Do you ever feel at a loss on how to proceed with a particular set of data? Your Statistical Consultant can provide the answer. This comprehensive guide introduces, describes, and makes recommendations regarding difficult statistical problems and techniques. The authors discuss common problems by addressing frequently asked questions; provide a conceptual overview of topics and techniques; give accounts of such new-found topics as the debate over statistica… More >>
Your Statistical Consultant: Answers to Your Data Analysis Questions
The book is handy and good reference for students of statistics and related disciplines. The downside perhaps, is lack of worked examples on most of the topics covered.
Rating: 4 / 5
I bought this book because it was required for a course, but I plan to keep it. Easy to understand, well-written and clear explanations of various statistical measures and how to apply them, with good examples throughout.
Rating: 4 / 5
Nice book for most engineers that need direction on which stat to use. I have it on my work desk and great when needed a few times a month.
Rating: 5 / 5
This book is not useful at all to a beginning level student. You would have to have another book in addition to this one in order to be able to select the appropriate operations and complete them. In addition, the completed results shown are not presented in a way that is comprehensible to a beginning level student. All in all, this book is only useful for someone who already knows what to do and how to do it.
Rating: 2 / 5
I wish I had come across this book sooner! I have a modest statistics background, but unlike most other statistics books, this book really opened my eyes to the reasoning behind various common statistical tests.
If you haven’t taken an introductory statistics course before, then this book is probably not for you, but if you have, chances are that you’ll find this book really helpful when doing data analysis. I plan to refer to it often.
The spirit of the book is described on page 54: “The biggest challenge is to be able to recognize which types of analysis are most appropriate for a given situation, and how to interpret and apply the results.” [Kachigan, 1986]
My comments:
- The book is well-written, with lots of examples that are pretty easy to follow.
- The authors promote the use of exploratory data analysis to reveal the meaning of data between pairs of variables (e.g., plot bivariate cases, examine shapes of distributions, do cross-tabulations and scatterplots).
- They discuss, in detail, data preparation (e.g., data coding for survey questions), descriptive vs. inference statistics, the relationship between statistical significance and substantial significance, alpha vs. beta, power, effect size, etc. For example, how large should my sample size be if I want to detect significance at a given alpha level?
- They make the case for confidence intervals being more useful than a test of significance, especially smaller confidence intervals.
- They evaluate the assumptions behind data that approximates a normal distribution, and give suggestions for how to handle non-normal data (e.g., via power transformations).
- They explain how to deal with outliers and missing values.
- They discuss the assumptions behind various statistical tests, and explain when some test isn’t going to be useful.
- They provide trade-offs in the consideration of sample size: the costs associated with collecting data, including the researcher’s own time, and the requirements of statistical testing (e.g., power analysis comparing the number of independent variables, alpha level, and desired power level with the sample size).
- In response to the question, “How do I select the appropriate statistical test?”, they begin by asking you to carefully consider the question(s) that you’re trying to address with your data. They provide checklists or flowcharts for selecting the appropriate statistical test (e.g., p. 140+) depending on the kind of dependent variable (continuous, discrete) and whether the independent variable is discrete or continuous, how many independent variables there are, whether you want to control for other variables (covariates), etc. The flowcharts provide several design frameworks for you to choose. For example, does your research question concern the degree or strength of a relationship between the dependent and independent variable(s)? Are there significant group differences that you want to investigate? Etc. You’re directed to the appropriate test (e.g., bivariate correlation, multiple regression, one-way ANOVA or t test, contingency table analysis, cross-tabulation, loglinear models, …)
- They discuss, in substantial detail, the two important families of statistical approaches: ANOVA (analysis of variance), and multiple regression.
- They show the “bigger picture” of statistical tests, namely, how the various tests relate to one another based on the number of independent and dependent variables and their level of measurement (discrete, continuous). For example, from more general to more specific: canonical correlation -> MANOVA -> factorial ANOVA -> one-way ANOVA -> t test -> cross-tabulation or 2×2 tables; canonical correlation -> discriminant function or logistic regression; canonical correlation -> multiple regression and correlation -> bivariate regression -> bivariate correlation -> point biserial correlation -> cross-tabulation or 2×2 tables; point biserial correlation < -> t test).
- Towards the end of the book, they spend a bunch of time on meta-analysis. Meta-analysis allows us to summarize, aggregate, and compare results across multiple studies, and includes power and effect size. The last part of the book is somewhat deeper than earlier on, but it will be a very useful reference should I get to that level of research.
Overall, this is a wonderful statistics book … a keeper for many years to come!
Rating: 5 / 5