Monday, March 05, 2018

2018 List of Summer Statistics and Methods Courses

Here is my 2018 compendium of summer stats and methods courses around the world. Check back often for updates!

Multiple programs at the same university or under the aegis of the same sponsor are shaded in color. As always, please notify me (via the link to my faculty webpage in the right-hand column) of errors, omissions, bad links, etc.

LAST UPDATED:  July 19, 2018 

Location (link)DeadlineSessionsTopics
AAPOR Convention Workshops (Denver)
5/15-19 (half-day courses)Variety of statistical, survey & sampling topics
APA Advanced Training Inst..........
Arizona State U.
5/29-6/2Longitudinal SEM
Arizona State U.
6/4-8Big Data
Michigan State
6/4-8Methods/racial-ethnic diversity
U. Cincinnati
6/18-22Non-linear methods
U. Wisconsin-Madison
8/13-17Single-case intervention research
American Statistical Assn (Vancouver)

Continuing education at Joint Statistical Meetings
AnalyticAble (Hilton Head, SC)
Burke Institute
3-4 day classes in many cities (schedules)Marketing, methodology, and statistics
BYU Family Studies Center - Workshop in Sundance, Utah6/86/20 (Mplus pre-session), 6/21-22Longitudinal SEM w/individual & family-level data
Ctr for the Adv of Res Methods and Analysis (CARMA)

Multiple topics in U.S. and international locations
Claremont Grad Univ
(also affiliated with TEI, see below)

8/15-22One-day workshops on evaluation and applied research methods (most days have multiple options for which course to take)
Concordia U (Montreal)
Spring workshops (old)
Curran-Bauer Analytics(Chapel Hill, NC)
Latent class/cluster
Longit. SEM
Enablytics (Toronto)
Spring & summer workshopsSEM, multilevel, growth modeling
Intl. Meeting of the Psychometric Soc.
(Columbia Univ., New York City)
7/9Pre-Conference Short Courses
Johns Hopkins/
Bloomberg SPH
urged by 6/4; but open until start of classes, if space permits
1- & 3-week courses (and shorter), 6/11-29Stat. and epi. techniques, survey methods, health issues
Johns Hopkins Summer Inst. in Mental Health Research
5/29-6/8Stat., epi., psychometric, and MH topics
Latent Variable Methods Workshop
(Providence, RI)

7/18-20 (tentative)Longitudinal
Loyola U. Chicago (downtown campus)3/128/5-11Meta-Analysis
MIT Professional Educ. (Cambridge, MA)6/18-22

Discrete Choice Analysis

Modeling/Simulation Transportation Networks
Michigan St U
(Enhancing Linkages btwn Math & Ecology)

5-7 day graduate courses in July & Aug
Michigan St U
Psychology (ADDED 5/9)

7/23-27Dyadic analysis
Muthen Mplus Courses

Short courses through the year at various locations
Northwestern U. Inst. for Policy ResearchCompetitive (application due 3/30); program will cover lodging & some other expenses7/30-8/9

Cluster-Randomized Trials
Ohio Program Evaluators' Group

Oklahoma St. U.4/15 (early-bird); 5/105/14-15Modeling within-person associations in longitudinal data (with Lesa Hoffman)
Penn St. U. (Methodology Center)

Optimization of Interventions (Bethesda, MD)

Ecological Momentary Assessment
Portland St. (OR)
6/17-231-day courses focused on R; also SEM
Quebec Inter-University Center for Social Statistics (QICSS)
May and June1-5 day courses (most in French; rest appear to be in English)
Scientific Software International (Chicago)
     " "
Society for Mathematical Psychology (Madison, WI)Potential funding for grad students & early professionals (apply by 3/15). Contact Daniela Mejia for info: mejia6@illinois.edu7/21-22International Workshop and Symposium on Probabilistic Specification and Quantitative Testing of Decision Theories (in conjunction with SPM)
STATA Usage Workshops (here & here)

Short courses all year in U.S. and other countries
Statistical Horizons

Brief workshops in Philadelphia and elsewhere
"Stats Camp"
(all year, in multiple locations; organized by Todd Little)

"Summer Camp" in Albuquerque, NM, 6/4-8, 6/11-1515 courses in all, including SEM and extensions thereof, Bayesian analysis,meta-analysis, and more
The Evaluators' Institute (TEI; affiliated with Claremont Graduate U)
Many courses centered on program development and evaluation
U at Buffalo
Qualitative Analysis
Spatial Analysis
Propensity Scores
U Calif Santa Barbara "Methods U" (Gevirtz Grad School of Educ)6/1 (added very late for 2018, but something to keep on radar for future)6/11-12SEM, Latent Class Analysis, Mediation-Moderation, Quasi-Exp
U Chicago (Summer Inst. on Field Experiments)


(6/7-8 & 21-22)
Mixture Modeling
Multilevel (with R)
Dyadic (with R)
(R prep sessions) 
UConn (Modern Modeling Methods)
Pre-conf (5/21)

Post-conf (5/24)
Just-in-Time Adaptive Interventions
IRT/Latent Variables
U Georgia College of Education

U Kansas Stats Camp
U Kentucky "LINKS Ctr"

6/4-8Social network analysis
U Maryland
(Joint Program in Survey Methodology)

Workshops all year, including summer
U Maryland
(Measurement, Statistics and Evaluation Program)
Workshops all year, including summer
U Maryland
E-mail Olivia Carter-Pokras for details

UMass Amherst
See within U. Michigan ICPSR
U Michigan
Ann Arbor (ICPSR)

4-wk courses (6/25-7/20 & 7/23-8/17) , plus 3-5 day workshops*Intro, regression, multivariate, SEM, Bayes, network analysis, longitudinal, "R,"...
U Michigan
Ann Arbor (SPH)

1- & 3-week coursesBiostat, epi, meta-analysis, clinical trials, survival analysis...
U Michigan
Ann Arbor (SRC)
6/4-7/27 (Lengths of few days to 8 wks)Questionnaire design, sampling, data analysis, qualitative, interviewing
U Minnesota (SPH)

(Three rounds of 1-week courses) 
Mainly public health content, but some methods courses (e.g., GIS, quantitative & qualitative analysis)
U North Carolina, Chapel Hill (Odum Institute)

Many courses, some in conjunction with Michigan ICPSR
UNC Greensboro/ Andy Supple (Applied Analyses)

5/21-24Latent-variable modeling/Mplus

7/26-27 (JUST ADDED)
Clin. Trials/Dyn. Trtmt
Longitudinal HLM
Applied Psychometrics

U San Francisco
Contact Tom Stillman

Math 101 - Elementary Stat 
Math 106 - Quant. Methods in Business
5/21-2427 four-day courses
U Washington
1- to 3-wk coursesHealth-oriented applications of statistics

*Some workshops in other cities, such as Amherst, MA; Boulder, CO; & Chapel Hill, NC.

Location (link)DeadlineSessionsTopics
Essex UK                  
Nearly 50 courses in statistics, methodology, and data visualization
Eurocamp (Held in 2018 as a "World Camp" in Australia)

European Consortium for Political Research, Methods School 4/20 (Early bird )7/26-8/10ECPR Summer School (Budapest, Hungary)
European Ed. Prog. in Epidemiology (Florence, Italy)


3-week course in Epi
GESIS, Cologne, Germany (and elsewhere)

Courses year-round
HSE University Russia (St. Petersburg) Institute of EducationAdmissions close 4/307/29-8/4International Summer School on Applied Psychometrics in Psychology & Education
Intl. Meeting of the Psychometric Society 

See USA for 2018
Istanbul (Turkey) Quantitative Lectures7/9-14Information Complexity...
King's College London
Courses year-round on statistics, methods, and mental health
Ljubljana Doctoral Summer School (Slovenia)4/13 (Early bird),  4/14-onward (Late fee)Three rounds of 1-wk courses (7/2-6, 7/9-13, 7/16-20)Exp. design, multilevel, mixed-methods, regression, SEM, PLS, qualitative (NVIVO)
London School of Economics (Methods Summer Program)
6/18-8/17Numerous 1- and 2-week courses
Modern Methods in Biostat & Epi (Italy)

6/3-16 (1- & 6-day courses)Epidemiology,
biostatistics, regression, survival
analysis, and other courses (many courses focus on Stata program)
Muthen Mplus Courses

Short courses through
the year at various
National Centre for Research Methods (UK)Ongoing courses
(including brief ones)

Northern Institute of Technology (Hamburg, Germany) ADDED 4/24
6/6-9Partial Least Squares
Oslo (Norway) Summer School in Comparative Soc. Science Studies
Mostly Political
Science courses,
but also specialized
methods courses (schedule)
Oxford U. 5/8-171-day workshops on R, SEM, multilevel models, IRT
Partial Least Squares
PLS School
Brief workshops all yearTopics focusing on
Partial Least Squares (courses in Europe)
PS Statistics (Glasgow, Scotland)7/2-6Social Network Analysis (Using R)
St. Petersburg, Russia
IPSA-HSE Summer School in Concepts, Methods and Techniques in Political Sci.
Early registration ended 2/1Two rounds of 1-wk courses (7/30-8/3, 8/6-10), plus beginner R course 7/26-2810 courses including regression, SEM, text analysis, & qualitative methods
Soc. for Imprecise Probability: Theories and Applications (SIPTA), Oviedo, Spain (held in even-number years)7/24-28
U Amsterdam 




Model-Based Neuroscience

Bayesian Modeling for Cognitive Science; JASP & Win BUGS

Bayesian Hypothesis Testing/JASP
U Amsterdam 
Psych Systems

Now appears to hold winter workshops
U Calabria, Italy, Research Methods for Social SciencesCourses taught in English 7/16-20 (Text mining) & 9/10/14 (Exp. design)... plus 8 other courses in Italian throughout the summer
U Edinburgh, ScotlandShort courses
UK "Figure It Out" (London School of Econ., Birkbeck College, & other venues)Year-roundNumerous 1-day courses
U Manchester (UK) UPDATED 4/217/2-6
R, longitudinal,  social networks, SEM/Mplus
UMIT (University for Health Sciences, Medical Informatics and Technology) Tyrol, Austria 

Courses year-round
U of Oxford (see Oxford U. above)
U Pompeu Fabra (Barcelona)
6/25-7/6Numerous 2- and 3-day courses
U St. Gallen (Switzerland; Global School Empirical Research Methods)June (primarily)Several 5-day courses
U Ulster (N. Ireland UK)

U Verona (Italy)7/2-6Longitudinal (multilevel and SEM)
Utrecht Univ. Netherlands

Statistics and survey-research courses, along with social-science content courses
XL Stat 

Brief courses year-round in Paris, London, New York, & elsewhere 

Link to Google Translate to figure out phrases in languages other than one's own.

Location (link)DeadlineSessionsTopics
National University of Singapore (IPSA)
3/31 (early-bird); 6/15; see financial aid
6/25-7/6Social science research methods
PLS Conference

Tel Aviv Univ. SWARM (Summer Workshops in Advanced Research Methods)6/16/14-15, 17
Adv. Regression
Mplus SEM
Multilevel Analysis
U of Melbourne (Statistical Consulting Ctr)

Courses throughout the year
U of Sao Paulo (IPSA; Methods & Concepts in Political Science)
1/14-2/1, 2019
(summer in Brazil)

"World Camp" (Brisbane)
4/26-28Latent Class/Latent Profile, SEM, Mplus

Friday, October 10, 2014

Writing Up Statistical Results in APA Style

Dr. Shera Jackson, our lab instructor, has compiled the following list of web resources for how to write up statistical results in APA style:

Reporting Statistics in APA Style (Matthew Hesson-McInnis, Illinois State University)

Reporting Results of Common Statistical Tests in APA Format (Psychology Writing Center, University of Washington)

Statistics in APA Style (Craig Wendorf, University of Wisconsin-Stevens Point); see summary table on page 5

Monday, August 25, 2014

Welcome Message

The message below is old, as I haven't taught Intro Stats in several years. However, it contains some potentially useful links, as well as my overview of the course.

Welcome to QM I for Fall 2014, and, for those of you who haven't been in school here in the past, welcome to Texas Tech! You'll be visiting this welcoming page a lot, as it contains the links for our lecture notes.

I'll do my best to provide a lot of practical, real-world exercises in analyzing data, and I'll try to keep things fun. This passage from a book I read several years ago, Coincidences, Chaos, and All That Math Jazz, by Edward B. Burger and Michael Starbird, provides a concise overview of what statistics can offer:

Statistics can help us understand the world. It is a powerful and effective tool for placing economic, social welfare, sports, and health issues into perspective. It molds data into digestible morsels and shows us a measured way to look at situations that have either random or unknown features. But we must use common sense when applying statistics or other tools that draw on our experience of the world to shape data into meaningful conclusions (p. 60).

In addition, the following article sets forth some goals for what you should learn in this class (and other classes). We can access this article via the Texas Tech Library website or Google Scholar.

Utts, J. (2003). What educated citizens should know about statistics and probability. American Statistician, 57(2), 74-79.

LECTURE NOTES (asterisked [*] pages are from my undergraduate research-methods class).

Units of analysis*


Types of Measures*

Visual depictions of a data distribution (examples):
  • Histograms (overview; determining interval/bin widths; SPSS instructions here and here). UPDATE 9/10/14: King and Minium (2003) offer some advice on interval widths and the appearance of histograms, citing the "convention that the height of the figure should be about three-quarters of the width." Also, "When we have relatively few cases and wish to see if a pattern exists, we can often reduce irregularity due to chance fluctuation by using fewer class intervals than usual" (pp. 56-57).
  • Frequency tables (click here and then select output), which contain similar information to histograms; the cumulative percentages also are roughly similar to percentiles (for a given score, you can see what percent of the sample falls below it)
  • Shapes of distributions
  • As a class exercise, we will attempt to reproduce via SPSS this histogram of U.S. Presidents' ages upon assuming office (note that Grover Cleveland, who served two non-consecutive terms is counted as being "two presidents," the 22nd and 24th)
Descriptive statistics:* Central tendency (mean, median, and mode) and spread (standard deviation); moments of a distribution; and z-scores (here, here, here, and here)

Probability (here and here)

Correlation and significance-testing



Non-parametric statistics

Statistical power

Confidence intervals

Tuesday, November 16, 2010

Statistical Power (Overview)

This week we'll be covering statistical power (also known as power analysis). Power is not a statistical technique like correlation, t-test, and chi-square. Rather, power involves designing your study (particularly getting a large enough sample size) so that you can use correlations, t-tests, etc., more effectively. The core concept of power, like so much else, goes back to the distinction between the population and a sample. When there truly is a basis in the population for rejecting the null hypothesis (e.g., a non-zero correlation, a non-zero difference between means), we want to increase the likelihood that we reject the null from the analysis of our sample. In other words, we want to be able to pronounce a result significant, when warranted. Here are links to my previous entries on statistical power.

Introductory lecture

Why a powerful design is needed: The population may truly have a non-zero correlation, for example, but due to random sampling error, your sample may not; plus, some songs on statistical power!

Remember that there's also the opposite kind of error: The population truly has absolutely no correlation, but again due to random sampling error, you draw a sample that gives the impression of a non-zero correlation.

How to plan a study using power considerations

Wednesday, November 03, 2010


My introductory stat notes for methods class have some introductory information on chi-square.

Here are direct links to some old chi-square blog postings. This one discusses the reversibility error and how properly to read an SPSS printout of a chi-square analysis. The other one illustrates the null hypothesis for chi-square analyses in terms of equal pie-charts.

The following photo of the board, containing chi-square tips, was added on November 15, 2011 (thanks to Selen).

Plus a song (added November 1, 2011):

One Degree is Free
Lyrics by Alan Reifman
(May be sung to the tune of “Rock and Roll is Free,” Ben Harper)

Look at your, chi-square table,
If it is, 2-by-2,
One cell can be filled freely,
While the others take their cue,

The formula that you can use,
Come on, from the columns, lose one,
And one, as well, from the rows,
Multiply the two, isn’t this fun?

One degree is free, in your table,
With con-tin-gen-cy, in your table,
One degree is free, in your table,
…free in your table,
…free in your table,

Say, your table is larger,
Maybe it’s 2-by-4,
Multiply one by three,
3 df are in store,

The df’s are essential,
To check significance,
Go to your chi-square table,
And find the right instance,

Three degrees are free, in your table,
With con-tin-gen-cy, in your table,
Three degrees are free, in your table,
…free in your table,
…free in your table,

(Guitar Solo)

Wednesday, October 06, 2010


NOTE: I have edited and reorganized some of my writings on correlation to present the information more coherently (10/11/2012).

Our next topic is correlational analysis. There are four major areas to address:

1. A general introduction to correlation, which is available here amidst my Research Methods lecture notes.

2. Running correlations in SPSS. This graphic of SPSS output tries to make clear that a sample correlation and its significance/probability level are two different things (although related to each other).

Second, in graphing the data points and best-fitting line, you start in "Graphs," go to "Legacy Dialogs," and select "Scatter/Dot." Then, select "Simple Scatter" and click on "Define." You will then insert the variables you want to display on the X and Y axes, and say "OK." When the scatter plot first appears, you can click on it to do more editing. To add the best-fit line, under "Elements," choose "Fit Line at Total."

Initially the dots will all look the same throughout the scatter plot. To make each dot represent the number of cases at that point (either by thickness of the dot or through color-coding), click on the "Binning" icon (circled below in red). Thanks to Xiaohui for finding this!

3. Statistical significance and testing the null hypothesis, as applied to correlation. Subthemes within this topic include how sample size affects the ease of getting a statistically significant result (i.e., rejecting the null hypothesis of zero correlation in the full population), and one- vs. two-tailed significance.

4. Partial correlation (i.e., the correlation between two variables, holding constant one or more "lurking" variables).

Here are some additional tips:

5. In evaluating the meaning of a correlation that appears as positive or negative in the SPSS output, you must know how each of the variables is keyed (i.e., does a high score reflect more of the behavior or less of the behavior?).

6. Statistical significance is not necessarily indicative of social importance. With really large sample sizes (such as we have available in the GSS), even a correlation that seems only modestly different from zero may be statistically significant. To remedy this situation, the late statistician Jacob Cohen devised criteria for "small," "medium," and "large" correlations.

7. Correlations should also be interpreted in the context of range restriction (see links section on the right). Here's a song to reinforce the ideas:

Restriction in the Range 
Lyrics by Alan Reifman
(May be sung to the tune of “Laughter in the Rain,” Sedaka/Cody)

Why do you get such a small correlation,
With variables you think should be related?
Seems you’re not studying the full human spectrum,
Just looking at part of bivariate space,
All kinds of thoughts start to race, through your mind…

Ooh, there’s restriction in the range,
Dampening the slope of the best-fit line,
Ooh, I can correct r for this,
Put a better rho estimate in its place...

Thursday, October 22, 2009

Further t-Test Info (SPSS Output, Independent Samples)

(Updated October 26, 2014)

I have just created a new graphic on how to interpret SPSS print-outs for the Independent-Samples t-test (where any given participant is in only one of two mutually exclusive groups).

This new chart supplements the t-test lecture notes I showed recently, reflecting a change in my thinking about what to take from the SPSS print-outs.

One of the traditional assumptions of an Independent-Samples t-test is that, before we can test whether the difference between the two groups' means on the dependent variable is significant (which is of primary interest), we must verify that the groups have similar variances (standard-deviations squared) on the DV. This assumption, which is known as homoscedasticity, basically says that we want some comparability to the two groups' distributions in terms of their being equally spread out, before we can compare their means (you might think of this as "outlier protection insurance," although I don't know if this is technically the correct characterization of the problem).

If the homoscedasticity (equal-spread) assumption is violated, all is not lost. SPSS provides a test for possible violation of this assumption (the Levene's test) and, if violated, an alternative solution to use for the t-test. The alternative t-test (known as the Welch t-test or t') corrects for violations of the equal-spread assumption by "penalizing" the researcher with a reduction of degrees of freedom. Fewer degrees of freedom, of course, make it harder to achieve a statistically significant result, because the threshold t-value to attain significance is higher.

Years ago, I created a graphic for how to interpret the Levene's test and implement the proper t-test solution (i.e., the one for equal variances or for unequal variances, as appropriate). Even with the graphic, however, students still found the output confusing. Stemming from these student difficulties and some literature of which I have become aware, I have changed my opinion.

I now subscribe to the opinion of Glass and Hopkins (1996) that, “We prefer the t’ in all situations” (p. 305, footnote 30). Always using the t-test solution for when the two groups are assumed to have unequal spread (as depicted in the top graphic) is advantageous for a few reasons.

It is simpler to always use one solution than go through what many students find to be a cumbersome process for selecting which solution to use. Also, despite the two solutions (assuming equal spread and not assuming equal spread) being different and having different formulas in part, the bottom-line conclusion one draws (e.g., that men drink significantly more frequently than do women) often is the same under both solutions. If anything, the preferred (not assuming equal spread) solution is a little more conservative; in other words, it makes it a little harder to obtain a significant difference between means than does the equal-spread solution. As a result, our findings will have to be a little stronger for us to claim significance, which is not a bad thing.

I've also created a graphic to interpret the SPSS output of a paired t-test.

I have also taken a screenshot from this University of Georgia webpage, which gives the formula for a paired t-test. The main focus is, of course, comparing the means of two variables, but as shown below, I have highlighted where the correlation r between the two variables enters the formula.


Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in psychology and education (3rd ed.). Needham Heights, MA: Allyn & Bacon.