Value - added
models control statistically for student background and previously demonstrated student ability.
Not exact matches
Many psychological factors could have confounded the results — differential sensitivity to gains and losses, for example — but Cavanagh and Frank
controlled for those with the help of a sophisticated computer
model that accounts for and
statistically disentangles the relationship of bias and theta from those other influences.
The performance differences, however, while
statistically significant, are much smaller than those found in the NAEP analysis, which supports the view that
control variables used in the NAEP
models are biased against private schools.
Estimates from regressions with detailed
controls, nearest - neighbor
models, and propensity score
models all indicate large, positive, and
statistically significant relationships between computer ownership and earnings and employment, in sharp contrast to the null effects of our experiment.
In Hibel, Farkas and Morgan (2010), the «underrepresentation» of blacks in special education becomes
statistically significant only once the test score
controls are included, going from
Model 2 to
Model 3 in Table 5.
A Value - Added
Model (VAM) is a multivariate (multiple variable) student growth model that attempts to account or statistically control for all potential student, teacher, school, district, and external influences on outcome measures (i.e., growth in student achievement over t
Model (VAM) is a multivariate (multiple variable) student growth
model that attempts to account or statistically control for all potential student, teacher, school, district, and external influences on outcome measures (i.e., growth in student achievement over t
model that attempts to account or
statistically control for all potential student, teacher, school, district, and external influences on outcome measures (i.e., growth in student achievement over time).
The third approach, by Gavin Schmidt and colleagues,
statistically controlled for variables that are known to affect
model output.
In a process called data homogenization climate scientists adjust quality
controlled raw temperature data to create a more steeply rising average temperature wherever their
model suggests the weather behaved «outside
statistically unexpectations».
This interpretation is strengthened by the observation that the associations among television and children's consumption of fruits, vegetables, and juices; all meats; and pizza, salty snacks, and soda remained
statistically significant in the full regression
models, where the effects of socioeconomic and other confounding factors were
controlled.
Because parental depression can modify the findings, we
statistically controlled for the patient's baseline BDI as well as its change over the corresponding time period in the
models.
In 1968, when we only
control for age and gender in
model 1, we find no
statistically significant difference in low educational attainment between respondents who grew up with both their parents and respondents from a dissolved family background.
Treatment status (denoted by group) was
controlled in the
model but was not
statistically significant
Furthermore, multilevel
modeling enables the influence of person - specific factors on outcome variables to be
statistically controlled (DeLucia & Pitts, 2006).