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.
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

DATAANALYTIC
PROCEDURES, SOFTWARE,
& THEORY

Advice for
Graduate Students Completing
Theses or Dissertations
1.
Hypotheses first, statistical
analyses second (unless you
have plenty of data to
crossvalidate 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
nontrivial 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 welldone 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 writeup 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 longterm 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 wellprepared, 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 wellprepared 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,
11781189.
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,
707711.
Synopsis:
Used OLS regression, chisquared, and
logistic regression.

Parashar, V., & Rogina, B. (2009).
dSir2
mediates the increased spontaneous
physical activity in flies on
calorie restriction. Aging,
1, 529541.
Synopsis:
Used linear models (ANOVA), posthoc
pairwise comparisons.


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
 Interested
in Operations Management? Visit
http://www.operationsmanagement.net/
 Psychology
Degrees Online

Online
Statistical Calculators & Demos
& Useful Links for Psychology,
Statistics and Mathematics
Odds
& Risk Ratios, ChiSquared 
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 withinsubject
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, 437448.
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
Online 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 Online Text
Effect
Size Calculator
RECENT COURSEWORK
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 fullscreen 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,
8993.
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,
233239.
Milani,
R. M. Parrott, A. C., Turner,
J. D., & Fox, H. C. (2004).
Gender differences in
selfreported anxiety,
depression, and somatization
among ecstasy/MDMA polydrug
users, alcohol/tobacco users,
and nondrug users. Addictive
Behaviors, 29, 965971.
Schmiege,
S., & Russo, N. F. (2005).
Depression and unwanted first
pregnancy: longitudinal cohort
study. British Medical
Journal, 331,
13031306.
Turner,
J. & Noh, S. (1988).
Physical disability and
depression: a longitudinal
analysis. Journal
of Health and Social Behavior,
29, 2337.
Wang,
J. (2004). A longitudinal
populationbased study of
treated and untreated major
depression. Medical
Care, 42,
543550.
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.
Email: 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 statisticallybased) 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
TaylorRussell
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
Tsquared in SPSS
Hotelling's
Tsquared  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
3group
discrim
1
2
3
4
5
Posted
Monday, October 21, 2013
***
Midterm
Exam (TakeHome)  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
510  Trends in Psychological
Research  Fall 2013 (GRAD)
Syllabus 
Recent Courses
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 posthoc analyses)
Random
Effects ANOVA (pdf)
Random
Effects ANOVA  OneWay
BetweenSubjects Design in SPSS
Posted
Friday, Feb. 10
Random
Effects ANOVA  TwoWay
BetweenSubjects 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
OneWay
Repeated Measures Analysis of
Variance
TwoWay
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, 233239.
Turner,
J. & Noh, S. (1988).
Physical disability and depression: a
longitudinal analysis. Journal
of Health and Social Behavior,
29,
2337.
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
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.
521  Advanced
Psychological Statistics
II (GRAD)  Spring 2011
Syllabus
Analysis
of Variance Using SPSS:
OneWay BetweenSubjects
Design
Random
Effects ANOVA (pdf)
Random Effects
ANOVA  OneWay
BetweenSubjects Design in
SPSS
SPSS Assignment #1
(pdf)
Random
Effects ANOVA  TwoWay
BetweenSubjects 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 posthoc
analyses)
Block
Designs & Repeated
Measures
OneWay
Repeated Measures Analysis
of Variance
TwoWay
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)

Seminar
Evaluation Criteria
Eyesenck
(1974)  A Review of
Factorial ANOVA
How
do I enter SPSS syntax for
the Eyesenck Simple Effects?
(best viewed in
fullscreen 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,
8993.
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, 233239.
Milani,
R. M. Parrott, A. C.,
Turner, J. D., & Fox, H.
C. (2004). Gender
differences in selfreported
anxiety, depression, and
somatization among
ecstasy/MDMA polydrug users,
alcohol/tobacco users, and
nondrug users. Addictive
Behaviors, 29,
965971.
Schmiege,
S., & Russo, N. F.
(2005). Depression and
unwanted first pregnancy:
longitudinal cohort study. British
Medical Journal, 331,
13031306.
Turner,
J. & Noh, S. (1988).
Physical disability and
depression: a longitudinal
analysis. Journal
of Health and Social
Behavior, 29,
2337.
Wang,
J. (2004). A
longitudinal populationbased
study of treated and untreated
major depression. Medical
Care, 42,
543550. 

Decision
Analysis for Hypothesis
Testing in Psychology
Denis, D.
(2010). Toward
a Bayesian DecisionTheoretic
Approach to HypothesisTesting
in Psychology. Journal of
NonSignificant 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 Orings 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
(Rsquared 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:
PreChallenger Prediction of
Failure. Journal of the
American Statistical Association,
84.
Friendly, M. (2000). Visualizing
Categorical Data. SAS
Publishing, NC. (pp. 208211)
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
reentry into the
earth's atmosphere.
Posthoc testing
revealed that a piece of
foam produced a hole in
the wing crippling the
shuttle during reentry.
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
posthoc 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).
PateCornell,
M. E. & Fischbeck,
P. S. (1994). Risk
management for the
tiles of the space
shuttle. Interfaces,
24,
6486.


DataAnalytic Articles, Books &
Guides (some
available online  if you find any links
that do not work, or out of date, please
email 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, 11091124.
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, 13041312.
Cortina,
J. M. (1993). What
is coefficient alpha? An examination of
theory and applications. Journal of
Applied Psychology, 78,
98104.
Cumming,
G. (2007). Inference by Eye: Pictures of
Confidence Intervals and Thinking About
Levels of Confidence. Teaching
Statistics, 29, 8993.
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
DecisionTheoretic Approach to
HypothesisTesting in Psychology.
Field,
A. (2009). Discovering statistics using
SPSS. Sage Publications: Los Angeles.
[a very userfriendly 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 online 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
pre1600 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 online 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]
RProject
for Statistical Computing.
Rozeboom,
W.
W. (1960). The
fallacy of the nullhypothesis
significance test. Psychological
Bulletin, 57, 416428. [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,
107120 [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, 561574.
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, RandomCoefficient Models
Bauer,
D. J. & Curran, P. J. (2005). Probing
interactions in fixed and multilevel
regression: Inferential and graphical
techniques. Multivariate Behavioral
Research, 40,
373400.
Curran,
P. J., Bauer, D. J., Willoughby, M. T.
(2004). Testing
main effects and interactions in latent
curve analysis. Psychological
Methods, 9, 220237.
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.99129). Mahwah, NJ: Lawrence Erlbaum
Associates.
Hardy,
S. A., & Thiels, C. (2009). Using
latent
growth curve modeling in clinical
treatment research: An example comparing
guided selfchange and cognitive
behavioral therapy treatments for bulimia
nervosa. International Journal of Clinical
and Health Psychology, 9,
5171.
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 (20110413).
History Corner
& Trivia
Click above image for
short video on Hubble Deep
Field

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)


