eventually develop a research question of your own that matters.

Pages: 23

After you’ve absorbed the lessons, debates, and major concepts common to an existing research literature, you have a bigger problem to solve: how do I meaningfully join this conversation? In this chapter, you’ll learn what makes a good research question by exploring common general approaches to research, the types of research questions you can meaningfully pose, and how to draft your own.

Pages: 20

After you have identified a research question with meaning and purpose that is feasible to study, you must precisely define who you want to understand in relation to that question as well as how to find people from that group. In this chapter, you’ll learn about the lab versus field debate, populations and samples, and the many challenges both conceptual and statistical associated with sampling from a population you want to understand.

Pages: 49

No chapter abstract available.

Pages: 22

With a population of interest and a research question set, your next challenge is to define your general approach to addressing that research question. In qualitative research, this requires first understanding the underlying philosophical foundations and common methods of qualitative inquiry. In this chapter, you’ll learn when and why qualitative research is the best choice for your research by exploring different philosophical traditions, specific approaches, and related ethical considerations.

Pages: 25

With a general qualitative approach in mind, your next challenge is to meaningfully collect and analyze data. Qualitative research brings a lot of unique challenges due to the relatively unstructured way that your research participants will provide your data. In this chapter, you’ll explore specific techniques for doing this, such as interviews and focus groups. You’ll also explore common analytic strategies for qualitative data, especially those related to meaningful content coding, and methods for reporting your findings.

Pages: 150

No chapter abstract available.

Pages: 32

With a population of interest and a research question set, your next challenge is to define your general approach to addressing that research question. Quantitative research brings unique challenges related to minimizing bias while maximizing precision of measurement in relation to both variables of interest and the phenomena you want to study. In this chapter, you’ll learn the basics of quantifying psychological constructs and the underlying philosophies that support it. You’ll spend the most time exploring two specific theories that link people’s traits, states, and behaviors to numbers: classical test theory and modern test theory.

Pages: 29

With the basics of quantifying constructs, you next must learn to design or identify a situation in which meaningful causal conclusions can be drawn using those quantification techniques. In this chapter, you’ll learn how experimentation and quasi-experimentation can be used to accomplish this. You’ll also explore alternative approaches, such as observation, surveying, and related methods, which gain convenience and speed at the expense of confidence in causal inference. You’ll also explore related big-picture challenges with designing and analyzing quantitative datasets, including levels of analysis and advanced concepts of causality, like mediation and moderation.

Pages: 25

Once you have a basic quantitative research design selected, you need to figure out specifically what you’re going to measure about your participants. Asking a question that gives you the information you want is much more complicated than it initially appears. In this chapter, you’ll gain specific skills related to writing effect questions to measure precisely what you want to measure – nothing more and nothing less. You’ll explore specific types of questions to do this as well as ways to detect when your respondents aren’t putting in enough effort for their responses to help get that done. You’ll also explore how the aesthetics and presentation strategy of data collection instruments can influence how they are understood and responded to.

Pages: 26

Once you’ve learned the basics of how to ask meaningful questions, you might want to step it up a notch: measuring psychological constructs. Unlike individual questions with a clear factual basis, psychological constructs reflect unseen and un-directly-measurable human characteristics. To measure them, we need a new toolset. In this chapter, you’ll learn about the two major philosophical approaches to psychological measurement, formative and reflective measurement. You’ll learn a step-by-step process to create your own meaningful rating scales to assess any construct you might imagine. You’ll also learn how to diagnose problems with scales that you find in the wild.

Pages: 35

Once you’ve collected data, whether quantitative or qualitative, you need to figure out how to turn those data into conclusions. The first step in this is data cleaning, the process by which raw collected data turns into data that can be meaningfully analyzed. In this chapter, you’ll learn the broader context for data cleaning’s importance, including how improper data cleaning has damaged trust in the conclusions of our field. You’ll next learn how to clean data sets and document your cleaning in a way that leads to trustworthy conclusions.

Pages: 124

No chapter abstract available.

Pages: 26

After you’ve cleaned your data, you need to analyze it, and there is no more common method than null hypothesis significance testing. Unfortunately, there is also no analytic approach so commonly misunderstood, which has led to a lot of wasted research effort and misleading conclusions over the years. In this chapter, you’ll learn what hypothesis testing actually tells us – an estimate of how reasonable our assumptions are. You’ll also learn about several alternative approaches that are often better choices.

Pages: 32

A common method used in social science that is a bit unlike other methods is meta-analysis, which refers to the secondary analysis of other people’s research for the purpose of reaching meaningful conclusions that individual papers could not reasonably reach alone. In this chapter, you’ll learn how meta-analysis works, how to understand the meta-analyses conducted by others, and how to conduct your own. You’ll also learn why meta-analysis doesn’t solve the problem of poor-quality research and in what situations it can amplify misleading results.

Pages: 32

Once you have analyzed your study and have some interesting conclusions to share, you have a lot of options for specifically what you’re going to do with those conclusions. You’re probably going to at least present your results to an audience. As a practitioner, you might also be writing a white paper or technical report. As an academic researcher or practitioner, you might be targeting an academic journal. In this chapter, you’ll learn the fundamentals of all of these along with techniques to ensure that your efforts will be rewarded with acceptance, including pre-registration, use of open science storage repositories, and curation techniques for data and code. You’ll also learn the context of the publishing industry and how that influences the life of your manuscript after submission.

Pages: 30

At this point in this book, you have all the tools you need to plan, execute, and share the results of a successful research study. In this chapter, you’ll put it all together. You’ll first explore common logic errors that many researchers make along the decision-making path to good research. After that, you’ll walk through a checklist that gives you a chronological set of steps to follow for your research – to get it done and get it right.