
These WINKS statistics tutorials explain the use and interpretation of standard statistical analysis techniques for Medical, Pharmaceutical, Clinical Trials, Marketing or Scientific Research. The examples include howto instructions for WINKS SDA Version 6.0 Software. Download evaluation copy of WINKS. 
MantelHaenszel Test
This
is one in a series of tutorials using examples from WINKS SDA.
The MantelHaenszel method is often used particularly in metaanalysis) to pool the results from several 2 x 2 contingency tables. It is also useful for the analysis of two dichotomous variables while adjusting for a third variable to determine whether there is a relationship between the two variables controlling for levels of the third variable.
Appropriate applications of the MantelHaenszel procedure
Disease incidence: Casecontrol data for a disease are collected in several cities, forming a 2 x 2 table for each city. You could use a MantelHaenszel analysis to obtain a pooled estimate of the odds ratio across cities.
Pooling results from previous studies: Several published studies have analyzed the same categorical variables summarized in 2 x 2 tables. In metaanalysis, the information from the studies is pooled in order to provide more definitive findings than could be obtained from a single study. MantelHaenszel analysis can be used to pool this type of information. For more on metaanalysis see Hunt (1997) and Lipsey (2000).
Hypotheses tests used in MantelHaenszel analysis
The hypotheses tested in the MantelHaenszel test are
Ho: There is no relationship between the two variables of interest when controlling for a third variable
Ha: There is a relationship between the two variables of interest when controlling for a third variable
Design considerations for MantelHaenszel test
A MantelHaenszel analysis looks at several 2 x 2 tables from the same bivariate variables, each representing some strata or group, (e.g. information from different departments at a university, etc.) or from different results of similar analyses (as in a meta analysis). The test also assumes that the tables are independent (subjects or entities are in one and only one table).
Example MantelHaenszel analysis of Berkeley graduate admission data
A classic data set illustrating the use of the MantelHaenszel test is data collected at the University of California at Berkeley concerning gender patterns in graduate admissions. (Bickel and O’Connell, 1975). The crosstabulated data for acceptance (no or yes) vs gender is given in Table 5.11 for five separate departments along with row percentages showing the percentage of each gender who were admitted within each program. From this table it can be seen that while Department 1 seems to have higher admission rates than the other departments, the comparative acceptance rates for males and females is about the same within departments with there being a slight tendency for females to be admitted at a higher rate.
The MantelHaenszel test can be used to test the hypotheses
Ho: Controlling for (or within departments) there is no relationship between gender and acceptance
Ha: Controlling for (or within departments) there is a relationship between gender and acceptance
Berkley (Mantel_Haenszel) Data in WINKS format (BIAS.SDA)
Step 1: Open the file BIAS.SDA. (Note that this data set contains formats for Gender and Acceptance in order to place Gender and Acceptance category labels on the tables.). Select Analyze, Advanced Tabulation, MantelHaenszel (Data from counts).
Step 2: From the following dialog box, select the variables as indicated. Click Ok.
Step 3: The (partial) results follow beginning with a sample of the crosstabulations for individual departments:
Department 1:
FREQUENCY NO YES TOTAL

FEMALE 8 17 25

MALE 207 353 560

TOTAL 215 370 585
ChiSquare .254 p = 0.615
Odds Ratio = .803 95% C.I. = (.340, 1.892)
Department 2:
FREQUENCY NO YES TOTAL

FEMALE 391 202 593

MALE 205 120 325

TOTAL 596 322 918
ChiSquare = .754 p = 0.386
Odds Ratio = 1.133 95% C.I. = (.855, 1.502)
. . . etc . . .
Department 5:
FREQUENCY NO YES TOTAL

FEMALE 317 24 341

MALE 351 22 373

TOTAL 668 46 714
ChiSquare = .384 p= 0.536
Odds Ratio = .828 95% C.I. = (.455, 1.506)
The overall MantelHaenszel results are shown here:
The MantelHaenszel procedure is used to analyze multiple 2x2 tables adjusting for the factor represented by the multiple tables.
MantelHaenszel Statistic = 0.12 with 1 d.f. p = 0.723 (twosided)
MantelHaenszel Average Odds Ratio = 1.031
Appx. 95% C.I. for Odds Ratio = 0.87 to 1.221
MantelHaenszel results, summarized by p = 0.723 indicate that controlling for departments, there is no reason to conclude that there is a difference between male vs. female admission rates. As mentioned previously, the consistent pattern is that the admission rates for males and females are about the same for each department with perhaps a slight tendency for females to have a higher rate of admission. Cochran’s test is similar to the MantelHaenszel test but is not commonly reported. In this example, Cochran’s test gives results consistent with the MantelHaenszel test.
Reporting Results of a MantelHaenszel Analysis
Narrative for the methods section:
“Controlling for department, the relationship between gender and acceptance is examined using a MantelHaenszel analysis.”
Narrative for the results section:
“Adjusting or controlling for department, no significant difference was found between male and female acceptance rates, ChiSquare (1) = 0.125, p = 0.72, N=3593.”
Note: You can also enter MantelHaenszel data from the keyboard using the "Enter from keyboard option."
End of tutorial
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