Single parents face a triple bind of inadequate resources, employment, and policies, which in combination further complicate their lives.

This book – multi-disciplinary and comparative in design – shows evidence from over 40 countries, along with detailed case studies of Sweden, Iceland, Scotland, and the UK. It covers aspects of well-being that include poverty, good quality jobs, the middle class, wealth, health, children’s development and performance in school, and reflects on social justice.

Leading international scholars challenge our current understanding of what works and draw policy lessons on how to improve the well-being of single parents and their children.

Don’t buy our book!

Well, you can. There is a beautiful hardback version available. But you don’t have to. The open access .PDF of the book is free to download, thanks to generous support of Knowledge Unlatched.

Free download: http://oapen.org/search?identifier=643492
Policy Press website: http://policypress.co.uk/the-triple-bind-of-single-parent-families

Less than a year ago, I wrote these exact words. I then discussed the publication of a Dutch book on statistics, to which I provided minor assistance. Now, I repeat these words to introduce the Enligsh translation of this conceptual introduction to statistics, called Statistical Tools. Again, I contributed to this publication, this time by providing a first, rough, translation from Dutch to English. Let me repeat below what I wrote before on this blog, for of course this still holds relevance for the translation to English:

With the focus on practical application rather than statistical theory, the first chapter starts explaining the goal of inferential statistics, meanwhile introducing the concepts of measurement and variables. Considerable attention is paid to the importance of high quality data to perform your analyses on. The second chapter deals with descriptive statistics, both in a numerical and a graphical way. Here, also the concepts of a distribution and of correlation are introduced. The third and final chapter discusses the testing of hypotheses, using techniques as the cross-table, tests for means and proportions, various forms of correlation, and finally multiple regression.

Clearly, the setup of this book is what one might expect from an introduction to statistics. However, I think this book has a unique approach by its strong focus on the conceptual level, rather than the (mathematical) statistical theory. Nevertheless, it does not shy away from relatively complex subjects such as the multiple regression. Even on the conceptual level, it pays a lot of attention to the assumptions required for the various analyses discussed. The practical approach of this book is enhanced even further, because all examples come from ‘real life’ research. On the accompanying website SPSS data files and syntax files are made available, so that every example from the book can be repeated by the reader.

Aimed at the novice statistics student, this book offers a comprehensible and conceptual approach at statistics. It will surely help students of statistics to grasp what they’re actually doing when pushing SPSS’s buttons or trying to interpret published figures. In that sense, I think that for many statistics student, this book successfully reaches is goal of transforming statistics form an abstract undertaking to an actually useful and applicable tool.

– – — — —– ——–
This post is part of my ‘Reading List’. In this series I jot down some thoughts about the books I read and enjoyed. Some posts my give a somewhat balanced overview of a book, others will just focus on some aspects that, for whatever reason, caught my attention. Never are these posts meant as an evaluation or even review of the book. I just like to share some impressions.

An overview of my Reading List is available, which contains both a list of the books that I wrote about, and another list of books I’m planning to read.

I have had the pleasure of providing minor assistance to this book, so please let me introduce it here. Admittedly I’m not impartial on this one, but I sincerely believe this book to be an asset to introductory statistics courses and those who have to interpret or perform statistics, without the need of knowing all about the mathematical niceties.

With the focus on practical application rather than statistical theory, the first chapter starts explaining the goal of inferential statistics, meanwhile introducing the concepts of measurement and variables. Considerable attention is paid to the importance of high quality data to perform your analyses on. The second chapter deals with descriptive statistics, both in a numerical and a graphical way. Here, also the concepts of a distribution and of correlation are introduced. The third and final chapter discusses the testing of hypotheses, using techniques as the cross-table, tests for means and proportions, various forms of correlation, and finally multiple regression.

Clearly, the setup of this book is what one might expect from an introduction to statistics. However, I think this book has a unique approach by its strong focus on the conceptual level, rather than the (mathematical) statistical theory. Nevertheless, it does not shy away from relatively complex subjects such as the multiple regression. Even on the conceptual level, it pays a lot of attention to the assumptions required for the various analyses discussed. The practical approach of this book is enhanced even further, because all examples come from ‘real life’ research. On the accompanying website SPSS data files and syntax files are made available, so that every example from the book can be repeated by the reader.

Aimed at the novice statistics student, this book offers a comprehensible and conceptual approach at statistics. It will surely help students of statistics to grasp what they’re actually doing when pushing SPSS’s buttons or trying to interpret published figures. In that sense, I think that for many statistics student, this book successfully reaches is goal of transforming statistics form an abstract undertaking to an actually useful and applicable tool.

Despite the reference to S and S-PLUS in the title of this book, it offers an excellent guide for the nlme-package in R-Project. Reason for this is the close resemblance between R and S. The nlme-package, available in R-Project for estimation of both linear and non-linear multilevel models, is written and maintained by the authors of this book.

The book is not an introduction to R. Basic knowledge of R-Project (or S / S-PLUS) is required to get the most out of it, as well as some knowledge on multilevel theory. Although the book forms a thorough introduction to multilevel modeling, addressing both some theory, the mathematics and of course the estimation and specification in R-Project (or S / S-PLUS), the learning curve it offers is quite steep. The authors are not shunned to apply matrix algebra and specify exactly the used estimation procedures.

Not only the specification of basic models is described, but many other subjects are brought up. A specific grouped-data object is considered, as well as ways to visualize hierarchical data and multilevel models. Heteroscedasticity, often a violation of assumptions, can be caught in the models easily, as is described clearly in one of the chapters. Finally, not only linear models are tackled, but non-linear models as well.

All in all, this book is an excellent addition for those who have prior knowledge of both R-Project and multilevel analysis. Using real-data examples and by providing tons of output, the authors accomplish to make clear the necessity of the more complex models and thereby invite the reader to invest time for the more fundamental aspects of multilevel analysis.

– – — — —– ——–

• Discuss this article and pose additional questions in the R-Sessions Forum
• Find the original article embedded in the manual.

– – — — —– ——–
R-Sessions is a collection of manual chapters for R-Project, which are maintained on Curving Normality. All posts are linked to the chapters from the R-Project manual on this site. The manual is free to use, for it is paid by the advertisements, but please refer to it in your work inspired by it. Feedback and topic requests are highly appreciated.
——– —– — — – –

For those who have some theoretical knowledge on statistics and regression techniques, and who want to learn to use R-Project to analyze some data, John Fox wrote just the book.

The introductory chapter shows the most basic aspects of R-Project. Halfway this chapter the reader finds himself analyzing real data using regression techniques. The following chapters introduce the reader to other aspects of the analytical process: reading data into your statistical program, exploring the data and performing some bivariate tests. Then, three full chapters are devoted to regression techniques. While working on practical examples, the reader is introduced to more fundamental aspects of the R-Project software where needed.

The beauty of R is, that anybody can make alterations and additions to it. John Fox did so on many functions, as well as added some functionality that resemble a SPSS-like usage. For instance, recoding in R-Project is normally done using conditionals. This can lead to a somewhat cumbersome process, so Fox wrote his own ‘recode’ function, in which values, or ranges of values, can be specified, as well as output-values. For many people this, and many other functions, will enhance their usage of R-Project. The drawback of this is that the fundamentals of R usage are lost out of sight. But then, the book is called ‘companion to applied regression’.

A broad array of analytical techniques is addressed, with a focus on regression. Both linear models as well as generalized models are described. A full chapter is reserved for diagnostics of the model fit. Finally, a chapter is devoted to graphically presenting results and the last chapter gives an introduction to programming in R. Although the book ends there, many other statistical techniques are covered by Fox in web-appendices that are freely available. Among the covered techniques in these appendices are structural equation modeling, multilevel modeling, and several specific types of regression, such as: non-parametric, robust, time-series and nonlinear regression.

This pleasantly written book is excellent for those who want to use R-Project as their main statistical software. Some knowledge on statistics is requires, while all the basics of R-Project are described. For the more advanced techniques, web-appendices were made available. Summarizing, this book is both a very good introduction, as well a reference. As the title says, it is a fine companion.

– – — — —– ——–

• Discuss this article and pose additional questions in the R-Sessions Forum
• Find the original article embedded in the manual.

– – — — —– ——–
R-Sessions is a collection of manual chapters for R-Project, which are maintained on Curving Normality. All posts are linked to the chapters from the R-Project manual on this site. The manual is free to use, for it is paid by the advertisements, but please refer to it in your work inspired by it. Feedback and topic requests are highly appreciated.
——– —– — — – –

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman is known for his expertise on Bayesian statistics. Based on that knowledge he wrote a book in multilevel regression using R and WINbugs. This book aims to be a thorough description of (multilevel) regression techniques, implementation of these techniques in R and bugs, and a guide on interpreting the results of your analyses. Shortly put, the books excels on all three subjects.


Admittedly, this review has been written based on first impressions on the book. But, a sunny day in the park reading this book (literally) left me to believe that I have some understanding on what this book is trying to achieve. I bought this book in order to have an overview on fitting multilevel regression models using R. Starting to read the book, I soon found out that that is indeed what it has to offer me, but it offers me a lot more. After some introductory chapters, the book starts off with an introduction to both linear regression as well as introducing the reader to R software, by showing how to fit linear regression models in R. This is readily expanded to logistic regression and generalized regression models. All is illustrated lushly with many examples and illustrations.

Before these ‘basic’ regression models are extended to multilevel models, Bayesian statistics are introduced. Based on simulation techniques, causal inferences, based on regression models, are made. The multilevel section of the book is set up similarly. First, ‘basic’ multilevel regression models are introduced. Throughout the book, the lmer function is used. This function is not only able to fit simple multilevel models, but logistic and generalized models as well. It can even estimate non-nested models. All in all, this forms a thorough introduction to multilevel regression analysis in itself, but the book continues here as well to introduce the reader to Bayesian statistics.

All above-mentioned models, as well as more complicated models, are fitted using WINbugs as well. This very flexible method allows the reader to estimate a greater variety of (multilevel) models. Causal inference on multilevel models, using Bayesian statistics, is described as well. The third main part of the book elaborates on the skills the reader uses to ‘just’ fitting models. It learns the reader to really think about what it going on. Topics such as ‘understanding and summarizing the fitted models’, ‘sample size and power calculations’, and most of all ‘model checking and comparison’ each receive their own chapter of the book. In this we can see that the authors of this book aimed higher than just writing instructions on how to let R fit (multilevel) regression models. The aim of this book, is to teach the reader how to analyze data the proper way. Much attention is paid to assumptions, testing theory, and interpretation of what you’re doing. To quote the authors: “If you show something, be prepared to explain it”.

This philosophy seemed to be a guideline for the authors while writing this book, as well as flexibility. The book starts off with some examples of the authors’ own research. These examples return throughout the book, resulting in some degree of familiarity with the data by the reader. Due to this, the concepts, models and/or analyses described are certainly more easy to be understood. As a reader, you start to think along with the author, when a new problem is described. The relative worth of the techniques, as well as their drawbacks, are made perfectly clear. The use of R software, as well as WINbugs, pays of well in the sense that it requires some more effort to master these programs, but in that process the reader learns to think deeply about what he really want to do and how it is done properly.

I found it not an easy book, but thanks to the many examples throughout the book it can be fully understood by people with some prior knowledge in regression techniques. All of the examples in the book can be tried yourself, since the data and syntax are available on the author’s website on the book. This helps the reader to get some feel for the more difficult subjects of the book. All in all, this seems to me as a great book for every applied researcher that has basic prior understanding of regression analysis. Due to its focus on one set of techniques, a great depth of understanding can be derived from this book.

Introductory Statistics with R

Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen in Denmark, and a member of the R-Project Core Development team. Also, he is an active participating and respected member of the R-help mailing-list. Based on these experiences, he set to write an introductory book on statistics and R.

The book start with relatively simple topics, easily working toward more complex statistical problems. Central techniques that are covered are analysis of variance and regression. Starting with bivariate analyses, multivariate analyses of both types are discussed to a high extent. Several types of linear (regression) models are introduced, covering polynomial regression, regression without an intercept, interactional model, two-way ANOVA with replication, and ANCOVA. A separate chapter focusses on logistic regression. Moreover, in many ways the equivalence or parallels of regression and ANOVA are discussed. Thereby, a greater understanding of the (differences between) techniques is stimulated.

In the course of the book, the reader is introduced to R by simple examples. The book has a R-package available for download that contains several data-sets that are used throughout the book. An appendix in the book describes the data-sets. In contrast with many other books on R, the package does not contain functions that were written by the author. The benefit of that is, is that the books only relies on the most basic functions that come with a basic installation of R. When the book was written, the most recent version of R was 1.5.0. Presently, it is 2.4.1, but all the examples still work without any problem.

The power of the approach in this book, is that both the statistical techniques are introduced, as well as it is shown how to perform these tests using R. But, while the book aims to be an introduction to statistics, it somewhat fails to introduce the techniques thoroughly. Strikingly, the author does not explain (much) on the interpretation of results. For instance, no guidance is given in the interpretation of the results of a logistics regression. This cannot be expected to be clear for a novice in statistics. If a reader does not have any understanding of statistics prior to reading this book, the book will fail in its’ purpose. To put it differently: the book hovers somewhere in between “Introductory Statistics with R” and “Introduction to R, using Statistics”.

Any author makes choices when writing, based on his or her background. Differences occur in the subjects that are addressed. In this, Peter Dalgaard has made some nice choices, that make to book pleasantly distinguishing from other introductory books. The cross-over between regression and ANOVA on the applied level has already been mentioned. Other niceties are a chapter dedicated to determining the statistical power of tests. To students starting to learn about statistics, this might be a starting point for discussing the relevancy of statistical significance. Another distinguishing chapter is a chapter on survival analysis. Not often found in starting books on statistics, this chapter shows that statistics in different contexts are based still on the same principles.

Nevertheless, the book seems ideal for teaching purposes in combination with a book that handles more fundamental issues of statistics. If used in that way, a powerful combination is at hand. The lack of guidance in interpretation then is not so much of a problem. A difficulty many students in statistics have, is transferring their new knowledge on a fundamental level to application. This book will be of help, mostly because it has an emphasis on application, but explains some of the more important fundamental issues, from a practical perspective. Thereby, when used with prior knowledge or an additional book on statistics, it is a wonderful addition to applied statistics and R-Project.

– – — — —– ——–

• Discuss this article and pose additional questions in the R-Sessions Forum
• Find the original article embedded in the manual.

– – — — —– ——–
R-Sessions is a collection of manual chapters for R-Project, which are maintained on Curving Normality. All posts are linked to the chapters from the R-Project manual on this site. The manual is free to use, for it is paid by the advertisements, but please refer to it in your work inspired by it. Feedback and topic requests are highly appreciated.
——– —– — — – –