Sentences with phrase «predictor variables on»

Results from Table 2 assessing the impact of predictor variables on literacy outcomes show that SES and ECE attendance strongly predicated letter naming, p < 0.05; age, SES and ECE predicted fine motor skills, p < 0.01; None of the predictor variables significantly predicted receptive language while ECE and age

Not exact matches

A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on response or dependent variables.

In place of reporting probit coefficients, we report the marginal effect of the variable on the award probability, which is the change in the award probability due to each predictor separately, with other variables evaluated at their mean values.

Instead, we used the ratings assigned to teachers on a joint (effective teaching and culturally responsive pedagogy) teacher accomplishment scale to classify teachers into three levels of accomplishment (most, moderately, and least); these levels were used as predictor variables to explain variations in the instructional practices used by teachers (Table 15).

Predictor and predictand can be the same variables on different spatial scales (e.g., Bürger, 1997; Wilks, 1999b; Widmann and Bretherton, 2000), but more commonly are different.

Statistical downscaling is based on relationships linking large - scale atmospheric variables from either GCMs or RCMs (predictors) and local / regional climate variables (predictands) using observations.

The region of ice concentration > 60 % on August 5 from MyOcean (TOPAZ4 model) was used as a predictor variable, and a linear regression was performed of September NSIDC extent vs. > 60 % concentration area on August 5.

These are all cell mean values on a grid with 37 latitudes and 72 longitudes, giving nine predictor fields each with 2664 values for three aspects (climatology, seasonal cycle and monthly variability) for each of three variables (OLR, OSR and N).

The five groups of variables on the left have been shown by Rogers and others researchers to be valid and reliable predictors of the rate of adoption of an innovation.

Several analyses focused on missing data.36 To explore missing data patterns, we coded loss to follow - up as a binary variable and tested baseline variables as predictors using a stepwise logistic regression.

This study focuses on one risk factor (daily hassles) and one resistance factor (social support) as predictors of adjustment in children with PRDs, with demographics and disease severity as control variables.

In Step 1, we conducted one - way analysis of variance (ANOVA) to determine whether there were site differences on the predictor and criterion variables.

Discrete - time survival analysis, with person - year the unit of analysis and a logistic link function, was used to examine associations of temporally primary (based on retrospective age - at - onset reports) mental disorders and subsequent first onset of suicidality.29 Time was modeled as a separate dummy predictor variable for each year of life up to age at interview or age at onset of the outcome, whichever came first.

When data on covariates had been collected at both time points (eg, SES or household adults), we used covariates assessed at 9 months for the 9 - month ITSC predictor variable, and covariates assessed at 2 years for the combined 9 - month + 2 - year ITSC variable.

To put the effect sizes for the hypothesized associations on wave 6 reckless driving into perspective, we re-ran the final model using logistic regressions (for the connections between the wave 6 indicators and the wave 6 latent variables) to obtain odds ratios (OR) for the indirect effects of wave 1 predictors on the individual wave 6 reckless driving items.

Baseline drinking status (ever vs never tried alcohol) did not predict attrition, but to account for attrition bias related to other variables, estimation was carried out after multiple imputation using the standard missing at random assumption (ie, missing data are assumed missing at random conditional on observed predictors included in the model).27 The imputation model included all the predictors in the alcohol models plus a number of auxiliary variables that were not of direct theoretical interest but were nonetheless predictive of missingness so as to improve the quality of the imputations and make the missing at random assumption more plausible.28

The analyses also included age, race / ethnicity (three binary variables for Black, Hispanic and other ethnicity, coded with Whites as the reference group), gender, household income and parental education, media - viewing habits — hours watching television on a school day and how often the participant viewed movies together with his / her parents — and receptivity to alcohol marketing (based on whether or not the adolescent owned alcohol - branded merchandise at waves 2 — 4).31 Family predictors included perceived inhome availability of alcohol, subject - reported parental alcohol use (assessed at the 16 M survey and assumed to be invariant) and perceptions of authoritative parenting (α = 0.80).32 Other covariates included school performance, extracurricular participation, number of friends who used alcohol, weekly spending money, sensation seeking (4 - wave Cronbach's α range = 0.57 — 0.62) 33 and rebelliousness (0.71 — 0.76).34 All survey items are listed in table S1.

Additional Level 2 predictors (sex, race, age, length of time on ESRD treatment, length of time married, number of own health conditions; last four variables all grand mean centered) were assessed.

Men's dyadic adjustment, which was a significant predictor of men's personal commitment in the regression of men's variables on men's personal commitment, was no longer a significant predictor.

The relations between independent predictor variables (measures of immunological and psychological function at entry to the trial, age of onset, and duration of illness) and dependent dichotomous outcome variables (self rated global outcome; presence or absence of caseness on the general health questionnaire at follow up; reduced or normal delayed responses to hypersensitivity skin test) were examined in separate logistic regression analyses.

Generally, higher levels of problems on the predictor variables were usually associated with greater improvement in treatment.

Across all of the significant predictors, however, families and children displaying fewer problems on the predictor variables at pretreatment had fewer problems at posttreatment and follow - up.

The second function showed external and internal mismatch, capitalized - on - transitions, and gradual separation contributed most to the synthetic predictor; being internally and externally mismatched were negatively related to the other two variables.

We also report whether there were significant indirect effects on 18 - month outcomes of any demographic baseline predictors mediated through the baseline latent variables.

On one hand, Okagaki and Frensch (1998) found that when SES and other parental involvement variables were controlled, a measure of parental expectations was a significant predictor of fourth and fifth graders» grades for European American and Asian American students.

Finally, for ease of interpretation, we opted for the ADOS Module classification as a measure of functional language in a sample where the majority of children had basal scores on formal language tests; this precluded a more fine - grained analysis of language impairment as a predictor variable.

The second example of post-hoc probing involves a two - way interaction of two continuous variables and is based on an analysis of observational data (as predictors) and teacher - report grades (as an outcome).

Due to the non-significant associations between child internalizing problems and the parent co-regulation variables, the following hierarchical regression analysis focused on predictors of child externalizing problems.

Parent report is often used instead of children's self - report, but relying on one informant (e.g., parent report) for outcome and predictor variables can lead to overestimates of associations because of common method variance (Lindell and Whitney 2001; Richardson et al. 2009).