Sentences with phrase «use of all predictor variables»

Not exact matches

The use of dichotomous variables did not alter significantly the strength or direction of the associated predictors.

She did, «finding that «beta weights» are the coefficients of the «predictors» in a regression equation used to find statistical correlations between variables.

However, there was not a strong multicollinearity between velocity and our other predictor variables (tolerance values for velocity: amphibians = 0.51, mammals = 0.49, birds = 0.52, values differ due to the use of different predictor variable subsets).

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 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.

When I apply full standardization to the predictor variables in the all - predictors - simultaneously case, the excess over one of the Prediction ratio halves, to 7.0 %, using 7 PLS components.

I tested use of the OLR seasonal cycle over the 30S — 30N latitude zone only, thereby reducing the number of predictor variables to 936 — still a large number, but under 4 % of the 23,976 predictor variables used in BC17.

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

In 2003, Michaels and John Christy were among the coauthors of a «Test for harmful collinearity among predictor variables used in modeling global temperature.»

The use of only these three sea ice variables is partly a result of these variables being the best predictors towards the end of the melt season.

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.

An intent - to - treat analysis was conducted using univariate analysis of covariance for continuous variables and multivariate analysis of covariance for continuous variables in which the predictor variable comprised multiple scales.

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.

We undertook multiple imputation (MI) of predictor variables to assess the sensitivity of results to missing data using the chained regression method of MI to generate five imputed data sets.

SLA - level predictor variables will include: accessibility (ARIA +), 33 socioeconomic status (using Socio Economic Status for Areas (SEIFA) indexes, four indexes that summarise different aspects of the socioeconomic conditions of people living in an area based upon sets of social and economic information from the Australian Census35); full - time equivalent GPs; medical workers, nurses, pharmacists, Aboriginal health workers and community services workers per 10 000 population; rates of unemployment and labour force participation.

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.

Furthermore, it investigated the predictability of dependent variable (LA) using all independent and predictor variables (RO, PWB, and SE).

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

Besides, results of regression analysis indicated that among the predictor variables, only impulsivity can predict the amount of mobile phone use.

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.

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

To test this potential indirect effect, we used a non-parametric Monte Carlo simulation method, in which the indirect effect obtained from the a (the link between the predictor variable and the indirect effect variable) and b (the link between the indirect effect variable and the dependent variable, controlling for the remaining predictors) paths in a series of regression analyses is simulated k number of times using the slopes and standard errors obtained from the data (we used k = 50,000).

Other variables (maternal parity, housing stability, hospitalization, perceived health status, employment, use of the Women, Infants, and Children Supplemental Nutrition Program, and cigarette smoking; whether the mother was living with a partner; and infant gestational age, birth weight, need for transfer to an intensive care nursery, health insurance, special needs, health status as perceived by the mother, and age at the time of the survey) were included if the adjusted odds ratio differed from the crude odds ratio by at least 10 %, which is a well - accepted method of confounder selection when the decision of whether to adjust is unclear.42, 43 Any variable associated with both the predictor (depression) and the outcome (infant health services use, parenting practices, or injury - prevention measures) at P <.25, as suggested by Mickey and Greenland, 42 was also included.

Similarly, provided that the within - group process variable significantly predicted retention, post hoc decomposition analyses using single - predictor linear regression models were conducted to ascertain which aspects of within - group process significantly predicted retention.

The same predictors were used in a different model, with the Disgust measure of FaceReader Software as the outcome variable.

We used multi-level modeling or longitudinal growth curve modeling to examine the relations of predictor variables to metabolic control (Singer & Willett, 2003).

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.

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).