Professional development to differentiate kindergarten Tier 1 instruction: Can already effective teachers improve student outcomes by differentiating Tier 1 instruction?

Student Behavior Ratings and Response to Tier 1 Reading Intervention: Which Students Do Not Benefit?

Core reading instruction and interventions have differential effects based on student characteristics such as cognitive ability and pre-intervention skill level. Evidence for differential effect based on affective characteristics is scant and ambiguous; however, students with problem behavior are more often non-responsive to core reading instruction and intensive reading interventions. In this study, we estimated the range of students' behavior ratings in which a core reading instruction intervention was effective using a data set including 3,024 students in K-3. Data came from seven independent studies evaluating the Individualized Student Instruction (ISI) Tier 1 reading intervention and were pooled using integrative data analysis. We estimated Johnson-Neyman intervals of student behavior ratings that showed a treatment effect both at the within and between classroom level. ISI was effective in improving reading scores (b=0.51, p=.020, d = 0.08). However, students with very low or very high behavior ratings did not benefit from the approaches (range of behavior rating factor scores: -0.95 - 2.87). At the classroom level, students in classrooms with a higher average of problem behaviors did not benefit from ISI (average classroom behavior rating factor score: 0.05 - 4.25). Results suggest differentiating instruction alone is not enough for students with behavior problems to grow in reading ability.

Assessing Measurement Invariance Across Multiple Groups: When Is Fit Good Enough?

Complex research questions often need large samples to obtain accurate estimates of parameters and adequate power. Combining extant data sets into a large, pooled data set is one way this can be accomplished without expending resources. Measurement invariance (MI) modeling is an established approach to ensure participant scores are on the same scale. There are two major problems when combining independent data sets through MI. First, sample sizes will often be large leading to small differences becoming noninvariant. Second, not all data sets may include the same combination of measures. In this article, we present a method that can deal with both these problems and is user friendly. It is a combination of generating random normal deviates for variables missing completely in combination with assessing model fit using the root mean square error of approximation good enough principle, based on the hypothesis that the difference between groups is not zero but small. We demonstrate the method by examining MI across eight independent data sets and compare the MI decisions of the traditional and good enough approach. Our results show the approach has potential in combining educational data.