Quantitative Decision-Analytic Modeling & Statistical Procedures for Univariate, Bivariate & Multivariate Data Analysis

Produced by Daniel J. Denis, Ph.D., University of Montana



The Data & Decision Lab is headed by Daniel J. Denis, Ph.D. Dr. Denis is Associate Professor of Quantitative & Statistical Psychology at the University of Montana, where he teaches advanced courses in decision-based statistical modeling in the Department of Psychology. Dr. Denis specializes in the teaching of statistical and mathematical concepts through a combination of analytical, practical, and historical analyses. For more information, please see his academic and professional experience, or download his curriculum vitae.  E-mail: daniel.denis@umontana.edu


Currently Accepting New Students - Quantitative, Scientific Training and Consulting

Are you interested in graduate studies in Psychology specializing in quantitative and statistical training? Would you like to learn how to apply your skills through consulting mentorship in a variety of professional domains, both academic and in the private sector? Would you like to gain real hands-on experience in quantitative consulting while studying for a Ph.D? The Data & Decision Lab specializes in consulting with clients to provide them with the very best in data-analytic and graphical visualization needs. Data analysis and graphical visualization are areas of specialization applicable to virtually every scientific field, and are currently in demand across virtually every industry. Skills such as these are very marketable, and opportunities to apply them both in academia and in private settings abound. If this type of work and learning interests you, contact us for more information at daniel.denis@umontana.edu and/or consider applying directly to the Department of Psychology's General Experimental Program in Quantitative Psychology.






Applied Statistics Textbook, John Wiley & Sons, Inc. - I am currently under contract with John Wiley & Sons, Inc. for the writing of an applied statistics text for the social sciences. The project is scheduled for publication in 2015. The tentative title of the book is "Applied Univariate, Bivariate and Multivariate Statistics for the Social Sciences."


Autumn 2014 - Courses

Psyc 522 - Multivariate Statistics - Fall 2014 (GRAD)

Syllabus


Posted Thursday, September 4, 2014

Matrix Algebra Using R (appendix notes)

Rencher Chapter 2 Sketchy

Rencher Chapter 2 - Exercises

Least Squares Matrices


Rencher Chapter 3 Sketchy

Chap 3 SPSS (exploratory)

Chap 3 R (exploratory)

Meyers (key terms)



Posted Thursday, September 11, 2014

Rencher Chapter 4 Sketchy

Rencher Chapter 5 Sketchy

Hotelling's T-squared in SPSS

Hotelling's T-squared - Preview to MANOVA

Operationism (Green)


Posted Thursday, September 25, 2014

Rencher Chapter 6 Sketchy

MANOVA III (pdf)

MANOVA DATA TABLE

Simple Effects in SPSS MANOVA

Assessing Multivariate Normality (optional)


Posted Thursday, October 2, 2014

Example of MANOVA (Univariate vs. Multivariate Test)

Example of MANOVA and Discriminant Analysis

Rencher Chapter 8 Sketchy

Discrim I (pdf)

Discrim II (pdf)



Posted Thursday, October 9, 2014

Rencher Chapter 9 Sketchy


3-group discrim

1   2   3   4   5

Testing Interactions in Multiple Regression I (pdf)

Testing Interactions in Multiple Regression II (pdf)

Moderated Regression (html)


Posted Thursday, October 16, 2014

Rencher Chap. 11 Sketchy

Canonical Correlation SPSS (UCLA)


Cancor SAS (UCLA)



Psyc 520 - Advanced Psychological Statistics I - Fall 2014 (GRAD)

Syllabus
         

Sketchy Notes

Appendix E
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5 - Sampling Distributions (pdf)
Chapter 5 - Sketchy
Chapter 6 - Sketchy
Chapter 7 - Sketchy
Chapter 8 - Sketchy


Optional Readings

Big Data & Decisions


Problems & Solutions


Chapter 1 - Problems & Solutions pdf
Chapter 2 - Problems & Solutions pdf
Chapter 3 - Problems & Solutions pdf
Chapter 4 - Problems & Solutions pdf

Chapter 5 - Problems & Solutions pdf
Chapter 6 - Problems & Solutions pdf
Chapter 7 - Problems & Solutions pdf
Chapter 8 - Problems & Solutions pdf

Chapter 10 - Problems & Solutions pdf
Chapter 11 - Problems & Solutions pdf
Chapter 12 - Problems & Solutions pdf


Handouts and other posted documents


Sign Test Using SPSS

The Arithmetic Mean as the Origin of the Variance (Hays)

Scheffe

Tukey

Independent Samples T-test as a Contrast

ANOVA Simple Effects

Final Exam Worksheet




Spring 2014 - Courses & Supervision
Psyx. 521 - Advanced Psychological Statistics II (GRAD)

Syllabus

Posted Friday, February 7


Factorial ANOVA (pdf)

Fixed Effects Factorial Analysis of Variance Using SPSS (with simple effects and post-hoc analyses)

Random Effects ANOVA (pdf)

Random Effects ANOVA - One-Way Between-Subjects Design in SPSS

Mixed Models (pdf)

Mixed Models (SPSS)

Block Designs & Repeated Measures

Randomized Block Design Analysis of Variance With N = 1 Per Cell Using SPSS

One-Way Repeated Measures Analysis of Variance

Two-Way Repeated Measures: Mixed Design

Inferring the Alternative Hypothesis: Risky Business


Posted Friday, March 7, 2014

Simple Linear Regression Part I (pdf)

Simple Linear Regression Part II (pdf)

Simple Linear Regression Part III (pdf)

Simple Linear Regression Using SPSS

Partial and Multiple Regression Part I (pdf)

Partial and Multiple Regression Part II (pdf)

Partial and Multiple Regression Part III (pdf)

Interpreting a Multiple Regression (pdf)


Posted Friday, March 28, 2014

Assignment #3 (due April 18)

-------------------------------------------
Psyx. 699 - Dissertation Supervision (GRAD)
Psyx. 290 - Multivariate Analysis (Independent Study - UGRAD)
Psyx. 596 - Computational Statistics Using R (Independent Study - GRAD)
Psyx. 596 - R Software for Statistics (Independent Study - GRAD)
Psyx. 596 - Essentials of Biostatistics (Independent Study - GRAD)
Psyx. 596 - Concrete vs. Abstract Learning in Chem Application (Independent Study - GRAD)




About Data & Decision

DATA & DECISION
consists of an ever-expanding collection of tutorial items, topics and instructional guides developed by Daniel J. Denis at the University of Montana. A first goal of these pages is to provide data-analytic guidelines and leadership to students in the Department of Psychology at the University of Montana and to the larger U of M community. These items can also be used by students and researchers at other universities who may benefit from data-analytic guidelines and topics in statistics and decision analysis more broadly defined. Select pages and notes are also used in quantitative coursework taught by Daniel J. Denis at the University of Montana.
Please address any comments, concerns, corrections or constructive (and very much welcomed) criticism to Daniel J. Denis at daniel.denis@umontana.edu. All comments will be carefully considered and reviewed in an effort to continuously ameliorate the site.

Please note that these pages are FOREVER under construction, and with any luck, the content will expand at an exponential rate! Even current DATA & DECISION pages are always subject to addition, proofing and revision of information, so check back often to see what's new!

HOW TO CITE THE LAB

The citation "Denis, D. (2010). [ . . . . . ]. Data & Decision Lab, Department of Psychology, University of Montana." can be used to cite pages, notes and tutorials referred to, where "[ . . . . . ]" refers to the particular resource. For instance, if you are citing notes titled "Moderated Regression," on July 28, 2010, the complete citation would take the form:

Denis, D. (2010). Moderated Regression. Data & Decision Lab, Department of Psychology, University of Montana.
[http://psychweb.psy.umt.edu/denis/datadecision/front/index.html] - retrieved on July 28, 2010.

If you are citing material within Data & Decision Lab pages that has been adapted from other sources, be sure to also provide due citation to those sources. However, if you are citing notes that are original to the site, the above citation will suffice. If in doubt, please e-mail daniel.denis@umontana.edu for instructions and clarification on how to cite a specific page or tutorial. We will be happy to advise you on the correct citation.                                     

                                                  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                          


Essential Mathematics

In these notes, we briefly (very briefly) survey elementary topics in mathematics from the excellent text Barnett, R.A., Ziegler, M. R. & Byleen, K.E. (2011). College Mathematics: For Business, Economics, Life Sciences, and Social Sciences. Prentice Hall.

Linear Equations Graphs & Lines
Simple Linear Regression
Functions Elementary Functions Quadratic Functions
Polynomials & Rational Functions Exponential Functions Logarithmic Functions
Introduction to Limits Infinite Limits Continuity
The Derivative Differentiation Procedures Differentials
Antiderivatives & the Indefinite Integral Integration by Substitution (Change of Variable) Definite Integral
Fundamental Theorem of Calculus Matrix Algebra Using R


Research & Data Analysis in the News

Every day we see research findings reported in the daily newspapers and on cable television programs. But is it credible? In this section, we critically evaluate research findings that make the popular press, and at minimum, ask questions that require answers before the research report can be taken seriously.  If you've spotted a research finding in the popular press that you feel is misleading or at minimum confusing (or even seemingly ridiculous), please send the link to daniel.denis@umontana.edu.

Sensitivity & Specificity - BMJ Rapid Response, July 9, 2010



DATA-ANALYTIC PROCEDURES, SOFTWARE, & THEORY

SOFTWARE & DATA MANAGEMENT

Getting to Know R (html)

How to read an SPSS (.SAV) file into R (html)

Dalgaard (2008) - What is the effect of categorizing a continuous predictor? (Using R to test).

Plotting Basic Functions & Curves in R

ANALYSIS OF VARIANCE (ANOVA)

Understanding ANOVA I (pdf)

Understanding ANOVA II (pdf)

Understanding ANOVA III (pdf)

The Logic of Linear contrasts in ANOVA (pdf)

Factorial ANOVA (pdf)

Random Effects ANOVA (pdf)

Mixed Model ANOVA (pdf)

Simple Effects in SPSS (html)


ANALYSIS OF COVARIANCE

Analysis of Covariance Using R (html)


SIMPLE & MULTIPLE REGRESSION

Simple Linear Regression Part I (pdf)

Simple Linear Regression Part II (pdf)

Simple Linear Regression Part III (pdf)

Partial and Multiple Regression Part I (pdf)

Partial and Multiple Regression Part II (pdf)

Partial and Multiple Regression Part III (pdf)

Interpreting a Multiple Regression (pdf)

Testing Interactions in Multiple Regression I (pdf)

Testing Interactions in Multiple Regression II (pdf)

Linear Models in R (html)

"Classic" Regression - Hays (html)

Moderated Regression (html)


REPEATED MEASURES ANOVA

2x2-way Repeated Measures Design in SPSS (with no between factors) (html)


MULTIVARIATE ANALYSIS OF VARIANCE (MANOVA)

MANOVA I (pdf)

MANOVA II (pdf)

MANOVA III (pdf)

MANOVA IV (pdf)

MANOVA V (pdf)

MANOVA VI (pdf)

MANOVA VII (pdf)


DISCRIMINANT ANALYSIS 

Discrim I (pdf)

Discrim II (pdf)


FACTOR ANALYSIS

Factor I (pdf)

Factor II (pdf)


PRINCIPAL COMPONENTS ANALYSIS (PCA)

Principal Components Analysis Using R (html)

Principal Components Analysis Using SPSS (html)


BINARY LOGISTIC REGRESSION 

Logistic Regression (pdf)

Binary Logistic Regression Using R (html)

Binary Logistic Regression Using SPSS (html)


BAYESIAN DECISION MODELS

Decision I (pdf) 

Decision II (pdf) 

Decision III (pdf) 

Decision IV (pdf) 

Denis, D. (2010). Toward a Bayesian Decision-Theoretic Approach to Hypothesis-Testing in Psychology. Journal of Non-Significant Results in Education, 1, 1.


CLUSTER ANALYSIS

Cluster Analysis Part I (pdf)

Cluster Analysis Part II (pdf)


SIMULATION

Simulation Using Excel (estimating ROI) (html)


STRUCTURAL EQUATION MODELING (AMOS)

Multigroup Analyses Using AMOS with Pairwise Comparisons of Path Coefficients (html)

How to Test for Mediation & Sobel Test

Equivalent Models in AMOS


MISC. ITEMS & SELECT STATISTICAL THEORY

Partition of Sums of Squares in ANOVA (pdf)

The Arithmetic Mean as Balance Point (pdf)

Unbiased Sample Variance (pdf)

Variance Estimators (biased and unbiased)

Sampling Distributions (pdf)

Understanding the Derivative Using a Parabola (html)

Matrix Algebra for Multivariate Statistics (pdf)

Gigerenzer (2002) - Excerpts from Calculated Risks: How to Know When Numbers Deceive You. Simon & Schuster, New York.


STUDY GUIDES

Denis, D. (2007). Study Guide for Kirk, R. E. (2008). Statistics: An Introduction. Wadsworth (pdf)


Advice for Graduate Students Completing Theses or Dissertations

1. Hypotheses first, statistical analyses second (unless you have plenty of data to cross-validate your exploratory analyses). The power of the scientist is in his or her ability to predict, not merely observe.

2. When completing your thesis or dissertation, foresee and address statistical issues ASAP. The expression "I'm done my dissertation, all that remains to do are my data analyses," usually means the project is nowhere near complete. Issues (extremely non-trivial ones) come up in data analysis that often require considerable time and thought, and the process should not be hurried. Procrastination on your statistical analyses is usually equivalent to procrastination on your entire thesis or dissertation, and an otherwise well-done project can be turned upside down overnight if a crucial methodological/statistical issue is not detected, addressed and resolved. For instance, hours and hours of analyses and dissertation discussion write-up can turn out to be of little value if you mistakenly assumed a predictor was continuous instead of categorical. Such an error is not minor, not merely a statistical artifact, and can cause an interpretational 360 on your conclusions. Do not underestimate the amount of time and effort and planning that is usually required to analyze your data and make sense of these analyses. When should you start planning your analyses and reading up on the analytical strategy? The day you begin planning the thesis or dissertation proposal. Plan your approach long-term so that when obstacles arise (and they always do), you'll have time built into your schedule to address and learn from them. If you require statistical and methodological advice, seek it out EARLY EARLY EARLY. Procrastinating on statistical issues is 10 times more stressful than addressing them promptly, and can often delay the timely completion of your thesis or dissertation. The best defense is a good offense, so attack your statistical and methodological issues head on.

3. p < .05 is usually always interesting statistically, but not always scientifically. Include effect size estimates in your results and discussion, and interpret them relative to other research in your field. Always contextualize your findings for your readers/audience. Should we be excited by what you found?

4. Verify that the conclusions made in your discussion match up with the conclusions allowed by your statistical analyses. Important as your findings may be, it's all too tempting to claim a solution to world hunger because all of your experimental rats have full bellies. Guard against unwary extrapolation and generalization.

5. Ask yourself as many critical and difficult questions as you can about your own research project, and research answers to them - these are likely to be similar questions posed by your committee at your defense. If you make your project bullet proof and are extremely well-prepared, the defense will likely be a celebratory demonstration of your knowledge, rather than a stressful "under the lights" exam of it. Don't wait until defense day to think about why you did a factor analysis rather than a principal components analysis. Have a well-prepared argument long beforehand. Anticipate as many questions as you can, know your craft, get confident, and you'll have a stronghold going into your defense. You do have a significant measure of control over how your defense proceeds and turns out if you prepare accordingly, and aspire to mastery of your chosen subject or field. 

SOME RECENT ANALYSES

Rogina, B. (2009). The Effect of Sex Peptide and Calorie Intake on Fedundity in Female Drosophila Melanogaster. The Scientific World Journal, 9, 1178-1189.

Synopsis: Used Generalized Estimating Equations with Negative Binomial Analyses.
----------------------------------------------------------------------------------------------------

Parashar, V., Frankel, S., Lurie, A. G., & Rogina, B. (2008). The Effects of Age on Radiation Resistance and Oxidative Stress in Adult Drosophila melanogaster. Radiation Research, 169, 707-711.

Synopsis: Used OLS regression, chi-squared, and logistic regression.
----------------------------------------------------------------------------------------------------

Parashar, V., & Rogina, B. (2009). dSir2 mediates the increased spontaneous physical activity in flies on calorie restriction. Aging, 1, 529-541.

Synopsis: Used linear models (ANOVA), post-hoc pairwise comparisons.
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Psychology Colloquia 

     Student Corner
  • Looking for a decent price on textbooks? Try Addall.com for the best price.
  • Looking for a job in academia? Try Chronicle of Higher Education, PsycCareers, JobBankUSA, JobBankCANADA.

  • TUTOR PLACEMENT - Do you need a tutor for your statistics or research methods class? Contact Dan @ daniel.denis@umontana.edu and we will try to match you up with a knowledgeable, caring, and reliable tutor. The majority of tutors we recommend charge a maximum of $20/hour and work independently. We currently only specialize in matching students for quantitative, statistics, and research methods courses, regardless of subject area (e.g., psychology, sociology, political science, biology, forestry, etc.). Do you require tutoring for another course? Contact us anyway and we'll see what we can do, or advise you on how and where to shop for a good tutor.
  • Want to get a higher degree in psychology but need advice and resources? Visit mastersinpsychology.net
  • Want to find the right psychology degree? Visit www.onlinepsychologydegrees.com
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  • Psychology Degrees On-line

On-line Statistical Calculators & Demos & Useful Links for Psychology, Statistics and Mathematics

Odds & Risk Ratios, Chi-Squared - provides computations for 2x2 table

Statpages - a variety of calculators and computational tools for various tests, including power estimation.

G*Power 3 - a free program for a variety of power analyses (including within-subject designs).

DanielSoper.com - a variety of programs for computing statistical power.

Iowa.edu - java applets for power analyses for various models.

Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). - Computational tools for probing interaction effects in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437-448.

Java Applets - a variety of programs that allow you to visualize changes in distributions instantaneously, as well as programs for running statistical tests.

Confidence Interval Simulation - "see" for yourself the meaning of a confidence interval (remember, it's the sample/interval that is random, not the population parameter!)

Prisoner's Dilemma (Game Theory Java)

Normal Distribution Applet - compute proportions under the curve.

Matrix Multiplication Java - multiply matrices of various dimensions.

Eigenvalue/Eigenvector Java

Visual Calculus

Calculus Review

Mathematics with Visualizations

Calculus Page

Paul's On-line Math Notes

Sobel Test Calculator

American Society of Trial Consultants

The Jury Expert

HG.org - World Wide Legal Directories

BMJ

Critical Past

Kids' Zone - Create a Graph

Physics formulas

APA Style

Real Analysis On-line Text

Effect Size Calculator

General Links


RECENT COURSEWORK

Psyc. 222 - Psychological Statistics (UGRAD) - Spring 2010

Syllabus

Denis, D. (2007). Study Guide for Kirk, R. E. (2008). Statistics: An Introduction. Wadsworth (pdf)


Spring 2010 - Psyc. 521 - Advanced Psychological Statistics II (GRAD) - OFFLINE

Syllabus

 Seminar Evaluation Criteria

 Seminar Schedule

Eyesenck (1974) - A Review of Factorial ANOVA

  How do I enter SPSS syntax for the Eyesenck Simple Effects? (best viewed in full-screen mode)

Pooling the Error Term in Random Effects Analysis of Variance

Block Designs & Repeated Measures

Covariance

Analysis of Covariance (ANCOVA)

ANCOVA Using SPSS


ASSIGNMENTS


Assignment 1 - due Wednesday, Feb. 3, 2010.

Assignment 2 - due Monday, Feb. 22, 2010.


READINGS

Cumming, G. (2007). Inference by Eye: Pictures of Confidence Intervals and Thinking about Levels of Confidence. Teaching Statistics, 29, 89-93.

McMinn, M. R., Tabor, A., Trihub, B. L. (2009). Reading in Graduate School: A Survey of Doctoral Students in Clinical Psychology. Training and Education in Professional Psychology, 3, 233-239.

Milani, R. M. Parrott, A. C., Turner, J. D., & Fox, H. C. (2004). Gender differences in self-reported anxiety, depression, and somatization among ecstasy/MDMA polydrug users, alcohol/tobacco users, and nondrug users. Addictive Behaviors, 29, 965-971.

Schmiege, S., & Russo, N. F. (2005). Depression and unwanted first pregnancy: longitudinal cohort study. British Medical Journal, 331, 1303-1306.

Turner, J. & Noh, S. (1988). Physical disability and depression: a longitudinal analysis. Journal of Health and Social Behavior, 29, 23-37.

Wang, J. (2004). A longitudinal population-based study of treated and untreated major depression. Medical Care, 42, 543-550.


Psyc 520 - Advanced Psychological Statistics - Fall 2009 (GRAD)

Chapter 1 - Problems & Solutions pdf
Chapter 2 - Problems & Solutions pdf
Chapter 3 - Problems & Solutions pdf
Chapter 4 - Problems & Solutions pdf

Chapter 5 - Problems & Solutions pdf
Chapter 6 - Problems & Solutions pdf
Chapter 7 - Problems & Solutions pdf
Chapter 8 - Problems & Solutions pdf

Chapter 10 - Problems & Solutions pdf
Chapter 11 - Problems & Solutions pdf
Chapter 12 - Problems & Solutions pdf

Handouts and other posted documents

Sign Test Using SPSS

The Arithmetic Mean as the Origin of the Variance (Hays)

Quiz 1 Key

Test 1 Key

Scheffe

Tukey

Independent Samples T-test as a Contrast

ANOVA Simple Effects

Final Exam Worksheet



Want to Specialize in Quantitative Methods? Here is a Sample Job Opportunity. Many such jobs are limited contract positions, but because they are funded by major granting associations, are often renewable. At minimum, such a position provides you with invaluable experience as you expand your quantitative repertoire and build a record of successful consultantships with public and private enterprise. At $62,000, the money isn't bad either! 








Requests? If you have a topic for which you would like to see a tutorial or additional notes, please contact Daniel J. Denis, Ph.D. at the Department of Psychology, University of Montana with your request. Many times brief overview notes are enough to get you started on a particular topic. Depending on your request and our current availability, your desired topic may appear on the site in the near future.

E-mail: daniel.denis@umontana.edu

Essential Mathematics

An Essay on the History of Panel Data Econometrics

Mathematical & Theoretical Statistics

MIT Course

Probability and Mathematical Statistics

Mathematical Proofs

Measure Theory

Expectations

Measure Theory and the Central Limit Theorem

Introduction to Mathematical Statistics

Mathematical Statistics

Statistical Theory

Advanced Calculus

Analysis


DNA (and other statistically-based) Evidence

Communicating DNA Evidence

Trial by Probability: Bayes' Theorem in Court

Bayes' Theorem & Weighing Evidence by Juries

Juror Understanding of DNA Evidence

Fundamentals of Probability and Statistical Evidence in Criminal Proceedings


History of Analysis

Russ, S. (2004). The mathematical works of Bernard Bolzano. Oxford.


Graphs & Visualization

Using R


Linear & Nonlinear Mixed Models

Generalized Linear Mixed Models


Linear Mixed Models in R and Splus (John Fox)

Mixed Models in SPSS

Nonlinear Mixed Models in SAS

Nonlinear Latent Growth


Data Visualization

Visualizing Categorical Data with SAS and R (Michael Friendly)

Tornado Diagrams

Interactive Visualization Techniques


FALL 2013 COURSES

Psyc 524 - Tests and Measurement - Fall 2013 (GRAD)

Syllabus


Posted Monday, September 9, 2013

Sketchy Notes Ch. 1, 2

Psychological Assessment for the Courts (pdf)

Buros Institute


Posted Monday, September 16, 2013

Factor I (pdf)

Factor II (pdf)


Posted Monday, September 30, 2013

Chapter 5 Sketchy Notes

Table 5.1


Posted Monday, October 7, 2013

Chapter 6 Sketchy Notes


Posted Monday, October 14, 2013

Chapter 7 Sketchy Notes


Posted Monday, October 28, 2013

Chapters 8, 9 - Sketchy Notes

Midterm Exam (blank)


Posted Monday, November 3, 2013

Taylor-Russell Tables

Binomial Effect Display

MTMMM


Posted Monday, November 11, 2013

Term Assignment *Due Dec. 11*


Posted Monday, November 18, 2103

Chapter 10 - Sketchy Notes

Chapter 11 - Sketchy Notes



Posted Monday, November 25, 2013

Chapter 12 - Sketchy Notes


Posted Monday, December 2, 2013

Chapter 13 - Sketchy Notes

Logistic Regression (pdf)


Psyc 522 - Multivariate Statistics - Fall 2013 (GRAD)

Syllabus


Posted Thursday, September 5, 2013

Rencher Chapter 2 Sketchy

Rencher Chapter 2 - Exercises

Least Squares Matrices

ASSIGNMENT #1


Posted Thursday, September 12, 2013

Rencher Chapter 3 Sketchy

Chap 3 SPSS (exploratory)

Chap 3 R (exploratory)

Meyers (key terms)

ASSIGNMENT #2


Posted Thursday, September 19, 2013

Rencher Chapter 4 Sketchy

Rencher Chapter 5 Sketchy

Hotelling's T-squared in SPSS

Hotelling's T-squared - Preview to MANOVA

Latent Profile Analysis


Posted Thursday, September 26, 2013

Rencher Chapter 6 Sketchy

MANOVA III (pdf)

MANOVA DATA TABLE

Simple Effects in SPSS MANOVA


Posted Thursday, October 3, 2013

Discrim I (pdf)

Discrim II (pdf)

Rencher Chapter 8 Sketchy


Posted Thursday, October 10, 2013

Rencher Chapter 9 Sketchy


Posted Thursday, October 17, 2013

3-group discrim

1   2   3   4   5


Posted Monday, October 21, 2013

*** Midterm Exam (Take-Home) - Due Oct. 31

Testing Interactions in Multiple Regression I (pdf)

Testing Interactions in Multiple Regression II (pdf)

Moderated Regression (html)


Posted Thursday, November 7, 2013

Rencher Chap. 11 Sketchy

Canonical Correlation SPSS (UCLA)


Cancor SAS (UCLA)


Posted Thursday, November 14, 2013

Factor I (pdf)

Factor II (pdf)

Principal Components Analysis Using SPSS (html)





Psyc 520 - Advanced Psychological Statistics I - Fall 2013 (GRAD)

Syllabus

         


GRADED ASSIGNMENTS

Class Problems - Aug. 30


Sketchy Notes

Appendix E
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5 - Sampling Distributions (pdf)
Chapter 5 - Sketchy
Chapter 6 - Sketchy
Chapter 7 - Sketchy
Chapter 8 - Sketchy


Posted Friday, November 15, 2013

Understanding ANOVA I (pdf)

Understanding ANOVA II (pdf)

Understanding ANOVA III (pdf)

The Logic of Linear contrasts in ANOVA (pdf)

Factorial ANOVA (pdf)


Posted Friday, November 22, 2013

Scheffe

Tukey


Independent Samples T-test as a Contrast

Final Exam Schedule



Problems & Solutions

Chapter 1 - Problems & Solutions pdf
Chapter 2 - Problems & Solutions pdf
Chapter 3 - Problems & Solutions pdf
Chapter 4 - Problems & Solutions pdf

Chapter 5 - Problems & Solutions pdf
Chapter 6 - Problems & Solutions pdf
Chapter 7 - Problems & Solutions pdf
Chapter 8 - Problems & Solutions pdf

Chapter 10 - Problems & Solutions pdf
Chapter 11 - Problems & Solutions pdf
Chapter 12 - Problems & Solutions pdf


Handouts and other posted documents


Sign Test Using SPSS

The Arithmetic Mean as the Origin of the Variance (Hays)

Scheffe

Tukey

Independent Samples T-test as a Contrast

ANOVA Simple Effects

Final Exam Worksheet



Psyc 510 - Trends in Psychological Research - Fall 2013 (GRAD)

Syllabus



Recent Courses


Psyx. 222 - Psychological Statistics (UGRAD) - Spring 2013

Syllabus

Denis, D. (2007). Study Guide for Kirk, R. E. (2008). Statistics: An Introduction. Wadsworth (pdf)

chap 2 graphs

Data visualization

Challenger

Test 1

Regression (summary notes for chapter 6)


Psyx. 521 - Advanced Psychological Statistics II (GRAD) - Spring 2013

Syllabus

Review of Stat I

Posted February 1, 2013

Understanding ANOVA I (pdf)

Understanding ANOVA II (pdf)   

*supplement for expected mean squares between

Understanding ANOVA III (pdf)

The Logic of Linear contrasts in ANOVA (pdf)

Factorial ANOVA (pdf)


Posted February 8, 2013

Assignment 1

Fixed Effects Factorial Analysis of Variance Using SPSS (with simple effects and post-hoc analyses)

Random Effects ANOVA (pdf)

Random Effects ANOVA - One-Way Between-Subjects Design in SPSS


Posted February 22, 2013


Mixed Models (pdf)

Block Designs & Repeated Measures

Randomized Block Design Analysis of Variance With N = 1 Per Cell Using SPSS

One-Way Repeated Measures Analysis of Variance

Two-Way Repeated Measures: Mixed Design

Hypothesis Testing Encyc. Entry

Inferring the Alternative Hypothesis: Risky Business


Posted March 1, 2013

Assignment 2

Simple Linear Regression Part I (pdf)

Simple Linear Regression Part II (pdf)

Simple Linear Regression Part III (pdf)

Simple Linear Regression Using SPSS


Posted March 8, 2013

Partial and Multiple Regression Part I (pdf)

Partial and Multiple Regression Part II (pdf)

Partial and Multiple Regression Part III (pdf)


Posted March 15, 2013

Multiple Regression Using SPSS Part I (pdf)

Multiple Regression Using SPSS Part II (pdf)


Posted April 12, 2013

Analysis of Covariance (ANCOVA)

ANCOVA Using SPSS




Psyc 521- Advanced Psychological Statistics II - Spring 2012 (GRAD)

Syllabus


Factorial ANOVA (pdf)

Fixed Effects Factorial Analysis of Variance Using SPSS (with simple effects and post-hoc analyses)

Random Effects ANOVA (pdf)

Random Effects ANOVA - One-Way Between-Subjects Design in SPSS


Posted Friday, Feb. 10

Random Effects ANOVA - Two-Way Between-Subjects Design in SPSS


Mixed Models (pdf)


Posted Friday, Feb. 17, 2012

Block Designs & Repeated Measures

Randomized Block Design Analysis of Variance With N = 1 Per Cell Using SPSS

One-Way Repeated Measures Analysis of Variance

Two-Way Repeated Measures: Mixed Design

Assignment #2 Due Friday, March 2, 2012 (in class)


Posted Friday, Feb. 24, 2012

Simple Linear Regression Part I (pdf)

Simple Linear Regression Part II (pdf)

Simple Linear Regression Part III (pdf)

Assignment #3 Due Friday, March 16, 2012 (in class)


Posted Friday, March 2, 2012

Simple Linear Regression Using SPSS

Partial and Multiple Regression Part I (pdf)

Partial and Multiple Regression Part II (pdf)

Partial and Multiple Regression Part III (pdf)



Posted Friday, March 23, 2012

Multiple Regression Using SPSS Part I (pdf)

Multiple Regression Using SPSS Part II (pdf)



Posted Friday, March 30, 2012

McMinn, M. R., Tabor, A., Trihub, B. L. (2009). Reading in Graduate School: A Survey of Doctoral Students in Clinical Psychology. Training and Education in Professional Psychology, 3, 233-239.

Turner, J. & Noh, S. (1988). Physical disability and depression: a longitudinal analysis. Journal of Health and Social Behavior, 29, 23-37.



Posted Friday, April 13, 2012

Analysis of Covariance (ANCOVA)

ANCOVA Using SPSS




Posted Friday, April 20, 212

Multilevel Modeling (Albright, Marinova)






FALL 2011 COURSES

Psyc 520 - Advanced Psychological Statistics - Fall 2011 (GRAD)



Syllabus

Sketchy Notes

Appendix E
Chapter 1
Chapter 2
Chapter 3
Chapter 4


Problems & Solutions

Chapter 1 - Problems & Solutions pdf
Chapter 2 - Problems & Solutions pdf
Chapter 3 - Problems & Solutions pdf
Chapter 4 - Problems & Solutions pdf

Chapter 5 - Problems & Solutions pdf
Chapter 6 - Problems & Solutions pdf
Chapter 7 - Problems & Solutions pdf
Chapter 8 - Problems & Solutions pdf

Chapter 10 - Problems & Solutions pdf
Chapter 11 - Problems & Solutions pdf
Chapter 12 - Problems & Solutions pdf


Handouts and other posted documents


Sign Test Using SPSS

The Arithmetic Mean as the Origin of the Variance (Hays)

Scheffe

Tukey

Independent Samples T-test as a Contrast

ANOVA Simple Effects

Final Exam Worksheet



Psyc 522 - Multivariate Statistics - Fall 2011 (GRAD)

Syllabus

Psyc 524 - Tests and Measurement - Fall 2011 (GRAD)

Syllabus

Psyc 510 - Trends in Psychological Research - Fall 2011 (GRAD)

Syllabus


Psyx. 222 - Psychological Statistics (UGRAD) - Spring 2011

Syllabus

Denis, D. (2007). Study Guide for Kirk, R. E. (2008). Statistics: An Introduction. Wadsworth (pdf)

Test 1 - February 23, 2011

Test 1 Descriptive Data

Test 2

Test 3

Psyx. 521 - Advanced Psychological Statistics II (GRAD) - Spring 2011

Syllabus


   Analysis of Variance Using SPSS: One-Way Between-Subjects Design

    Random Effects ANOVA (pdf)

   Random Effects ANOVA - One-Way Between-Subjects Design in SPSS

   SPSS Assignment #1 (pdf)

  Random Effects ANOVA - Two-Way Between-Subjects Design in SPSS

     Pooling the Error Term in Random Effects Analysis of Variance


     Mixed Models (pdf)


     Randomized Block Design Analysis of Variance With N = 1 Per Cell Using SPSS

     Fixed Effects Factorial Analysis of Variance Using SPSS (with simple effects and post-hoc analyses)

     Block Designs & Repeated Measures

    One-Way Repeated Measures Analysis of Variance

     Two-Way Repeated Measures: Mixed Design

     SPSS Assignment #2 (pdf)

     Simple Linear Regression Part I (pdf)

     Simple Linear Regression Part II (pdf)

     Simple Linear Regression Using SPSS

     Partial and Multiple Regression Part I (pdf)

     Partial and Multiple Regression Part II (pdf)

     Partial and Multiple Regression Part III (pdf)

     Multiple Regression Using SPSS Part I (pdf)

     Multiple Regression Using SPSS Part II (pdf)
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Seminar Evaluation Criteria

Eyesenck (1974) - A Review of Factorial ANOVA

  How do I enter SPSS syntax for the Eyesenck Simple Effects? (best viewed in full-screen mode)


Covariance

Analysis of Covariance (ANCOVA)

ANCOVA Using SPSS



READINGS

Cumming, G. (2007). Inference by Eye: Pictures of Confidence Intervals and Thinking about Levels of Confidence. Teaching Statistics, 29, 89-93.

McMinn, M. R., Tabor, A., Trihub, B. L. (2009). Reading in Graduate School: A Survey of Doctoral Students in Clinical Psychology. Training and Education in Professional Psychology, 3, 233-239.

Milani, R. M. Parrott, A. C., Turner, J. D., & Fox, H. C. (2004). Gender differences in self-reported anxiety, depression, and somatization among ecstasy/MDMA polydrug users, alcohol/tobacco users, and nondrug users. Addictive Behaviors, 29, 965-971.

Schmiege, S., & Russo, N. F. (2005). Depression and unwanted first pregnancy: longitudinal cohort study. British Medical Journal, 331, 1303-1306.

Turner, J. & Noh, S. (1988). Physical disability and depression: a longitudinal analysis. Journal of Health and Social Behavior, 29, 23-37.

Wang, J. (2004). A longitudinal population-based study of treated and untreated major depression. Medical Care, 42, 543-550.

Decision Analysis for Hypothesis Testing in Psychology

Denis, D. (2010). Toward a Bayesian Decision-Theoretic Approach to Hypothesis-Testing in Psychology. Journal of Non-Significant Results in Education, 1, 1.

Bayesian decision models are extremely useful to conceptualize and construct decision problems. They have been used in many disciplines (e.g., medicine, business, law), and have been fully developed by decision theorists such as James O. Berger (1993) and Robert L. Winkler (2002). I recently wrote a paper that promotes decision theory for psychology. The following is a table taken from the manuscript, and shows how prior information in the form of probabilities of null and alternative hypotheses can be integrated with data and loss estimates in arriving at an informed decision. The table can be found on p. 18 of the manuscript. 






Data & Decision - Challenger, 1986



On January 28, 1986, space shuttle Challenger was launched at a temperature of 31 degrees Farenheit. The coldest temperature of any prior launch was 53 degrees Farenheit. Prior to the launch, data were available to suggest that the rocket booster O-rings had an increased chance of failing in cold temperatures, yet the launch proceeded nonetheless. Was it raw data that informed the decision to launch, or were other factors involved?

National Geographic published a documentary on the Challenger accident, of which select outtakes can be viewed in YouTube below [Note: there is no question that television episodes such as those by National Geographic are sensationalized and the facts potentially exaggerated, and I personally have not verified their fact base. However, assuming their report is more or less accurate, sensationalism aside, it serves as a good example of the interplay of how organizations may use (or misuse) data in making decisions, regardless of what actually transpired].  

What are the predictors of a "go for launch" decision? What factors explain variance (R-squared like) in the dichotomous variable of the decision "launch yes" vs. "launch no"? 

For further details on the Challenger launch decision, including statistical analyses of the probability of failure prior to launch, see the following sources: 

Dalal, S. R., Fowlkes, E. B., & Hoadley, B. (1989). Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure. Journal of the American Statistical Association, 84

Friendly, M. (2000). Visualizing Categorical Data. SAS Publishing, NC. (pp. 208-211)

Vaughan, D. (1996). The Challenger Launch Decision: Risky Technology, Culture, and Deviance at Nasa. The University of Chicago Press, Chicago. 
 
 


Data & Decision
- Columbia, 2003


On February 1, 2003, NASA suffered its second loss of a shuttle. This time, space shuttle Columbia, as a result of damage suffered on one of its wings during launch, disintegrated during re-entry into the earth's atmosphere. Post-hoc testing revealed that a piece of foam produced a hole in the wing crippling the shuttle during re-entry. The tragedy is a perfect example of how "common sense," without empirical evidence, can lead even the best of engineers and scientists to false conclusions. Nasa engineers speculated that a piece of foam could not have caused any substantial damage to the shuttle wing. However, in their post-hoc test using real data and while suspending their speculative beliefs, NASA learned that a piece of foam traveling at extremely fast speeds could indeed impart significant damage to the shuttle wing (see second video below).

One lesson to take from the Columbia accident is that without proper empirical test, common sense and logic, even by "experts," can grossly deceive. In this case, the data came after the decision to "ok" the shuttle's return to earth (rather than sending a rescue shuttle mission to space to return the astronauts).

Pate-Cornell, M. E. & Fischbeck, P. S. (1994). Risk management for the tiles of the space shuttle. Interfaces, 24, 64-86.


Data-Analytic Articles, Books & Guides
(some available on-line - if you find any links that do not work, or out of date, please e-mail daniel.denis@umontana.edu and I will either correct the link, upload the paper myself, or delete it permanently).

Bargsted, M. (2010). An empirical assessment of the Bayesian unbiased voter hypothesis. Draft, Department of Political Science, University of Michigan.

Bollen, K. A. (1989). Structural equations with latent variables. Wiley: New York. [considered to be the foundational reference on SEM latent variable models]

Bullock, J. G. (2009). Partisan bias and the Bayesian ideal in the study of public opinion. The Journal of Politics, 71, 1109-1124.  

Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Lawrence Erlbaum Associates: London. 

Clemen, R. T., & Reilly, T. (2001). Making hard decisions. Duxbury: CA. [an excellent book for an introduction to using statistics as an aid for making hard decisions, the applications are in business mostly, though the same technology can be used across virtually all disciplines] 

Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45, 1304-1312.

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78, 98-104.

Cumming, G. (2007). Inference by Eye: Pictures of Confidence Intervals and Thinking About Levels of Confidence. Teaching Statistics, 29, 89-93.

Degroot, M. H. & Schervish, M. J. (2002). Probability and Statistics. Addison Wesley: New York. [an excellent "introductory" text on statistics, assumes knowledge of calculus]

Denis, D. (2003). Alternatives to Null Hypothesis Significance Testing. Theory & Science, 4.

Denis, D. (in press). Toward a Bayesian Decision-Theoretic Approach to Hypothesis-Testing in Psychology. 

Field, A. (2009). Discovering statistics using SPSS. Sage Publications: Los Angeles. [a very user-friendly book on how to use SPSS]

Fisher, R. A. (1925). Statistical methods for research workers. Oliver and Boyd: Edinburgh. [original and infamous text on ANOVA, linked from "Classics in the History of Psychology," a site devoted to providing on-line resources to students and researchers who are either learning or teaching the history of psychology (edited by Dr. Christopher D. Green of York University).

Fisher, R.A. (1966). The design of experiments. 8th edition. Hafner:Edinburgh. [foundational text on null hypothesis significance testing and experimental design]

Friendly, M., & Denis, D. (retrieved Oct. 2009). Milestones in the history of thematic cartography, statistical graphics and data visualization. [a collection of annotated graphics over history from pre-1600 to the present, the site originates from York University, Canada, the primary author is Dr. Michael Friendly]

Fox, J. (2009). A mathematical primer for social statistics (Quantitative applications in the social sciences). Sage Publications: Los Angeles. [and excellent overview of the mathematics regularly used in statistics (algebra, calculus), including a presentation of matrix algebra]  

Hays, W. (1994). Statistics. Harcourt College Publishers: New York. [a foundational text for statistics, though it does contain a few typos] 

JSTOR [an historian and researcher's dream come true, a huge archive of on-line papers and articles]

Keith, T. Z. (2006). Multiple regression and beyond. Pearson Education: New York. [a very applied book on regression models including a brief presentation of interactions in regression, as well as confirmatory factor analysis models]  

Maindonald, J. H. (2008). Using R for Data Analysis and Graphics: Introduction, Code and Commentary.

Magidson, J. & Vermunt, J. K. A Nontechnical Introduction to Latent Class Models. Statistical Innovations Inc.

Newsom, J. (retrieved Oct. 2009). Structural Equation Modeling Reference List. [a very thorough collection of articles related to SEM models and related issues]

R-Project for Statistical Computing.

Rozeboom, W. W. (1960). The fallacy of the null-hypothesis significance test. Psychological Bulletin, 57, 416-428. [one of the earlier (of many to come) articles criticizing statistical significance testing]

Searle, S. R. (1982). Matrix algebra useful for statistics. Wiley: New York. [an excellent text on matrix algebra and how they are used in multivariate statistics]

Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach's alpha. Psychometrika, 74, 107-120 [A critical look at the infamous reliability coefficient]

Sloughter, D. (2009). A primer of real analysis.

Smith, J. E., & Winterfeldt, D. V. (2004). Decision Analysis in "Management Science." Management Science, 50, 561-574.

Upton, G. & Cook, I. (2006). Dictionary of statistics. Oxford University Press: New York. [a good dictionary that serves as a nice accompaniment to any traditional statistics text]


Papers on Latent Curve/Growth Analysis, SEM, Multilevel, Mixed, HLM, Random-Coefficient Models

Bauer, D. J. & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40, 373-400.

Curran, P. J., Bauer, D. J., Willoughby, M. T. (2004). Testing main effects and interactions in latent curve analysis. Psychological Methods, 9, 220-237.

Curran, P. J., Bauer, D. J., Willoughby, M. T. (2006). Testing and probing interactions in hierarchical linear growth models. In C.S. Bergeman & S.M. Boker (Eds.), The Notre Dame Series on Quantitative Methodology, Volume 1: Methodological Issues in Aging Research, (pp.99-129). Mahwah, NJ: Lawrence Erlbaum Associates.

Hardy, S. A., & Thiels, C. (2009). Using latent growth curve modeling in clinical treatment research: An example comparing guided self-change and cognitive behavioral therapy treatments for bulimia nervosa. International Journal of Clinical and Health Psychology, 9, 51-71.



R Statistical Software

Kuhnert, P., & Venables, B. (2005). An Introduction to R: Software for Statistical Modeling & Computing. CSIRO Mathematical and Information Sciences Cleveland, Australia.

Venables, W.N. & Smith, D. M. (2011). An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics. Version 2.13.0 (2011-04-13).



  History Corner & Trivia
Click above image for short video on Hubble Deep Field
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Do you know the significance of the following diagram (below)?
(for a larger version (and the answer), go to the Milestones site and look under 1885)




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DATA & DECISION, Copyright 2013, Daniel J. Denis, Ph.D. Department of Psychology, University of Montana. Contact Daniel J. Denis by e-mail daniel.denis@umontana.edu.