Tuesday, March 07, 2017

2017 List of Summer Statistics and Methods Courses

Here is my 2017 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:  March 22, 2017 
(There may  be formatting glitches as I edit the tables; please bear with me!)

Location (link)DeadlineSessionsTopics
Academy of Marketing Science (Coronado, CA; near San Diego)5/27Partial Least Squares (PLS)
AAPOR Convention Workshops (New Orleans)
Half-day courses on 5/17, 18, & 21Variety of survey and sampling topics
APA Advanced Training Inst..........
Arizona State U.3/205/30-6/3Longitudinal SEM
Arizona State U.3/276/5-9Big Data
Michigan State3/276/5-9Methods/racial-ethnic diversity
U. Cincinnati4/36/19-23Non-linear methods
U. Wisconsin-Madison
Not offered in 2017; tent. sched. for 2018Single-case intervention research
American Statistical Assn (Baltimore)
7/29-8/3Continuing education at Joint Statistical Meetings
AnalyticAble (Hilton Head, SC)5/22-25
Diagnostic Measurement
Longitudinal Multilevel
Burke Institute
3-4 day classes in many cities (schedules)Marketing, methodology, and statistics
BYU Family Studies Center - Workshop in Sundance, Utah6/66/22-23 (pre-session 6/21)Longitudinal SEM
Ctr for the Adv of Res Methods and Analysis (CARMA)

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

2017 information not yet listedOne-day workshops on evaluation and applied research methods (most days have multiple options for which course to take)
Concordia U (Montreal)
Spring workshops
Curran-Bauer Analytics(Chapel Hill, NC)
Longitudinal SEM
Network Analysis
Enablytics (Mississauga; Toronto area)
Periodic spring and summer workshopsSEM and extensions thereof
Johns Hopkins/
Bloomberg SPH
urged by 6/9; but open until start of classes, if space permits
1- & 3-week courses (and shorter), 6/12-30Stat. and epi. techniques, survey methods, health issues
Johns Hopkins Summer Inst. in Mental Health Research
5/30-6/16Stat., epi., psychometric, and MH topics
Latent Variable Methods Workshop
(Providence, RI)

(This year, 11/29-12/1)Longitudinal
MIT Professional Educ. (Cambridge, MA)6/12-16

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

7/10-14Dyadic analysis
Muthen Mplus Courses

Short courses through the year at various locations
Northwestern U. Inst. for Policy ResearchCompetitive (application due 4/20); program will cover lodging & some other expenses


Ohio Program Evaluators' Group

Oklahoma St. U.Early bird: 5/15/15-16Mixture modeling, ordered regression, missing data, reproducibility issues
Penn St. U. (Methodology Center)3/16/29-30Power Analysis for Intensive Longitudinal
Portland St. (OR)
Dynamical Systems
Latent Class Analysis
Quebec Inter-University Center for Social Statistics (QICSS)
May through JulySEM (Rex Kline, 5/1-5)
("There are also CIQSS/QICSS summer school courses on longitudinal data analysis (French), SEM (French), and multilevel modeling (French, English), among other topics.")
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"
(Albuquerque, NM; organized by Todd Little)

6/5-9 & 6/12-16 15 courses in all, including SEM and extensions thereof, Bayesian analysis,meta-analysis, and networks
The Evaluators' Institute (TEI; affiliated with Claremont Graduate U)7/10-22Many courses centered on program development and evaluation
U at Buffalo

U of Chicago (Summer Inst. on Field Experiments)


Longitudinal Mplus
Dyadic (Multilevel/R)
UConn (Pre-conference workshops at Modern Modeling Methods)
5/22Pre-conference: Dynamic SEM
U Georgia College of Education

U Kansas Stats Camp5/15 5/22-6/9 R, Stata, SEM
U Kentucky "LINKS Ctr"

6/12-16Social network analysis
U Maryland
(Joint Program in Survey Methodology)

Workshops all year, including summer
U Maryland

U Maryland
E-mail Olivia Carter-Pokras for details

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

4-wk courses (6/26-7/21 & 7/24-8/18) , plus 3-5 day workshops*Intro, regression, multivariate, SEM, Bayes, network analysis, longitudinal, "R,"...
U Michigan
Ann Arbor (SPH)

1- & 3-week courses, Biostat, epi, logistic regression, meta-analysis, clinical trials, survival analysis...
U Michigan
Ann Arbor (SRC)
Lengths of few days to 8 weeks, 6/5-7/28 (schedule)Questionnaire design, sampling, data analysis, qualitative, focus grps, interviewing
U Minnesota (SPH)

1-week courses, 5/22-6/9Mainly 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)
4/225/15-18Latent-variable modeling/Mplus

U San Francisco
Contact Tom Stillman

Math 101 - Elementary Stat 
Math 106 - Quant. Methods in Business
5/22-2525 four-day courses
U Washington
1- to 3-wk courses, 7/10-28  Health-oriented applications of statistics

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

Location (link)DeadlineSessionsTopics
Essex UK                  
Courses of varying length, 7/10-8/18Nearly 50 courses in statistics, methodology, and data visualization
Eurocamp (Lisbon, Portugal)2.5-day courses, 5/1-5SEM & extensions, mediation/moderation, meta-analysis, & "My First Bayes"
European Consortium for Political Research, Methods School 6/1 (Early bird 4/21)Refresher courses 7/27-29; main courses 7/31-8/11ECPR Summer School (Budapest, Hungary)
European Ed. Prog. in Epidemiology (Florence, Italy)


3-week course in Epi
GESIS, Cologne, Germany
1- and 2- week courses, 7/10-28
Uni/bivariate stats, dimension reduction, regression, Big Data w/Python, text-mining
Intl. Meeting of the Psychometric Society 

This year in Zurich, Switzerland; short courses on 7/17
Istanbul (Turkey) Quantitative Lectures2017 program to be "announced soon"
King's College London (MRC SGDP)6/19-23Twin model fitting
London School of Economics (Methods Summer Program)
8/14-25Numerous 1- and 2-week courses
Modern Methods in Biostat & Epi (Italy)

 6/4-17 (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)
Oslo (Norway) Summer School in Comparative Soc. Science Studies7/24-28 & 7/31-8/4Mostly Political
Science courses,
but also specialized
methods courses (schedule)
Partial Least Squares
PLS School
Brief workshops all yearTopics focusing on
Partial Least Squares (courses in Europe)
St. Petersburg, Russia
IPSA-HSE Summer School in Concepts, Methods and Techniques in Political Sci.
Early registration ends 4/307/30-8/1310 courses
Soc. for Imprecise Probability: Theories and Applications (SIPTA)

U Amsterdam (Netherlands)8/4 (Early-bird 3/15)


Bayesian Modeling for Cognitive Science

Bayesian Hypothesis Testing in JASP Software
U Amsterdam (Netherlands) Psych Systems

U Calabria, Italy (When new page comes up, please scroll down for updates on Summer School 2017)

U Edinburgh, Scotland
UK "Figure It Out" (Birkbeck College, London)Year-roundNumerous 1-day courses
U Manchester (UK)6/26-30 & 7/3-7Qualitative, social networks, & mixed methods
(Course list)
UMIT (University for Health Sciences, Medical Informatics and Technology) Tyrol, Austria 

U Pompeu Fabra (Barcelona)6/206/26-7/7Several 1-wk courses
U St. Gallen (Switzerland; Global School Empirical Research Methods)6/1-23
U Ulster (N. Ireland UK)

Utrecht Univ. Netherlands

Statistics and survey-research 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)[Financial aid grants for students; due 5/1 6/19-30Social science research methods
PLS Conference
(Macau, China)

Tel Aviv Univ. SWARM (Summer Workshops in Advanced Research Methods)6/1
Advanced Regression
U of Melbourne (Statistical Consulting Ctr)

Courses throughout the year
U of Sao Paulo (IPSA; Methods & Concepts in Political Science)
1/8-26, 2018 
(summer in Brazil)

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 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

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)

Tuesday, October 26, 2010

(Updated October 22, 2013)

At it's most basic, the t-test is for comparing the means of two groups or conditions ("t for two"). We'll first take a brief detour into the topic of experimental design, as a lot of experiments compare two groups.

As we cover t-tests, beyond the introductory notes from my "Basic Statistics Page," we'll be looking at older blog posts here and here.

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...