I appreciated how Leedy and Ormrod (2019) used their first chapter to explain many topics that I had often taken for granted. Discussing the differences between deductive logic and inductive reasoning (pp. 20-22), the importance of the scientific method (pp. 22-23), and their focus on practical applications of research throughout struck me as a crucial reading for any research class, regardless of academic discipline. Leedy, Ormrod, and Johnson (2019) examined the early phase of research in a nice amount of detail. Chapter 2 was full of good advice for how researchers can immerse themselves in a topic and develop the literature review section of a research proposal. It also aided in providing further examples of what a research question is not and where those lost for a topic can turn for inspiration (p. 33-34). Their discussion about reviewing the relevant literature in chapter 3 was familiar, but I appreciate how they outlined and discussed the topic, explaining the topic and methodology in clear language.

Leedy, Omrod, and Johnson’s (2019) focus on different methodologies for data collection as well as the benefits and drawbacks of each method. They further developed the differences between quantitative and qualitative research, writing, “Quantitative researchers choose methods that allow them to objectively measure the variable(s) of interest. In order to minimize the chances of collecting biased data, they also try to remain relatively detached from the phenomena being investigated and from the participants in their studies. A qualitative study is often more holistic and emergent, with the specific focus, design, data-collection techniques (e.g., observations, interviews), and interpretations developing and possibly changing along the way” (89). In their term definitions, Leedy, Omrod, and Johnson covered unobtrusive measures (94). I appreciate some of the methods they used as examples from the Park Service as these work to address the observer’s paradox, in which the act of observation alters the conditions of the process being observed. I greatly appreciated the discussions of validity, from internal to external and especially the degrees of validity based on interpretations of survey respondents’ answers. It helps to show how even the most well-planned data collection instrument may not hold up to human interpretation. Something they said early in the chapter struck me as a good axiom for all research and data collection: “Data are volatile: They evaporate quickly” (84).

Leedy, Ormrod,, & Johnson’s (2019) comment about not losing sight of the forest for the trees is one that I’ve found particularly important in any large project (p. 117). It’s all too easy to get caught up in the minutia of any project and lose focus. That said, their admonition to keep a “task-oriented outlook” is similarly important, as focusing on placing one foot ahead of another can help when things become overwhelming (p. 122). In their thirteenth and final chapter, Leedy, Ormrod,, & Johnson describe preparing the deliverable of a research project. Having written a thesis and a dissertation, I know that this can be overwhelming as one seeks to balance the requirements of an academic department and graduate school with the specifics of a project. Their initial breakdown on describing methodology, presenting data, interpreting data, as well as identifying any weaknesses and larger connections work well for final projects of any size or discipline. I’ve found that well-written research articles generally follow this format, though they may label, group, or present the basic components in slightly different ways. In their discussion of references, Leedy, Ormrod,, & Johnson (2019) mention footnotes and endnotes (p. 380).

Leedy, Omrod, and Johnson’s description of the different types of qualitative analysis was beneficial. I was familiar with case studies and content analysis, but hadn’t heard of a grounded theory study prior to reading their work. Table 8.1, in which they categorized each type of analysis as well as their methods of data collection and analysis was a good break down of everything (Leedy, Ormrod, & Johnson 2019, pgs. 236-237). Similarly, their guidelines in figure 8.1 were useful in understanding how one can translate research questions into a format for interviewing potential subjects (Leedy, Ormrod, & Johnson 2019, pgs. 246-247). I was interested in how Leedy, Ormrod, and Johnson (2019) examined the mechanisms of quantitative research. For example, their discussion of different mechanisms for surveys, the relative value of rating scales, and the difficulty in reconciling one type of scale with another demonstrated just how difficult designing a survey is (pg. 157). Their discussion of constructing sampling surveys in very large populations was also insightful and I liked the way Figure 6.14 broke down each population level (pg. 179). I found all the combinations of experimental, quasi-experimental, and ex post-facto designs fascinating. Leedy, Ormrod, and Johnson did a great job explaining the topic from the simple to the complex as each methodology built upon the other, allowing for greater levels of experimental control and variation in the data results. Leedy, Ormrod, and Johnson’s (2019) introduction to mixed-methods research was perfect, “Some research problems practically scream for both quantitative and qualitative data” (pg. 259). Their discussion of how such an approach can offer a chance at completeness or hypothesis testing keyed into my own thoughts about research. While qualitative offers the opportunity for nuance and quantitative methods can generate data that researchers may apply to future studies, the ability to contextualize statistical data with qualitative information seems especially important as it helps to preclude readers from misinterpreting the data or reading it out of context (pgs. 260-261).

Leedy, Ormrod, & Johnson (2019) described several useful ways to process the data researchers collect, especially if one uses a blend of quantitative and qualitative data. Describing qualitative data, they write, “Qualitative data are inevitably messy data that can’t easily be wrapped up in one tight little package. …The goal of qualitative analysis is not necessarily to achieve a single truth, or one answer, but rather to uncover multiple meanings and experiences” (p. 350). Complexity is the point, after all. When evaluating human interactions, a wholistic picture may only develop if one captures every facet of the people, location, or interactions they’re studying. To this point, Leedy, Ormrod, & Johnson write, “Credible qualitative researchers don’t claim that they have approached a project with complete objectivity. …Instead, they carefully look inward, reflecting on and then describing possible beliefs, expectations, and cultural values that might have predisposed them to interpret data in particular ways” (p. 357). Finally, I greatly appreciated Leedy, Ormrod, & Johnson’s warning to back-up data (p. 355). It’s a habit I got into during various projects and now keep two portable hard drives and use my Google Drive for important materials.

Leedy, Ormrod, and Johnson (2019) stated of quantitative data, “In research questions regarding the physical world, the method for organizing data is apt to be fairly straightforward. …But in other disciplines – for instance, in the social sciences, humanities, and education – a researcher may need to give considerable thought to the issue of how best to organize the data” (p. 305). I also appreciated the refresher on statistics, especially their discussion of kurtosis and how skewed data might still be useful for research purposes. Like with this week’s lecture, it’s been a long time since I took a math class, so this was a much-needed section that was also easy to understand (p. 314-315). Returning to their earlier discussion of the difference between qualitative and quantitative data, Leedy, Ormrod, and Johnson (2019) wrote, “The nature of the data determines the technique that is most appropriate for calculating correlation” (p. 235). Finally, their caution about correlation not equaling causation (p. 327) recalled one of the lessons from the very beginning of the semester when we examined research fallacies. ( )