Sentences with phrase «regression model controlling»
Regression models controlled for several child characteristics that could also affect the likelihood of being suspended or expelled.
https://www.americanprogress.org/ Not exact matches
Comparisons between intervention and control groups were tested with the use of ANCOVA regression models.
As described in the main text, ordered logistic regression analyses were carried out for each brain region in which social network distances were modeled as a function of local neural response similarities and dyadic dissimilarities in control variables (gender, ethnicity, nationality, age, and handedness).
To investigate to what extent these effects can be controlled for, we used a linear regression model that allows incorporating additional covariates.
Multiple linear regression models (in PROC GLM) were used to control for potential confounding factors.
A logistic regression model was fit to evaluate the effects of each sociodemographic variable level on odds of screening positive for depression controlling for each of the other sociodemographic variables.
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.
Again, this is a difference that could have been produced by chance and is controlled in the regression models.
We conducted a regression of the Principal Instructional Leadership measure on the principals «responses to items in the District Focus on Instruction scale, including building characteristics (size and level), student characteristics (% minority and % FRP) as control variables in the model.
The only reason I mentioned other factors is because current work running Fama - MacBeth regressions control using multi-factor models (at least) instead of just CAPM.
A nested negative binomial regression model was created with intervention or control as the main effect, and type of pet (Cat or Dog) and age of pet (under two years, 2 - 7 years, or over 7 years) as covariates.
A nested logistic regression model was created with group (intervention or control) as the main effect, and type of pet (Cat or Dog) and age of pet (under two years, 2 - 7 years, or over 7 years) as the covariates.
Results from the negative binomial regression model demonstrated that more abnormal sites were identified in the intervention group than the control group.
Second, a case - control setup was used to compare differences between the most severe tail chasers (TCindex5 — 12) and the controls (TCindex = 0) using a generalized linear model with a binomial distribution (logistic regression).
There is a sizeable and significant role of education in predicting knowledge on the index even when controlling for gender, age, and race and ethnicity in a linear regression model.
Poisson regression models that control for covariates compare birth defect prevalence rates associated with maternal residence in county mining type: mountaintop mining areas, other mining areas, or non-mining areas.
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Table 2 presents the results of the logistic regression model for all children, without controlling for early bullying.
On average, single - parent families had only half the income of two - parent families, and this difference accounted for about half the gap between the two sets of children in high school dropout and nonmarital teen birth rates (in regression models that also controlled for race, sex, mother's and father's education, number of siblings, and residence).31
The regression models were then expanded to test the independence of the effects of adverse childhood experiences while controlling for established predictors of age - related - disease risks.
Regression analyses confirmed that the income - to - needs ratio was significantly associated with caregivers» education (path A1; ranges across all regions: P <.001 in all models), predicted caregiving support / hostility assessed 1 year after baseline controlling for caregivers» education (path A2, P <.001), and predicted children's experience of stressful life events between baseline and time of scan when covarying for caregivers» education and supportive / hostile parenting (path A3, P <.001 in all models).
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.
Using regression models we examine associations between persistent income poverty, family transitions, and children's cognitive ability, controlling for family demographics and housing conditions, as well as child characteristics.
Logistic regression models were used for controlling eight confounding variables such as maternal age, maternal education, employment status, parity, maternal BMI, hypertension, diabetes and medically assisted conception.
Two separate moderated regression models were tested for each indicator of quality of life using the SPSS 17.0 statistical software package; one model tested the moderating role of marital quality in the effects of self - reported (subjective) vision controlling for visual acuity (objective vision) and the second tested the same in the effects of objective vision controlling for subjective vision.
As controls in regression models predicting abuse, none of these factors individually explained the difference in partner abuse between cohabitors and daters.
Control variables in the multilevel modeling and multiple - linear regression analyses included gender, race, and pretest scores on the outcome being predicted.
All three human capital factors were also positively associated with income (educational attainment: β = 0.39, P < 0.001; cognitive ability: β = 0.35, P < 0.001; self - control β = 0.38, P < 0.001); however, multivariate regression models revealed that all three human capital factors were associated with creditworthiness and heart age independent of income (Tables S1 — S4).
An exploratory logistic regression model was run to investigate the factors independently associated with children being in the lowest CSBS quartile (with the least well - developed communication skills), once other factors were controlled for at the same time.
The results of the regression analyses were virtually identical when controlling for the effect of intelligence on the model variables.
To address the main hypotheses of the study, we examined the effects of the three parenting practices — support, structure, and behavioral control — with the above factors controlled in the third model of the regression analyses (see Tables III and IV).
A multiple regression model was constructed to identify parental factors that predicted adherence and glycemic control while controlling for confounders (patient age, sex, and treatment method).
Because bivariate analyses indicated that baseline levels of social support were highly correlated to levels at follow - up, in examining program effects on social support we controlled for their baseline levels by entering them first into the regression models.
Variables were entered into the regression model hierarchically as shown in Table II with control variables entered first, 12 - month temperament, 36 - month externalizing or internalizing behavior, current family conflict, and finally, the interaction of conflict and temperament.
Taking into consideration the fact that control variables identified do not have a significant relationship to the dependent variable and those variables used to compute our dependent variable, control variables were not entered into any regression models.
Anxious vs. control group membership as a predictor of the frequency of parent - dependent negative life events and chronic adversities from regression models
The authors estimated two linear regression models that predicted early delinquency: one with individual and family controls and a second with the addition of parenting stress and school belonging.
The results for pubertal status and age are strikingly similar, indicating that after controlling for the effect of all the other variables in the regression model, the impact of life events on depression is significantly greater in the pubertal girls (sex × pubertal status [age] × life events interaction).
The above findings remained the same when additionally controlling for monthly household income and sleep condition during the EEG recording in the regression models.
Two logistic regression models were designed to 1) determine significant early (6 — 12 months) predictors of sleep problems at 3 to 4 years, controlling for group membership, and 2) determine significant concurrent correlates of sleep problems at 3 to 4 years.
Regression models examined the independent relationships between three social isolation variables, taken from the SDQ Peer Relationship Problems, Pro-social Behaviour and Emotional Symptoms subscales, controlling for demographics.