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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.

• Newman, A (2019) Research methods for cognitive neuroscience. Sage:London.

(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.

• Dugard P, Todman J and Staines H (2010) Approaching multivariate analysis. A practical introduction. Second Edition. Routledge:New York. This text has a range of quite complex example analyses using SPSS including Clustering, Poisson regression (including the use of the offset for variable time periods or count lists) and Multi-Dimensional Scaling.

• Field, A (2005, 2009, 2013) Discovering Statistics using SPSS. 2nd-4th Editions. Sage:London.

• Pallant, J (2010) SPSS Survival Manual. A step by step guide to data analysis using IBM SPSS. 4th Edition. Open University Press: Maidenhead. Covers linear and logistic regressions, ANOVAs and Exploratory Factor analysis. There is also a 5th edition (2013).

• 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.

• Beaumont, R (2014) Health Science Statistics using R and R Commander. Scion publishing:Banbury, Oxfordshire.
• Braun, WJ and Murdoch, DJ (2021) A First Course in Statistical Programming with R (3rd Edition). Cambridge University Press. Comprehensive text with hundreds of datasets, exercises and solutions with downloadable code. On order for CBU library (June 2021).

• 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)

• Field, A, Miles, J and Field, Z (2012) Discovering statistics using R. Sage:London

• Grolemund, G and Wickham, H (2017) R for Data Science O'Reilly Media:Gravenstein Highway North, Sebastopol, Canada. (Introduces R from scratch with examples from exploratory data analysis and graphics)

• James, G, Witten, D, Hastie, T and Tibshirani, R (2013) An Introduction to Statistical Learning with Applications in R. Springer: New York. (covers linear and logistic regressions and resampling methods)

• Kabacoff, RI (2015) R in Action, Second Edition Data analysis and graphics with R. Manning publications: Shelter island, New York. Details and pages of this book are here

• Meys, J and de Vries, A (2012) R for dummies. Wiley:Chichester.

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.

• Osborne, JW (2015) Best practices in logistic regression. Sage:Los Angeles. A primer introducing the concepts of logistic regression to the complete beginner.

Factor Analysis (both exploratory and confirmatory)

• Beaujean AA (2014) Latent Variable Modeling Using R: A Step-by-Step Guide. Routledge:New York.

• Blunch NJ (2016) Introduction to structural equation modeling using IBM SPSS STATISTICS and EQS. Sage:Los Angeles.

• Brown, T (2012) Confirmatory Factor Analysis for Applied Research. NY: Guildford Press. Suitable for beginners.

• 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.

• Dunn, G, Everitt, B and Pickles, A (2003) Modelling covariances and latent variables using EQS. Chapman and Hall: London.

• 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.

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.

• Kaufman, L and Rousseeuw, P (1990) Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.

• 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. The third edition (2013) is downloadable from here.

• 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.

• Retherford RD and Choe MK (1993) Statistical Models for Causal Analysis. Wiley:New York. Chapter 5 covers logit models and Chapter 6 on Multinomial Logit Regression explains the concepts in easily understandable terms with practical examples.

• Tarling R (2008) Statistical modeling for social researchers. Routledge:Abingdon, Oxon has an excellent accessible chapter on 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.

Missing data analysis

• Enders, C. K. (2010) Applied missing data analysis. Guilford Press: New York. Features macros for handling missing data.

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

• Dugard, P, Todman, J and Staines, H (2010) Approaching multivariate analysis. A practical introduction. Second Edition. Routledge:New York. This text has a range of quite complex example analyses using SPSS including Clustering, Poisson regression (including the use of the offset for variable time periods or count lists) and Multi-Dimensional Scaling.

• Field, A (2005,2009,2013) Discovering statistics using SPSS (2nd-4th Editions). Sage:London. (2009) and (2013)

• Hair Jr., JF, Anderson, RE, Tatham, RL and Black, WC (1998) Multivariate Data Analysis (5th edition). Prentice-Hall:Upper Saddle River, NJ. This accessible and comprehensive text features plenty of illustrations and rules of thumb. There are also sixth (2005) and seventh (2009) editions by Hair Jr, JF, Black, B, Babin, B, Anderson, RE, Tatham, RL published by Pearson International. All editions are also available in the university library.

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

• Meyers, LS, Gams,t 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.

Partial Least Squares Regression

• Hair Jr, JF, Hult, G, Tomas M, Ringle, CM & Sarstedt, M (2014) A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage Publications.

Power

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

  Brysbaert, M (2019) How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables. Journal of Cognition 2(1) 16 1-38. Not a book but 38 pages presenting concepts of power analysis with some examples using the G*Power software. We usually need more people than are currently being used to have adequate power.

  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.

• Heck, RH, Thomas, SL and Tabata, LN (2010) Multilevel and longitudinal modeling with IBM SPSS. Routledge:New York.

• Heck, RH, Thomas, SL and Tabata, LN (2013) Multilevel modeling of categorical outcomes using IBM SPSS. Routledge:New York.

• 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.

Time Series

• Warner RM (1999) Spectral analysis of time-series data. The Guilford Press:New York. Written for psychologists with a guide to harmonic (cosinor) and spectral analyses with what to check when fitting models and how to report them. Refers to procedures in SPSS.

MATLAB

• Rosenbaum, D.A., Vaughan, J. and Wyble, B. (2014) MATLAB for Behavioral Scientists. Second Edition. Taylor and Francis: Hove, East Sussex.

• Wallisch P., Lusignan, M. E., Benayoun, M. D., Baker, T. I., Dickey, A. S., Hatsopoulos, N.G. (2009) MATLAB for Neuroscientists:an introduction to scientific computing in MATLAB’ Elsevier:Burlington, MA.

Erman Misirlisoy (author) submitted a book proposal to Routledge in June 2015 on 'Programming behavioural experiments: learn it the quick way with MATLAB' which was a slimmed down primer for MATLAB in a more informal tone than existing texts aimed at people who are not from a programming background particularly in cognitive neuroscience which is the background of the author.

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.

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