Social Work Research Methods. Reginald O. York
progress, the analysis of data, and conclusions. Further lessons on inferential statistics are included. The exercise for this chapter requires the student to examine data given to them from a study of the treatment of depression for a hypothetical mental health agency. An Internet website is employed. Chapter 9 provides students with concepts and skills of qualitative research. After a review of general concepts in qualitative research methods, students engage in a research practice exercise in content analysis. Program evaluation is the theme of Chapter 10. In this chapter, students learn about how agencies evaluate client need, service process, and client outcome in their comprehensive assessment of their programs. The exercise asks them to report on how a familiar agency addresses some aspect of the service system evaluation.
When students complete this section, they will be prepared to conduct basic research studies, including the statistical analysis of data. In other words, the mystery of research will have been substantially resolved and the students will be prepared to take action. But they will need additional lessons to take the step of competence at the intermediate level. That is the goal of the third part of this book.
Part 3: Conducting Each Phase of Social Work Research
In the third part of this book, the student examines each of the four major phases of social work research in more depth. Developing the knowledge base and the intervention is the theme of Chapter 11. The intervention part of this chapter, of course, relates to the evaluative type of study, which is the emphasis in this book. One of the practice exercises for this chapter asks students to undertake a preliminary review of the literature with regard to an evaluative study they would like to undertake. They report the procedures they employed in their search of the literature and summarize their findings in simple terms. Another exercise calls on the students to describe the intervention they would like to evaluate with regard to objectives, structure, model, and personnel.
Study methodology is the theme of Chapters 12 through 15. In Chapter 12, students review sampling as the tool for understanding study generalization. Different types of samples are described, and the student examines the distinction between scientific generalization (when you have a random sample) and logical generalization (when you do not have a random sample). Procedures for selecting a study sample from a study population are described, based on the type of sampling method employed. In the practice exercise, students describe how a random sample could be selected for a study they have designed. Various concepts such as sampling interval are included in this exercise.
Measurement is the theme of Chapter 13. Following the review of essential concepts in measurement (e.g., measurement error, reliability, validity), students examine how to select a published measurement scale or how to develop one themselves. One of the practice exercises in this chapter requires the student to find a tool for measuring alcoholism for a hypothetical set of clients and to explain what amount of gain on this scale would constitute practical significance.
In Chapter 14, students examine how to select a group research design with emphasis on the threats to internal validity that are addressed by various designs. The practice exercise calls on the study to discuss which threats are of special importance in a set of hypothetical studies in their decision of the research design that would be optimal with regard to both practicality and threats to internal validity.
Chapter 15 focuses on the single-subject research design. Various single-subject designs are discussed, including the limited AB design, a unique feature of this book. The practice exercise for this chapter is similar to the one for Chapter 14 except that it deals with the selection of a single-subject research design.
Chapters 16 and 17 focus on the analysis of data, both quantitative and qualitative. In Chapter 16, students review concepts related to the analysis of quantitative data and the drawing of conclusions from study results. Students review basic concepts in data analysis (e.g., correlation, effect size, levels of measurement) and are guided through a process of selecting a statistic for a given study. For example, they may find that the paired t test is appropriate if you have a set of matched pretest and posttest scores for a single group of clients. After they find the statistic, they are given instructions on how to use an Internet website to examine their data. They use these websites to examine hypothetical data in the practice exercise for this chapter.
Qualitative data analysis is the theme of Chapter 17. Both narrative analysis and content analysis are described along with examples of each. Coding in content analysis is a major emphasis of this chapter and is the focus of the practice exercise at the end of this chapter.
New Ideas in This Book
There are several ideas in this book that may be unfamiliar to those who have read other texts on social work research methods. These new ideas are motivated by my attempt to present content that is of practical value to the social worker. They address various tasks in social work research in a way that enhances comprehensiveness of application.
One of these new ideas deals with the theme of the generalization of study findings based on the method of sampling employed. Most texts suggest that you must have a random sample to generalize your findings. My new idea is that there are two bases for generalization: scientific and logical. You can generalize your findings on a scientific basis if you have a random sample. If you do not have a random sample, you can generalize your findings on a logical basis if you can show that your study sample and your study population are similar on variables important to the study. My rationale for using the word scientific for the first form of generalization is that you have a scientific basis for estimating sampling error when you employ a random sample.
Admittedly, scientific generalization is superior to the logical alternative. But I believe that we should engage in practical alternatives when we have a study that is less than perfect. In other words, we should not simply say that we cannot generalize our findings because we do not have a random sample. Instead, we should say we cannot scientifically generalize our findings but we may be able to generalize on a logical basis.
Another idea is that we should carefully determine what causes of client improvement (other than the intervention) should be of special concern in a given situation before we decide on the research design that is warranted. We should decide if a given alternative cause of client improvement (e.g., normal growth over time) is likely to occur in our specific evaluative study. If it is likely, we should make special efforts to use a research design that controls for that cause. In all situations, we should use the best research design that is feasible. However, if there are no alternative causes of client improvement that seem likely, we do not need to apologize for the fact that our chosen research design fails to control for them. We should report that our design does not control for these alternative causes but that we do not have reason to believe that these causes are likely to be important in our particular situation.
A third idea that you will not likely find in other social work research texts is the use of what I call the limited AB single-subject research design. This is a single-subject design where there is only one baseline recording of target behavior before the treatment begins, accompanied by several measurements during the treatment period. Most texts will describe the AB single-subject design, where you have repeated measurements