As can be seen in Table II, these effects were maintained in the final step
of the hierarchical regression, which included interaction terms.
Summary
of hierarchical regression analyses for children's birth status and children's sustained selective attention performance predicting children's problem behavior, as reported by mothers and teachers
Model 1
of each hierarchical regression analysis contained a block of demographic variables including parent and child gender, parent's childhood SES, age at parenthood, current family SES, and neighborhood risk.
A series
of hierarchical regression analyses were performed to test whether the influence of early infant behavioral characteristics on later parenting stress and temperament ratings would vary depending on factors in the infant — parent dyad.
Summary
of hierarchical regression analyses predicting rumors in the fall and spring of 6th grade
Summary
of hierarchical regression analyses testing peer - and teacher - reported popularity among boys as a mediator of the link between pubertal timing and rumors
The results
of the hierarchical regression analysis are presented in table 5.
Final results
of hierarchical regression analyses for time to complete and number of excess moves on Tower of London task.
Prior to conducting the main analysis
of hierarchical regression, the data were checked for outliers that might show undue influence in some analyses.
Result
of hierarchical regression analysis (dependent variable: process innovation performance).
Table 2 presents the results
of the hierarchical regression analyses.
In each regression equation SES was entered first, collective leadership second, and one of the teacher variables third.44 Results
of these hierarchical regressions, described in Table 1.1.5, indicate that only motivation explains a unique and significant proportion of variation in student achievement after controlling for student SES.
Not exact matches
Logistic
regression using a
hierarchical stepwise method indicated that none
of the demographic factors, which were entered first, were found to be predictive
of the infants» ability to achieve developmental milestones.
Specific statistical areas
of expertise include factor and cluster analysis, basic bivariate analyses, repeated measures analyses, linear and
hierarchical / mixed models, structural equation modeling, and nonparametric analyses including logistic
regression techniques.
In order to estimate the contribution
of student SES (calculated as the percentage
of students in a school eligible for free or reduced lunch) to relationships described in the path model between the three teacher variables and student achievement, we computed three
hierarchical regressions.
Three analyses were conducted with the cross-sectional data using teachers» WSS ratings
of student achievement and students» WJ - R standard scores: a) correlations comparing the students» standard scores on the various subtests
of the WJ - R and the WSS checklist and summary report ratings
of student achievement within the corresponding WSS domains, b) four - step
hierarchical regressions examining the different factors that accounted for the variance in students» spring WJ - R scores, and c) Receiver - Operating - Characteristic (ROC) curves, which make possible a determination
of whether a random pair
of average and below - average scores on the WJ - R would be ranked correctly in terms
of performance on the WSS.
Contrary to expectation, the presence
of painful symptoms in patients was not statistically significantly associated with partners» psychological distress in the
hierarchical regression analysis, despite 65.1 %
of partners having reported the presence
of painful symptoms in the patient.
In Step 3, we conducted
hierarchical multiple
regressions on each
of the adjustment variables.
To clarify the nature
of these interactions, we ran two additional
hierarchical multiple
regression analyses by entering the demographic / disease severity variables, followed by daily hassles, the specific social support source
of interest (classmate or teacher), and the relevant interaction between hassles and social support (classmate or teacher).
Summary
of hierarchical multiple
regression analyses (stepwise method) examining multivariate correlates
of SA
An examination
of collinearity was undertaken comparing changes in the standard errors and magnitude and sign (positive or negative)
of the bivariate analyses results with the standard bivariate
regression models for each sex and the full
hierarchical regression models.
Multivariable
hierarchical regression permits grouping
of related variables (as described earlier in the «Instruments» section), which are then added successively to the model.
Hierarchical regression analyses were computed to determine the impact
of predictor variables — age, gender, SES and ECE attendance.
Summary
of hierarchical multiple
regression analyses (stepwise method) examining multivariate correlates
of DSH
RESULTS:
Hierarchical regression analyses revealed that long - term success (at least 5 % weight reduction by the 1 - year follow - up) versus failure (dropping out or less weight reduction) was significantly predicted by the set
of psychosocial variables (family adversity, maternal depression, and attachment insecurity) when we controlled for familial obesity, preintervention overweight, age, and gender
of the index child and parental educational level.
In
hierarchical regression analysis
of SA, social support was present in models 2 and 3, but disappeared after adjusting for substance use and depressive symptoms in model 4 (table 3).
The author performed
hierarchical linear
regressions to examine the relationships between attachment dimensions (Security and Anxiety) and the provision
of instrumental and emotional care.
The results
of Pearson correlation analysis and
hierarchical regression analysis revealed a statistically significant rela - tionship between job and life satisfaction, even after controlling for demographic and socioeconomic variables.
Two
hierarchical multiple
regressions were calculated, both
of which included estimated METs per week, mean choice reaction time, age, and gender as predictors at the first level and the five NEO-FFI scores at the second - level.
Data analyzed using zero - order correlation and
hierarchical regression analysis showed positive correlations
of POS and job satisfaction with work performance, and also showed independent and joint positive associations
of POS and job satisfaction with OCB and each
of its four dimensions.
As shown in the results
of the Pearson's correlations and the
hierarchical regression analysis, social support had a significant negative association with PTSD symptoms, and this finding is consistent with other researches.9 36 51 52 The level
of PTSD symptoms was significantly and negatively correlated with the healthcare workers» scores for objective support and utilisation
of support.
Analysis involved correlations,
hierarchical multiple
regression and analysis
of variance.
RESULTS: The
hierarchical regression model for job stress explained 26.8 %
of... the variance among those with a low monthly income (β = − 0.151, p = 0.021), an irregular diet (β = 0.165, p = 0.014), and high daily work hours (β = 0.380, p = 0.000), showing that these respondents were more likely to report high job stress levels.
Hierarchical multiple
regression analyses indicated that commonly investigated psychosocial factors such as affectivity, coping, and social support moderated the relationship between perceived stress and one illness behavior (report
of illness without visits to the doctor).
Hierarchical regression analyses indicated that gender (females were less likely to be employed), IQ (lower IQ associated with unemployment), and transportation dependence accounted for 42 %
of the variance in employment.
RESULTS: The
hierarchical regression model for job stress explained 26.8 %
of
Independent sample t - test was used to compare the level
of self - esteem, family function score and social support score between the two groups with and without grandparenting experience; Pearson correlation was calculated to explore how levels
of self - esteem and family functions as well as perceived social support were related;
Hierarchical regression analysis was applied to examine the moderating effect
of social support on the relationship between family function and self - esteem.
Using the first year women's panel data
of Korea Women's Development Institute, a series
of analyses including
Hierarchical Multiple Regression were taken to examine the comparative influence of socio - demographic factors, factors of the interaction with spouse and factors of the interaction with family of origin, Findings of hierarchical multiple regression identified that the influence of interaction with spouse was high, and factors of interaction with family of origin had a significant influence on marital satisfaction even after controlling the influences of ot
Hierarchical Multiple
Regression were taken to examine the comparative influence of socio - demographic factors, factors of the interaction with spouse and factors of the interaction with family of origin, Findings of hierarchical multiple regression identified that the influence of interaction with spouse was high, and factors of interaction with family of origin had a significant influence on marital satisfaction even after controlling the influences of othe
Regression were taken to examine the comparative influence
of socio - demographic factors, factors
of the interaction with spouse and factors
of the interaction with family
of origin, Findings
of hierarchical multiple regression identified that the influence of interaction with spouse was high, and factors of interaction with family of origin had a significant influence on marital satisfaction even after controlling the influences of ot
hierarchical multiple
regression identified that the influence of interaction with spouse was high, and factors of interaction with family of origin had a significant influence on marital satisfaction even after controlling the influences of othe
regression identified that the influence
of interaction with spouse was high, and factors
of interaction with family
of origin had a significant influence on marital satisfaction even after controlling the influences
of other factors.
Means and Standard Deviations
of General Liking and Romantic Attraction as a Function
of Availability, Commitment, and Sociosexuality Predicting the Target s General Likability:
Hierarchical Regression Analyses Predicting Romantic Attraction toward a Target:
Hierarchical Regression Analyses.
She has technical expertise in a wide range
of statistical techniques used in the social sciences, including structural equation modeling, confirmatory factor analysis and MIMIC approaches to measurement, path modeling,
regression analysis (e.g., linear, logistic, Poisson), latent class analysis,
hierarchical linear models (including growth curve modeling), latent transition analysis, mixture modeling, item response theory, as well as more commonly used techniques drawing from classical test theory (e.g., reliability analysis through Cronbach's alpha, exploratory factor analysis, uni - and multivariate
regression, correlation, ANOVA, etc).
Multivariate
hierarchical logistic
regression was used to evaluate the determinants
of being in the optimal versus less optimal feeders group.
Drawing on three waves
of data collected from an ethnically diverse sample
of middle school girls (n = 912),
hierarchical multiple
regression analyses revealed that more advanced development at the start
of middle school predicted peer - and teacher - reported popularity as well as increased risk
of being targeted for rumors.
Table 6 gives the results
of the significant
hierarchical regression analyses.
Hierarchical regression analyses were conducted to examine the effects
of friendship quality on the adolescents» well - being as a function
of country and hearing status.
Hierarchical regression analyses were used to examine relations between contingent responsiveness and child compliance, after accounting for the quality
of parent directives and parent negativity.
Hierarchical regression analyses were conducted to examine the effect
of pc use with a friend, country, hearing status, school setting and age on the online, mixed and offline friendship quality.
In order to test the potential moderator effect between negative affectivity and effortful control on ODD - related problems, we conducted two separate multiple
hierarchical regression analyses, one for the parental and the other for the teacher rate
of ODD - related problems.
Therefore, given that only these four parameters were significantly associated with CU traits and ODD problems (teacher rate), we further conducted four separate multiple
hierarchical regression analyses, one for each
of these parameters, in order to examine the contributions
of CU traits, anxiety, ODD - related problems and their interactions on attentional processing
of emotional faces as indexed by these parameters.
In
hierarchical regression analyses with the various ENRICH factor scores as dependent variables and job satisfaction as the independent variable in the first block, the two SSQ factors in the second block, and the WOC factors in the third block, between 24 and 38 %
of the variance in seven
of the nine ENRICH factors (not significant model for «Family & Friends» and «Marriage & Children») could be explained by the variation in all the independent variables with varying weight
of the several independent variables (Table 3).
To test both questions,
hierarchical regression analyses were performed with demographics entered in block one
of each model, the mindfulness subscales entered into block two, and one conflict style entered as the criterion variable for each model.