Making Sense of Statistical Methods in Social Research is a critical introduction to the use of statistical methods in social research. It provides a unique approach to statistics that concentrates on helping social researchers think about the conceptual basis for the statistical methods they're using.

Whereas other statistical methods books instruct students in how to get through the statistics-based elements of their chosen course with as little mathematical knowledge as possible, this book aims to improve students' statistical literacy, with the ultimate goal of turning them into competent researchers.

Making Sense of Statistical Methods in Social Research contains careful discussion of the conceptual foundation of statistical methods, specifying what questions they can, or cannot, answer. The logic of each statistical method or procedure is explained, drawing on the historical development of the method, existing publications that apply the method, and methodological discussions. Statistical techniques and procedures are presented not for the purpose of showing how to produce statistics with certain software packages, but as a way of illuminating the underlying logic behind the symbols.

The limited statistical knowledge that students gain from straight forward ‘how-to’ books makes it very hard for students to move beyond introductory statistics courses to postgraduate study and research. This book should help to bridge this gap.

Methods for Analysing Latent Variables

Methods for analysing latent variables

Latent Variables in Social Research

After introducing multiple regression models in Chapter 7, we have expanded our discussions in two different directions. First, we added temporality to our analysis in Chapter 8, studying two statistical models that deal with time. Then we enriched our study of cases in Chapter 9 by learning several statistical tools for discovering grouping patterns among the cases. In this chapter we make a third expansion on variables.

So far, when we use variables, we take them as completely true measurements of what they are supposed ...

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