Wednesday, February 05, 2014

2014 List of Summer Statistics and Methods Courses

Here is my 2014 compendium of summer stats and methods courses around the world. I am leaving all programs from the last few years in the table below, on the assumption that most will also operate this year. Information for 2014 (e.g., session dates) is filled in, if known. Cells are blank for programs that have not yet reported this information for 2014.  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:  April 13, 2014  
(There may  be formatting glitches as I edit the tables; please bear with me!)

UNITED STATES/NORTH AMERICA
Location (link)DeadlineSessionsTopics
AAPOR Convention Workshops (Anaheim, CA)
5/14-18Survey methods, e.g., total survey error, complex design, and smartphone/tablet
APA Advanced Training Inst..........
UC Davis3/245/27-31Longitudinal SEM
UC Davis3/246/2-6Exploratory data mining
Michigan State3/316/2-6Methods/racial-ethnic diversity
U. Cincinnati3/316/16-20Non-linear methods
American Statistical Assn

Continuing education at Joint Statistical Meetings
BYU (Family Studies Center)
6/19-20Longitudinal SEM
Ctr for the Adv of Res Methods and Analysis (CARMA)
6/2-7 (Wayne St. Univ., Detroit)Multiple topics in Detroit, Michigan and international locations
Ctr for Spatially Integrated Social Sci.


Claremont Grad Univ

Evaluation and applied research methods
Concordia U (Montreal) Workshops on Soc. Sci. Research

Features "short, intensive seminars," mainly in political science, but also in stats/methods
Curran-Bauer Analytics(Chapel Hill, NC)
6/2-6
6/9-13
6/16-18
Multilevel models
SEM
Latent curve
DePaul U. (Education)

SEM
Intl. Meeting of the Psychometric Society (Madison, WI)
7/21 (pre-conference)Mplus, item analysis, multi-way analysis, social networks, propensity scores (full details here
Johns Hopkins/
Bloomberg SPH
Registration 
urged by 6/13but open until start of classes, if space permits
1-, 2-, & 3-week courses (and shorter) 6/16-7/3Stat. and epi. techniques, health issues (HIV/AIDS, etc.);  COURSE LIST
Johns Hopkins
Mental Health



Longitudinal Research Inst.(UC Davis)


Michigan St U
Enhancing Linkages btwn Math & Ecology

6/2-6
6/9-13
6/16-20
Max. Likelihood
Bayesian
SEM
Michigan St U
Psychology

6/23-27Dyadic analysis
Muthen Mplus Courses

Short courses through the year at various locations
North Carolina St

Applied regression, multilevel, and latent variable models
Northwestern U. Inst. for Policy Research
7/7-17Cluster-randomized trials
Oregon St.

"Please check back in early 2014 for details of our next Summer Institute."
Partial Least Squares Applications Symposium(Montreal)
5/30-6/1
Penn St. U. 
Summer Inst on Innovative Methods

6/19-20Developing Adaptive Interventions (being held at U of Michigan)
Portland St. (OR)
6/14-15
6/16-17
6/18-19
Longitudinal SEM
Intro SEM
"R"
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"
(Lawrence, Kansas)

6/2-6 and 6/9-139 courses in all, including SEM and extensions thereof, IRT, meta-analysis, networks, and modern mediation/moderation
Texas A&M (Educ & Human Dev)
5/18-23Meta-analysis, instrument dev't, SEM, IRT, HLM (see list)
U at Buffalo


U of Calgary
UConn
(DATIC)

6/4
6/5-6
6/9-13
6/16-20
6/23-27
"R"
Meta-Anl. w/ R
Dyadic (multilevel) 
SEM
HLM
UConn (Neag School of Ed.)


U Georgia College of EdOne week before each workshopBrief workshops in MayLongitudinal Analysis, SEM, Qualitative (Phenomenology)
U Illinois, Urbana-Cham


U Kansas -- See "Stats Camp" above


U Kentucky "LINKS Ctr"

Social network analysis
U Maryland
(Joint Program in Survey Methodology)

Workshops all year, including summer
U Maryland
(Measurement, Statistics and Evaluation Program)

5/28-30
6/26-27
7/22-24
8/13-15
Secondary Analysis
Logistic Regression Bayesian Stat Models
Appl Data Analysis w/R
UMass Amherst


6/2-4
6/9-13
6/16-20
6/24-27   
Dev. trajectories
HLM
Categorical/STATA
Intensive longit.
U Michigan
Ann Arbor (ICPSR)

4-wk courses 6/23-7/18 & 7/21-8/15, plus 3-5 day workshops*Intro, regression, multivariate, SEM, Bayes, network analysis, longitudinal, "R,"...
U Michigan
Ann Arbor (SPH)

1- & 3-week courses, 7/6-25Biostat, epi, logistic regression, meta-analysis, clinical trials, survival analysis...
U Michigan
Ann Arbor (SRC)

1-, 2-, & 4-week courses, 6/2-7/25Questionnaire design, sampling, data analysis, qualitative, focus grps, interviewing
U Minnesota (SPH)
5/26–6/165-day (and shorter) courses mainly with public health content, but some methods courses (e.g., GIS, epi, qualitative)
U New Hampshire (Carsey Inst)


U San FranciscoContact Tom Stillman


Math 101 - Elementary Stat Math 106 - Quant. Methods in Business
U Texas
(Statistics & Scientific Computation)



5/19-22Many courses in basic and advanced statistics, and software programs
U Washington
(Biostatistics)

Courses of varying length in June & July......covering health-oriented applications of statistics
Western Psychol Association 
(Portland, OR)

4/24-27Workshops on IRT, multiple-regression, and latent growth models




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

EUROPE
Location (link)DeadlineSessionsTopics
Ctr for the Adv of Res Methods and Analysis (CARMA)......Ongoing series
of workshops
around the world
Essex UK                  
7/7-8/15
European Consortium for Political Research (held in Ljubljana, Slovenia)
7/24-8/9Mostly 1- and 2-week courses on numerous topics, such as regression, SEM, multilevel, "R," and qualitative (full list)
European Ed. Prog. in Epi. (Florence, It)
6/23-7/11
(special pre-course on genomics 6/16-20)
One-week courses on various topics in
epidemiology
German Collaborative Summer School in Epidemiology
(Bremen)

7/28/8-1Statistics, epidemiology, and health content (cardiovascular)
German Soc Sci Infra-structure Services (GESIS);CologneRegistration opens 2/188/7-29 (courses 1 week or shorter)Mostly methods courses, but a few in statistics including "R," and Mplus SEM
Istanbul (Turkey) Quantitative Lectures
8/25-30"Data Mining with R"
King's College London (MRC SGDP)
6/16-20Intro to "R," longitudinal Mplus, genetic association
Modern Methods in Biostat & Epi (Italy)
6/9-21 (6/8 pre-session with STATA)Epidemiology,
biostatistics, regression, survival
analysis, meta-analysis and other courses (many courses focus on Stata program)
Muthen Mplus Courses

Short courses through
the year at various
locations
National Centre for Research Methods (UK)

Ongoing courses
(including brief ones)
Oslo (Norway) Summer School in Comparative Soc. Science Studies

Mostly Political
Science courses,
but also specialized
methods courses
PLS School
Brief workshops all yearTopics focusing on
Partial Least Squares
(courses mainly in
Germany)
St. Petersburg, Russia (Lab. for Comparative Social Res.)
6/29-712Categorical Data Analysis
Soc. for Imprecise Probability: Theories and Applications (SIPTA), Montpellier, France
7/21-25Various topics in the study of imprecise probability
U Amsterdam (Netherlands)
8/11-15Bayesian Modeling
for Cognitive Science
U Calabria, Italy (formerly known as SDIPA)
7/28-8/1
9/8-12
Experimental Res
SEM (Advanced)
U Edinburgh, Scotland

SEM
United Kingdom "Figure It Out"
Brief courses year-round, including summer
U Pompeu Fabra (Barcelona)


U St. Gallen (Switzerland; 
Summer School in Empirical Res. Methods)

Main sessions 6/2-6 & 6/10-13Numerous topics including SEM, regression, time-series, Bayesian, and qualitative
U Ulster (N. Ireland UK) Lifelong Learning 


Utrecht Univ. Netherlands
Mainly 1- & 2-week courses in July & AugustNumerous courses in statistics, methods, and substantive areas (e.g., education, criminology)
XL Stat 

Brief courses in
Paris & London




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

OTHER LOCATIONS
Location (link)DeadlineSessionsTopics
U of MelbourneBayesian and frequentist path analysis and SEM
National University of Singapore (IPSA)4/30 (early bird)

[Financial aid grants for students; due 5/31]
6/30-7/11Courses include experimental and survey methods, and regression







Sunday, August 18, 2013

Welcome to QM I for Fall 2013, 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. In this class, you'll learn what I mean by such phrases as "Paula Abdul significance" and "t for two," and how the statistical concept of "degrees of freedom" is nicely illustrated by one of the games on The Price is Right.

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*

Sampling*

Types of Measures*

Visual depictions of a data distribution (examples):
  • Histograms (overview; determining interval/bin widths; SPSS instructions here and here)
  • 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

t-tests

Chi-square

Non-parametric statistics

Statistical power

Confidence intervals

Tuesday, November 23, 2010

Today, we'll be covering confidence intervals (CI). We've alluded to the basic idea in terms of margin-of-error in a political poll, but today, we'll have a more formal treatment of CI's. Many researchers feel CI's are the best way to present results, rather than null-hypothesis significance testing. CI's have taken hold in many fields, but are only slowly catching on in the social sciences. Here are links to my previous postings on the topic.

Primary lecture notes on confidence intervals

General formula for confidence intervals and a song

Also, here's a potentially useful article:

Kalinowski, P., & Fidler, F. (2010). Interpreting ‘significance’: The difference between statistical and practical importance. Newborn and Infant Nursing Review, 10, 50-54.

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...