Sentences with phrase «as the predictor variables»

Andrew M. Greeley, «Religious Imagery as a Predictor Variable in the General Social Survey,» paper presented at a plenary session of the Society for the Scientific Study of Religion, Chicago, 1984.

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).

The unadjusted models only included prior academic achievement as predictor variables, and are shown for comparison purposes only.

Because Maternal behavior scores were significantly positively correlated across weeks 1, 2, and 3 (36), we use week 2 Maternal behavior scores in all analyses as our predictor variable.

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.

List of the 42 occupied weather stations used as predictor variables, and four automatic weather stations (AWS) in West Antarctica.

In the final multivariate model which included two or more adversities as a predictor variable, sexual abuse (OR 9.3, p < 0.001), childhood physical abuse (OR 2.2, p = 0.003) and parental divorce (OR 3.1, p < 0.001) retained significant associations with lifetime suicide attempts in the total sample.

Second, we sought to determine whether the frequency of S allele carriers predicts cultural individualism and collectivism by conducting a multiple regression analysis with individualism — collectivism as the criterion variable and frequency of S allele carriers, as well as four other economic and health factors previously associated with individualism — collectivism including GDP per capita, inequity in the distribution of wealth (Gini index) as well as historical and contemporary pathogen prevalence as predictor variables (Fincher et al. 2008).

A parallel series of analyses was conducted using maternal reports of parenting style as the predictor variables.

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 associations between the level of maternal relationship satisfaction and infectious disease in the group of < 6 - month - old infants were first tested by performing separate bivariate logistic regression analyses for each of the eight infectious diseases as the dependent variable, using the level of relationship satisfaction as the predictor variable.

In the present study, we assessed dimensions of parenting practices at age 2 as a predictor variables.

Not exact matches

Multivariate analyses were performed with logistic regression for outcome variables with paternal depression and other covariates as predictors.

When all variables were considered, disordered eating at age 24 was a predictor of lower psychological wellbeing among both women and men as well as a lower self - evaluation of health among men ten years later.

If models must compete against one another, we suggest comparing the model sets with and without each candidate predictor variable, as we did when calculating the summed Akaike weights for each variable (2).

To account for demographic differences that might impact social network structure, our model also included binary predictor variables indicating whether subjects in each dyad were of the same or different nationalities, ethnicities, and genders, as well as a variable indicating the age difference between members of each dyad.

Seven environmental variables, which were previously identified as potential predictors for podoconiosis in Ethiopia (Deribe et al., 2015b), were used to model podoconiosis prevalence.

Let denote outcome h for classroom j in school i. Let denote a vector of predictor variables such as class size, years of teacher experience, and an average of test scores from a previous year for members of the classroom.

This analysis allowed us to control more precisely for demographic variables by covarying sex, age, and years of experience, while accounting for learning style as a predictor of attrition.

However, results indicated that of these variables, only learning style — specifically, being classified as ST or not — was a significant predictor of teacher attrition (p <.01).

To compare the predictive strength of maternal style and young adult test performance, we built a single model that incorporated both classes of variables as predictors.

We built a generalized estimating equation (GEE) general linear model (GLM) with outcome as the dependent variable; time in the nursing box, licking / grooming per puppy, vertical nursing per puppy, and ventral nursing per puppy were entered as predictors with breed, maternal parity, sex of puppy, and age at return entered as covariates.

After controlling for all the variables, police officer and age remained significant predictors for both groups, suggesting that police officers and younger people use SC more frequently as a coping strategy.

We include as potential confounders any variable that has been shown to be associated with bullying and has also been shown to be associated with any of the 3 main predictors.

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.

Predictors of Client Engagement and Attrition in Home - Based Child Maltreatment Prevention Services Damashek, Doughty, Ware, & Silovsky (2010) Child Maltreatment, 16 (1) View Abstract Examines the relative influence of provider, program, and individual factors from the Integrated Theory of Parent Involvement as well as maternal and family demographic and risk variables in predicting service enrollment and completion in a home - based child maltreatment prevention service (SafeCare +) and a standard community care program (Services as Usual).

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.

To assess the potential effect of missing data (ie, ignorable vs informative missing data), a pattern - mixture analysis was implemented using 2 - tailed tests.51 We defined patterns using a binary completer status variable, which was entered as a predictor in the RRM and MMANOVA.

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.

Predictors of outcome, such as comorbidity, family variables, therapist characteristics, and length of treatment also need further exploration.

We individually modeled the 9 - month ITSC variable and combined 9 - month + 2 - year ITSC 4 - category variable as categorical predictors.

Information was collected in the surveys to date the transitions in time - varying variables (eg, respondent's age at birth of siblings and at parental death or divorce), allowing us to redefine these variables for each year of the respondent's life as time - varying predictors of onset of suicidality.

explore predictors of anxiety, depression and unmet needs, including demographic and disease variables (as previously established in English - speaking cancer patients) and acculturation

Sex and relationship variables as predictors of sexual attraction in cross-sex platonic friendships between young heterosexual adults.

This study compared movie alcohol and alcohol marketing exposures with family factors and other variables as predictors of alcohol use onset separately from transition to binge drinking.

The three character strengths were set as the predictors, and psychological stress and symptoms as dependent variables in each regression equation.

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.

Personality: While individual differences have been accounted for previously in the DRIVE model by including coping style and attributional style variables, personality variables represent a significant omission in this area, particularly when considering subjective well - being outcomes where personality has been cited as potentially the most important predictor (Diener et al., 2003).

Maternal behavior was assessed as a statistical control variable; however, the study concluded that «Parenting was a stronger and more consistent predictor of children's development than early child care experience.»

The results of logistic and multiple regression analyses to evaluate trial entry variables as predictors of outcome are shown in tables III and IV respectively.

Stepwise logistic and multiple regression analyses gave the same outcome variable predictors as the one step method: global outcome rating (disease conviction, P = 0.04; odds ratio 0.65, 95 % confidence interval 0.43 to 0.65); general health questionnaire score 5 or more (affective inhibition P = 0.007; 1.46, 1.1 to 1.9); delayed type hypersensitivity skin response (delayed hypersensitivity P = 0.005; 1.55, 1.35 to 1.82) and Karnofsky score (disease conviction, P = 0.003).

In step 1 of all regression analyses, the friendship variable concerned was entered as predictor.

Variables from within these domains were chosen as potential predictor and moderators of treatment response.

For three other variables, life stress, difficult child temperament, and parent — child dysfunction as predictors of the CBCL externalizing scale, lower levels of pretreatment problems were associated with greater treatment gains.

We followed Kraemer et al. (2002) in describing a predictor as a variable that is present at the time intervention started and is associated with a response to treatment, but that does not show a differential response to type of treatment.

As marriage length was highly correlated with age (husbands: r =.51, p <.001; wives: r =.57, p <.001) and unrelated to perceptions of collaboration and well - being when rival predictors were considered, we did not include this variable in our analyses.

This multilevel AR model enables researchers to estimate the average inertia in the population and to use observed person - level variables as predictors for the inertias, to see which person characteristics are related to regulatory weakness.

Importantly, using the multilevel TAR model, researchers can use person - level variables as predictors both for the inertias, representing the state - dependent regulatory weakness, and for the threshold representing a person's equilibrium.

Teixeira, P.J., Going, S.B. et al. (2006) Exercise Motivation, Eating, and Body Image Variables as Predictors of Weight Control.