Training Doctoral Students in Educational Statistics in the United States: A National Survey

Figures & data

Table 1. Universities Initially Contacted for this Study

1. University of Wisconsin ‐‐ Madison

2. University of Illinois

3. Ohio State University

4. Stanford University

5. University of Minnesota

6. Indiana University ‐‐ Bloomington

7. Michigan State University

8. Columbia ‐‐ Teachers College

9. University of Georgia

10. Pennsylvania State University

11. University of Maryland

12. University of Texas ‐‐ Austin

13. University of Michigan

14. Arizona State University

15. University of California ‐‐ Los Angeles

16. University of Washington

17. University of California ‐‐ Berkeley

18. University of Chicago

19. Harvard University

20. University of Virginia

21. Vanderbilt University ‐‐ Peabody

22. University of North Carolina

23. University of Florida

24. Florida State University

25. Syracuse University

26. University of Arizona

27. University of Nebraska

28. Virginia Polytechnic

29. SUNY-Buffalo

30. University of Missouri

31. University of Pittsburgh

Table 2.1. Demographics: Program Size

	Mean	Median	SD	N
Number of full-time tenured or tenure-track faculty in QM program	5.1	4	2.2	23
Number of full-time faculty who regularly teach statistics during the academic year	3.6	3	1.7	23
Number of lecturers and adjunct faculty members who regularly teach statistics in QM program	1.8	1	2.8	23
Approximate number of students admitted to QM program each year over past five years	7.2	4	8.8	22

Table 2.2. Location of Required Statistics Courses in QM Program (N = 22)

School/College of Education	100%
Mathematics/Statistics Department	46%
Psychology Department	9%
Other	5%

Table 3. Preparation of Quantitative Methods Doctoral Students (Percent of Universities Where Most or All QM Program Graduates Receive Training in Topic/Method, N = 23)

ANOVA
Covariance analysis	100%
Repeated measures designs	96%
Power/sample size calculations	96%
Mixed-effects models	91%
Random-effects models	87%
Nonorthogonal designs	83%
Thorough coverage of multiple comparison procedures	74%
Cell means models	57%
Complex designs (e.g., fractional factorial)	39%
Multiple Regression
Ordinary least squares estimation	100%
Weighted least squares estimation	70%
Nonlinear-in-the-predictors models	70%
Logistic regression	65%
Nonlinear-in-the-parameters models	39%
Traditional Multivariate Procedures
Canonical correlation	91%
MANOVA	91%
Discriminant analysis	91%
Factor analysis	87%
MANCOVA	83%
Principal components analysis	78%
Log-linear models	56%
Nonparametric Procedures
Kruskal-Wallis test	48%
Exact tests	44%
Jackknifing/Bootstrapping	39%
Repeated measures tests (e.g., Friedman, Cochran)	30%
Asymptotic relative efficiency	26%
Rank-transform tests	22%
Other Topics and Procedures
Matrix algebra	83%
Meta-analysis	74%
Structural equation models	74%
Multilevel models/Hierarchical linear models	35%
Times series models	4%

Table 4. Faculty Members' Perceptions of the Mathematical/Statistical Training and Computer Skills of Quantitative Methods Program Graduates

	Weak			Strong
	(1)	(2)	(3)	(4)	M	SD	N
1. Mathematical statistics training of most QM graduates	1	8	10	2	2.6	.74	21
	(5%)	(38%)	(48%)	(10%)
2. Computer skills: Standard data analysis programs (e.g., SAS, SPSS, SYSTAT)	0	0	2	21	3.9	.28	23
	(0%)	(0%)	(9%)	(91%)
3. Computer skills: Database management	1	6	14	2	2.7	.69	23
	(4%)	(26%)	(61%)	(9%)
4: Computer skills: Programming (e.g., FORTRAN, PASCAL, C)	7	5	9	2	2.3	1.01	23
	(30%)	(22%)	(39%)	(9%)

NOTE: For this question, the anchors were 4 = Strong, perhaps took multiple courses in mathematical statistics and probability theory, would probably be well acquainted with topics such as quadratic forms, Likelihood Ratio Test Principle, Gauss-Markoff Theorem, etc.; and 1 = Weak, little or no coursework in mathematical statistics or probability theory.

Table 5. Faculty Members' Perceptions of QM Program Graduates' Ability to Do Publishable Statistical Work (Percent of Faculty Selecting Each Option, N = 23)

Faculty who believe that recent graduates are better prepared than graduates from 5–6 years ago	44%
Faculty who believe that recent graduates are less well prepared than graduates from 5–6 years ago	0%
Faculty who believe that there is no difference between recent graduates and graduates from 5–6 years ago	56%

NOTE: “Recent graduates” were defined as students who graduated within the last two years.

Table 6. Faculty Members' Perceptions of Doctoral Students' Need for Additional Statistics Coursework for Quantitative Methods Program (Percent of Faculty Selecting Each Option)

Statistics Coursework	<50%	>50%	Can't Judge
1–2 additional (probably specialized) statistics courses (N = 21)	43%	48%	10%
3 or more additional (probably specialized) statistics courses (N = 20)	65%	20%	15%

NOTE: Data are in response to the following question: In hindsight, of the quantitative methods program doctoral students who graduated from your program in the last 5 years, what percent do you think could have profited from (the following additional coursework)?

Table 7. Coursework Requirements for Doctoral Students in Education Who Are Not in Quantitative Methods Programs (Percent of Faculty Selecting Each Option, N = 23)

Students in some programs can graduate without taking a statistics course	44%
Students in all programs are required to take at least one statistics course	39%
Students in all programs are required to take at least two statistics courses	13%
Don't know	4%

Table 8. Changes in Statistics Enrollment over the Past Five Years for Doctoral Students in Education Who Are Not in Quantitative Methods Programs (Percent of Faculty Selecting Each Option, N = 22)

Doctoral students have tended to take fewer statistics courses	27%
Doctoral students have tended to take more statistics courses	36%
Doctoral students have tended to take about the same number of statistics courses	27%
Don't know/No Response	9%

Table 9. Perception of Students' Competence to Critically Read and Interpret Research Articles Utilizing Specific Statistical Procedures for Doctoral Students Who Are Not in Quantitative Methods Programs (Percent of Faculty Selecting Each Option, N = 22)

	<50%		>50%
	N	(Pct)	N	(Pct)
ANOVA	8	(36%)	14	(63%)
OLS regression	13	(59%)	9	(41%)
Nonparametric rank tests	19	(86%)	3	(14%)
MANOVA	20	(91%)	1	(5%)
Log-linear models	21	(95%)	1	(5%)
Causal models	20	(91%)	1	(5%)

NOTE: Data are based on responses to the following question: Think about the doctoral students in your School/College of Education who are NOT in your quantitative methods program that you have had contact with over the past 5 years or so ‐‐ through dissertation committees, classes, consulting, and so forth. Of these non-quantitative students, what percent do you think are competent to critically read and interpret research articles that utilize the following procedures? (1) Less than half, (2) More than half, (3) Can't judge.

Table 10. Faculty Members' Perceptions of Graduates' Need for Additional Statistics Coursework for Doctoral Students Who Are Not in Quantitative Methods Programs (Percent of Faculty Selecting Each Option)

Statistics Coursework	<50%	>50%	Can't Judge
1–2 additional (probably specialized) statistics courses (N = 23)	52%	44%	4%
3 or more additional (probably specialized) statistics courses (N = 21)	48%	43%	10%

NOTE: Data are in response to the following question: Think about the doctoral students in your School/College of Education who are NOT in your quantitative methods program that you have had contact with over the past 5 years or so ‐‐ through dissertation committees, classes, consulting and so forth. Of these non-quantitative students, what percent do you think could have profited from (the following additional coursework)?