Statistical Methods for the Social and Behavioural Sciences: A Model-Based Approach
Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data.
In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to:
- Understand and choose the right statistical model to fit your data
- Match substantive theory and statistical models
- Apply statistical procedures hands-on, with example data analyses
- Develop and use graphs to understand data and fit models to data
- Work with statistical modeling principles using any software package
- Learn by applying, with input and output files for R, SAS, SPSS, and Mplus.
Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.
- Publisher: Sage Publications Ltd
- Publish Date: Jan 30th, 2018
- Pages: 472
- Language: English
- Dimensions: 9.60in - 21.80in - 1.10in - 1.85lb
- EAN: 9781446269831
- Categories: • Statistics
--Dennis L Jackson
This book is so incredibly impressive. There are many general statistics texts in the field, but this one is fundamentally different by approaching the topic through a model-based perspective. It reaches a broad audience on many levels -- it's clearly technically rigorous, but makes wonderful use of bold in the text, call-out boxes, section recaps, and recommended readings. The topical coverage is also great -- this could be used either as a primary or secondary resource for a large variety of classes ranging from general introductions to multivariate topics to a graduate regression course.--Patrick J. Curran