Abstract
In Part (Chapter) 1 of The 7 Steps of Data Analysis: A Manual for Conducting a Quantitative Research Study, author, William Bannon describes, in the first semester of his master of social work (MSW) program noticing that, unlike him, most students enrolled in the required social work research and statistics course were not getting “a great deal of fulfillment” from studying the topic. It was from this experience as a MSW student, and the experience of several subsequent years working as a statistician, that Bannon began to try to find a way to make “data analysis more understandable, meaningful, and enjoyable for advanced degree students in clinically-based programs,” which is the goal and purpose of the book being reviewed. Part 1 of the book goes on to describe and explain, in four sections, using clear explanations and user-friendly tone, what is data analysis, the components of data analysis (i.e., the “cake recipe, ingredients, and cooking utensils” used in the 7 Steps of Data Analysis), “why statistics are awesome,” and “applying the materials.”
Continuing with the same clear explanations and user-friendly tone and use of graphics demonstrated in Part 1, Part 2, entitled, “A Model for Conducting Statistical Research” explains both, how the seven steps of data analysis can serve as an effective model for conducting clinical/statistical research in social work and other clinical fields and why having a model for conducting statistical research is essential for clinical practitioners at this time. Bannon’s description and explanation of the shared, rudimentary elements of all successful models of data analysis (planning, properly preparing data for analysis, assuring the data are appropriate for answering the research question, analyzing the data, and properly reporting the results of the analysis), which also represent the seven steps of data analysis, provides a “road map” through the data analysis process that students can use to locate where they are in the process, what, if any, steps they may have missed along the way, and/or what is the next step in completing the data analysis process in which they are engaged.
Part 3, “The Data Analysis Concepts You Need to Know,” defines and explains what students needs to know to select and prepare for the statistical analysis process that will answer their research question. It includes easy to understand explanations of level of measurement; parametric and nonparametric statistics; descriptive and inferential statistics; single and composite item scores; variable types; the need for and how to create a clear and succinct hypothesis; the three dimensions of relationships between variables, effect size, confidence intervals; and concludes with a memorable and cautionary tale of why it is critically important to save your work throughout the quantitative analysis process, called, “Haste or Paste: Save the Syntax!”
Part 4 and Part 5 of the 7 Steps manual provide systematic explanations of the data analysis process, including screenshot/pictorial views of SPSS Instructions and Output (Version 21.0.0, IBM Corp, 2012). Part 4, “A Quantitative Study with a Continuous Dependent Variable,” provides explanations and “how-to’s” for a study map, SPSS power analysis, assumption tests, managing missing data, assessing the reliability and validity of measurement tools, univariate analysis, Bivariate analysis, t-tests, analysis of variance, multivariate analysis, multicollinearity, and dummy coding as well as instructions for and the write-up and report and a sample manuscript for the aforementioned types of statistical analysis. Part 5, “A Quantitative Study with a Categorical Dependent Variables,” using the same detailed and step-by-step approach with instructions for SPSS as is used in Part 4, explains and describes creating a study map, checks for data integrity, power analysis, assumptions tests, independent samples t-test, χ2, dummy coding, and conducting binary logistic regression. Part 5 also ends with a sample manuscript and instructions for writing and reporting the results of the analysis.
Part 6, Assessing Published Quantitative Research Studies, using the same seven-step process, which includes assessing (1) the study map, (2) data entry, (3) checks of data integrity, (4) univariate analysis, (5) bivariate analysis, (6) multivariate analysis, and (7) write-up and report of results, describes how to apply a strength-based perspective to effectively assess the quality and credibility of a published or proposed for publication quantitative study.
As an experienced instructor of MSW Social Work Foundation and Concentration-year Research Methods, Statistics, and Thesis-Writing Courses and a Research Methods and Data Analysis Supervising Member of Dissertation Committees, I have found The 7 Steps of Data Analysis: A Manual for Conducting a Quantitative Research Study to be a clear, concise, and thus a very readable guide to planning, implementing, and assessing quantitative studies of social work practice, that is very reasonable priced compared to similar books. That additional “Stats Whisperer” support is provided for social work practitioners, doctoral students, and agencies engaged in creating evidence on social work practice, in the forms of 6-week webinars, individual consultation, and quarterly newsletters, demonstrates Bannon’s facility with social work practice and statistical analysis knowledge and skills as well as his compassion for and commitment to students and practitioners who find the subject of data analysis intimidating and confusing. The addition of a section on the dependent t-test would improve the text. Nonetheless, based on my experience with its use, Bannon successfully achieves his goal of finding a way to make data analysis more understandable, meaningful, and enjoyable for advanced degree students in clinically-based programs with The 7 Steps of Data Analysis: A Manual for Conducting a Quantitative Research Study.