Types of ISAT Studies
Professional Development Studies
This series of studies investigated the impact of the OMS-provided professional development (PD) for teachers. OMS-provided PD was specific to the instructional materials endorsed by the CMSI. The Office of Mathematics and Science provided detailed data pertaining to teacher participation in the PD from the beginning of the initiative through 2007-2008. The data included teacher identification variables, number of PD courses for which a teacher registered, the number of days and hours attendance was required by the course, the actual number of days and hours attended by each teacher registering for the course.
The objective of the PD analyses was to determine if a link exists between participation in OMS PD and student performance on the ISAT. The studies excluded PD aimed at K-2 teachers because historically these grades have not participated in ISAT testing. PD aimed at principals was excluded because principals had no direct teaching impact on students.
Each strand of professional development sessions (referred to here as a “course”) was classified based on the subject (math or science); and grade (3-5 or 6-8). In addition, the study looked at two “attendance” variables cite and examined school-level changes in average ISAT scores between two years. cite
These analyses are limited in a number of respects. Foremost among these are the relatively brief time period covered (two school years) and the preliminary nature of the investigation.
The analyses employed the standard "stepwise" selection process included as part of the SPSS 14.0 OLS regression procedure. Stepwise selection is a combination of forward and backward procedures. This involves a four-step process (Brace, N., R. Kemp and R. Snelgar (2006) SPSS for Psychologists: A Guide to Data Analysis Using SPSS for Windows.):
Step 1 - The first predictor variable is selected in the same way as in forward selection. If the probability associated with the test of significance is less than or equal to the default .05, the predictor variable with the largest correlation with the criterion variable enters the equation first.
Step 2 - The second variable is selected based on the highest partial correlation. If it can pass the entry requirement (PIN=.05), it also enters the equation.
Step 3 - From this point, stepwise selection differs from forward selection: the variables already in the equation are examined for removal according to the removal criterion (POUT=.10) as in backward elimination.
Step 4 - Variables not in the equation are examined for entry. Variable selection ends when no more variables meet entry and removal criteria.