== Recommended statistical texts == A :-) indicates in CBU library. Most of the others are in the university library. You can check at [[http://ul-newton.lib.cam.ac.uk/|here.]] Statistics books in the CBU library are listed [[http://imaging.mrc-cbu.cam.ac.uk/statswiki|here.]] The Psychological Postgrads website also has a list of suggested ''Statistics and Research Methods'' textbooks with comments located [[http://www.psypag.co.uk/resources/recommended-reading|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 [[attachment:Howell2010.pdf | 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) [[attachment:Newman.pdf | 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 [[FAQ/collier| 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) [[http://r4ds.had.co.nz/ | 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. [[https://www.manning.com/books/r-in-action-second-edition | 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 [[attachment:clusterch.pdf|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 [[https://onlinelibrary.wiley.com/doi/book/10.1002/9781118548387 | 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) [[http://www.appliedmissingdata.com/ | 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. :-) *[[attachment:meyers.pdf|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) [[attachment:brysbaert.pdf | 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 [[attachment:mixedspss.pdf|is here.]]) '''Statistica''' A list of books which detail the use of Statistica are listed at the [[http://www.statsoft.com/support/books-on-statistica/|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. :-)