Recommended statistical texts

A indicates in CBU library. Most of the others are in the university library. You can check at here. Statistics books in the CBU library are listed here. The Psychological Postgrads website also has a list of suggested Statistics and Research Methods textbooks with comments located here.

General

• Everitt, BS (1996) Making sense of statistics in psychology. OUP: Oxford.

• Gravetter, FJ and Wallnau, LB (2006) Statistics for the behavioral sciences. (7th Edition). Wadsworth:Pacific Grove, California. The swecond edition was recommended by a psych-postgrads list contributor especially for its chapter on a step-by-step introduction to structural equation modelling.

• Howell, DC (2002) Statistical methods for psychology. (5th edition). Wadsworth:Pacific Grove, CA. Examples and illustrations of a variety of analyses put in an entertaining and understanding manner. (Third and fourth edition also in library). There is also now a sixth edition of Howell (2010) with a significantly updated discussion of effect sizes and examples on how to write up the results of data analysis.

(The two below are recommended by psychology students on the PSYCH-POSTGRADS email list).

• Rowntree, D (1991) Statistics Without Tears:an introduction for non-mathematicians. Penguin:London.

• Salkind, NJ (2008) Statistics for people who think they hate statistics. (3rd Edition). Sage:London. (New edition due in November 2010)

SPSS learning books which cover a range of statistical procedures

• Boslaugh, S (2005) An Intermediate Guide to SPSS Programming: Using Syntax for Data Management. Sage:Thousand Oaks, CA. SPSS syntax (including macros) with some description of the statistical methods that they implement.

• Brace, N, Kemp, R & Snelgar, R (2006) SPSS for psychologists (3rd edition). Lawrence Erlbaum: London. A broad range of advanced statistical procedures as implemented in SPSS. (On order; First edition in library)

• Collier, J (2009) Using SPSS syntax - A Beginner's Guide. Sage: London. Further details are here.

• Field, A (2005, 2013) Discovering Statistics using SPSS. 2nd/4th Editions. Sage:London.

• Kinnear, PR and Gray, CD (2009) SPSS 16 made simple. Psychology Press: Hove, East Sussex, England. Revised (updated) versions available.

• Sweet, SA and Grace-Martin, K (2012) Data Analysis with SPSS: A First Course in Applied Statistics. Fourth Edition. Pearson:London. Features AN(C)OVA and Logistic regression analyses in SPSS.

R learning books which cover a range of statistical procedures

• Baguley, T (2012) Serious stats: A guide to advanced statistics for the behavioral sciences. Basingstoke: Palgrave. (covers basic analyses, effect sizes, messy data, AN(C)OVA and multilevel models). There is also some SPSS syntax given for comparison with R.

• Cornillon, P-A, Guyader, A, Husson, F, Jegou, N, Josse, J, Kloareg, M, Matzer-Lober, E and Rouviere L (2012) R for statistics. CRC Press:Abingdon. (Covers regressions, ANOVAs, PCA, clustering and graphics in R)

• Crawley, MJ (2005) Statistics: an introduction using R. Wiley:New York. (covers basic analyses such as descriptives and one and two-sample tests)

• Crawley, MJ (2007) The R book. Wiley:New York. (covers more advanced analyses such as general linear models including regressions and analysis of (co)variance).

Analysis of Variance

• Keppel, G (1991). Design and analysis: A researcher's handbook (3rd edition). Prentice-Hall: Englewood Cliffs, New Jersey. Clear substantive and quantitative introduction to analysis of variance

• Maxwell, SE and Delaney, HD (2004). Designing experiments and analyzing data: a model comparison perspective (2nd Edition). Lawrence Erlbaum: Mahwah, NJ. A good blend of a smattering of important formulae and practical usage.
• Miller Jr, RG (1998) Beyond ANOVA. CRC Press LLC: Boca Raton, Florida, USA (1998). There is also a Chapman and Hall 1997 edition. Involved treatment of ANOVAs and its formulation as a multiple regression.

• Winer, BJ, Brown, DR, & Michels, KM (1991). Statistical Principles in Experimental Design (3rd Edition). McGraw-Hill: New York. Excellent book. Good resource. The third edition by the last two authors was completed after Winer died. 1962 version in CBU library

Categorical Data Models

• Agresti, A (1996) An Introduction to Categorical Data Analysis. Wiley: New York. A primer covering a wide variety of methods.
• Agresti, A (2002) Categorical data analysis. Wiley: New York. More in-depth and comprehensive approach.

Factor Analysis (both exploratory and confirmatory)

• Byrne BM (2006) Structural Equation Modeling with EQS: Basic Concepts, Applications, and Programming. Lawrence Erlbaum:Mahwah, NJ. A practical introduction with plenty of examples to using EQS (available at the CBSU) for confirmatory factor analysis. Barbara has also written analogous texts for AMOS and MPLUS users.

• Harrington D (2009) Confirmatory factor analysis. Oxford University Press:New York. At 132 pages long this paperback is a short introduction to Confirmatory Factor Analysis intended for social researchers.

• Little TD (2013) Longitudinal Structural Equation Modeling (Methodology in the Social Sciences). The Guilford Press:New York.

• Loehlin, JC (1987, 2004) Latent variable models: An introduction to factor, path, and structural analysis. Lawrence Erlbaum: Hillsdale, NJ.
• Dunn, G, Everitt, B and Pickles, A (2003) Modelling covariances and latent variables using EQS. Chapman and Hall: London.

Cluster Analysis

Clustering can be used on small samples (N<100) usually grouping items assessing service attributes which have a limited range of responses such as Yes/No.

• Everitt, BS, Landau, S and Lees, M (2001) Cluster analysis. Fourth Edition Arnold:London. The first (1974) and second 1980) editions are in the CBSU library.

• Clustering is also covered in most multivariate data analysis textbooks including Chapter 8 of Tan, P-N, Steinbach, M. and Kumar, V. (2005) Introduction to Data Mining. Addison-Wesley:Upper Saddle River, NJ.

Logistic Regression

• Hosmer, DW and Lemeshow, S (1989) Applied logistic regression. Wiley: New York.

• Long, J Scott (1997) Regression Models for Categorical and Limited Dependent Variables. Sage:London. This book contains a good description of Multinomial Logistic Regression which analyses the effects of sets of predictors on three or more categories.

• Pampel, FC (2000) Logistic regression: a primer. Sage: London. A clear account, using mainly medical examples, of binary logistic regression.

Log-linear models

• Knoke, D and Burke PJ (1983) Log-linear models. Sage: London. A primer for an area whose best known example is logistic regression.

Multiple Regression

• Aiken, L and West, S (1991) Multiple Regression: Testing and Interpreting Interactions. Sage:London. This applied text addresses issues surrounding regression including multicollinearity and fitting interactions involving continuous covariates.

• Cohen, J and Cohen, P (1983) Applied multiple regression/correlation analysis for the behavioral sciences. Second edition. Lawrence Erlbaum: Hillsdale, NJ.

• Cohen, J, Cohen, P, West SG and Aiken LS (2002) Applied multiple regression/correlation analysis for the behavioral sciences. Routledge: London.

• Miles, J and Shevlin, M (2005) Applying regression and correlation: a guide for students and researchers. Sage:London.

Multivariate Analyses

• Field, A (2005) Discovering statistics using SPSS (2nd edition). Sage:London.

• Hair Jr., JF, Tatham, RL, Anderson, RE and Black W. (1998) Multivariate Data Analysis (5th edition). Prentice-Hall:Englewood Cliffs, NJ. This accessible and comprehensive text features plenty of illustrations and rules of thumb. It is also available, together with earlier editions, in the university library

There is also a sixth and seventh edition (2009) by Hair Jr, JF, Black,B, Babin, B, Anderson, RE, Tatham, RL published by Pearson International

• Tabachnick, BG and Fidell LS (2007) Using multivariate statistics (5th edition). Pearson International:Boston, MA.

• Meyers LS, Gamst G and Guarino AJ Applied multivariate research. Design and interpretation. Sage:London. The authors’ emphasis is on conceptual understanding of a comprehensive range of multivariate methods with illustrations of their use on data using SPSS.

• Warner RM (2012) Applied Statistics: From Bivariate Through Multivariate Techniques. Second Edition. Sage:Los Angeles which contains SPSS examples including a chapter on exploratory factor analysis.

Nonparametric Statistics

• Jureckova, J, Sen, PK and Picek J (2012) Methodology in robust and nonparametric statistics. CRC Press:Abingdon. (In addition to nonparametrics, Covers a closely related set of techniques to nonparametrics called robust statistics).

• Siegel, S. and Castellan, NJ (1988) Nonparametric Statistics for the Behavioural Sciences. McGraw-Hill, 2nd edition. A comprehensive text with illustrative examples of numerous nonparametric tests.

Power

• Aberson, CL (2010) Applied Power Analysis for the Behavioral Sciences. Routledge Academic. Contains SPSS syntax

  Cohen J (1992) A power primer. Psychological Bulletin 112 155-159. Cohen is a giant in power analysis having written books. This is actually one of his papers but is a pint size primer.

• Kraemer, HC and Thiemann, S (1987) How Many Subjects? Statistical Power Analysis in Research. Sage.

Random Effect modelling

• Brown, H and Prescott, R (2006) Applied mixed models in medicine (2nd edition). Wiley:New York. This illustrates a wide variety of applications using SAS.

• Luke, DA (2004) Multilevel modeling. Sage: London.

• SPSS Inc. document. Linear mixed effects modeling in SPSS. (Pdf file giving details of fitting random effect models in SPSS is here.)

Statistica

A list of books which detail the use of Statistica are listed at the here on the software makers (Statsoft's) own website.

Mathematics Primers

• Aitken, M., Broadhurst, B., Hladky, S. (2009) Mathematics for Biological Scientists. Garland Science:New York.

• Cann, A. J. (2002) Maths from Scratch for Biologists. Wiley:New York.

• Foster, P. C. (1998) Easy Mathematics for Biologists. Harwood academic:Amsterdam.

• Reed, M. B. (2011) Core Maths for the Biosciences. Oxford University Press.