Sentences with phrase «statistical difference in student»

Not exact matches

In order to separate student characteristics from aspects of segregated public schools, Kainz used a statistical technique called «propensity score matching,» which allows for comparison of reading growth in segregated and non-segregated schools, while also accounting for numerous differences in the students» backgroundIn order to separate student characteristics from aspects of segregated public schools, Kainz used a statistical technique called «propensity score matching,» which allows for comparison of reading growth in segregated and non-segregated schools, while also accounting for numerous differences in the students» backgroundin segregated and non-segregated schools, while also accounting for numerous differences in the students» backgroundin the students» backgrounds.

In addition, statistical techniques can control for the influence of differences in the background of students in each group or in the additional resources provided to each grouIn addition, statistical techniques can control for the influence of differences in the background of students in each group or in the additional resources provided to each grouin the background of students in each group or in the additional resources provided to each grouin each group or in the additional resources provided to each grouin the additional resources provided to each group.

Despite differences in statistical approach and in the selection of students to be included in the analysis, Barnard's findings are largely consistent with those we reported.

The most important characteristic included among our statistical controls is 8th - grade test score, which aims to capture differences in student ability and students» educational experiences prior to high school.

Students in magnet public schools have slightly higher scores than assigned public school students, although the difference does not approach statistical signiStudents in magnet public schools have slightly higher scores than assigned public school students, although the difference does not approach statistical signistudents, although the difference does not approach statistical significance.

Sophisticated statistical programs can help administrators draw vital inferences about the learning process, especially about the extent to which each teacher is providing «value - added» to students (after allowing for differences in student backgrounds and other influences on learning that teachers can't control).

These characteristics were used in statistical models to adjust for whatever differences remained between students who were offered and not offered vouchers.

The researchers also point out there were 1290 unique school and grade combinations in the study sample — an average of 40 students per combination — which meant it «lacked statistical power to find significant differences between treatment conditions or grade levels».

In the elementary grades 3 through 5, students of new Teach for America teachers gained an average of 5.8 percent of a standard deviation more on the TAAS reading exam than did students with other new teachers, a difference that fell just short of statistical significance (see Figure 2).

Finally, while the motivation of the entire study was to investigate the role and effect of different state policies, the only policies RAND's researchers actually built into their main statistical models were differences in per - pupil spending, student - teacher ratios, and other resource variables.

This is in part because there are many other influences on student gains other than individual teachers, and in part because teachers» value - added ratings are affected by differences in the students who are assigned to them, even when statistical models try to control for student demographic variables.

However, given the statistical controls employed and the consistency of their findings with other studies at different grade levels, one can conclude that the question as to whether effective teachers make a significant difference in student achievement has been answered.

From deploying AI to avoid paying parking tickets in the UK to using law students as a paralegal resource to the harnessing of natural language processing and statistical probability to recognise textual differences, all the innovations in this report confront and shake up the status quo.

This non-parametric statistical hypothesis test can be considered as an alternative to the paired Student's t - test and can be used to compare repeated measures on a single sample to assess differences in the population mean ranks.