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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 during both the baseline and treatment periods, but these texts do not describe the limited AB design. This idea is promoted by practicality: Most social work students do not find it feasible to measure a target behavior for a single client several times before treatment begins. The social worker typically begins to offer service in the first or second encounters with the client. They may have the opportunity to measure client behavior at the end of an assessment session that takes place before they begin the service that is being evaluated, but they are not likely to be able to measure this behavior each week for several weeks before service begins. There is a statistical measure that can be used in this situation when the client’s behavior is measured as a score (the one-sample t test). Many of the students in my research courses have employed this evaluative research design, but few have employed the traditional AB single-subject design.

A fourth distinction of this book is the use of the Internet to analyze data in a simple application. There are several practice exercises where the student analyzes data using an Internet website. They are given step-by-step instructions that they will find to be rather simple. They will be able to compose a study hypothesis, enter their data, and report whether the data supported the study hypothesis. They can also report on the study conclusions that are supported by the data analysis. This simple application of data analysis has the effect of reducing the technical mystery of statistics often encountered by social work students.