Sentences with phrase «relationships measuring variables»

More in detail, Schönbrodt and Asendorpf (2010) examined the correlation between the behaviors for a virtual partner and real - life relationships measuring variables as interpersonal motives and relationship satisfaction.

Not exact matches

(R - square is a statistical measure that reveals how well a regression line — the line of best fit you see — explains the relationship between two variables.

It is also worth noting that, while these variables were measured with data from around 1960, the relationships between variables are not necessarily time - bound.

You could not directly measure sleep (via EEG / EMG signals), or manipulate any of these environmental variables to actually determine causation in any relationships you may find.

This enterprise led him deep into the relationship between economics, physiology, and longevity, where he analyzed variables such as the amount of food consumed by the average slave (or freeman) measured against the amount of work he produced.

Researchers measured the participants» body composition, aerobic fitness, and glycemic control, and assessed the relationships between pre-intervention variables and intervention - induced changes.

He has particular interests in (1) the use of ancient DNA methods to document changes in genetic variation through time and phylogenetic relationships of extinct or endangered organisms (especially of the recently extinct Hawaiian avifauna); (2) the use of highly variable genetic markers to measure genetic structure and relatedness, and to ascertain mating systems, in natural populations, and (3) the use of genetics to study the evolutionary interactions between hosts, vectors and infectious disease organisms (e.g., major projects on introduced avian malaria in native Hawaiian birds and invasive chytrid fungus in amphibians).

They also predict variables and relationships between variables that haven't yet been measured or analysed — that is just as valid a falsifiability criteria.

We report the results of these analyses below as correlation coefficients, a statistical measure that summarizes the strength of the relationship between two variables.

To investigate the relationship between school effectiveness and classroom instruction, we initially conducted a multivariate analysis of variance (MANOVA) with the school effectiveness rating serving as the independent variable and eight teacher variables serving as outcome measures (see Table 11).

One statistic that describes the year - to - year persistence in value - added estimates is the correlation coefficient, a measure of the linear relationship between two variables.

Project RED was designed to provide data for later analysis of the relationships between the 22 independent variables and the 11 education success measures.

Correlation The correlation coefficent measures the stregnth and direction of a linear relationship between two variables.

A model was developed to understand relationships between measured sound levels and variables such as climate, topography, human activity, time of day, and day of year.

As long as there is a real causative physical relationship known, and you have a good reason to believe you have accounted for all the other important variables, statistically - measured phenomenology is almost the only way we know anything for sure about the world.

Specifically, r2 measures the strength of a linear relationship between two variables when the linear fit is determined by regression.

They also predict variables and relationships between variables that haven't yet been measured or analysed — that is just as valid a falsifiability criteria.

So we can proceed and calculate the correlation coefficient, which measures the strength and direction of a linear relationship between two quantitative variables.

Specifically, the procedure was to a) obtain a sample of jobs having work values scores, b) regress these scores on variables that measured important characteristics of the jobs, and c) evaluate the degree of relationship between the predicted and actual scores.

Measures for which rates are well documented, such as lifetime prevalence of suicidality, were consistent with other available data.31, 32 Examination of discriminate and convergent validity in between variable analyses within the survey also showed predictable relationship patterns.

A palpable relationship exists between ACE scores and independent variables measuring clinical impairment and outcomes.

Only variables significantly related to TV - viewing time were considered to potentially confound the relationship between this outcome measure and either maternal depression or maternal obesity.

Both partners completed a self - report survey that included questions about their current loneliness levels, relationship quality measures, and a range of demographic and baseline variables (e.g., income, education, employment status, number of children, whether other family members lived with them, health, depression, etc.).

Health care communication investigators are beginning to emphasize that measuring how the patient perceives and engages in relationships might be as important as focusing primarily on provider communication variables, but that such patient variables are less frequently investigated (15).

These measures spanned a total of 50 variables describing maternal health and well - being; partner adjustment; family stability, relationship satisfaction, and family violence; family material circumstances and material well - being; and family susceptibility to stress and crisis.

The mediator variable relationship satisfaction was measured with one question at T2 asking the respondents to rate their degree of satisfaction in the relationship from 1 = very unsatisfied to 7 = completely satisfied.

Furthermore, they found that a composite measure involving intrinsic and environmental factors was associated with each step in their marital cascade model, indicating that a broader range of variables than relationship quality alone may be involved in cascades towards relationship dissolution.

To address this goal, we adopted a Confirmatory Factor Analysis (CFA) approach (see Figure 1) which allowed us to partition each measured (manifest) variable into three components: (1) variance that is common to all relationships, (2) variance that is unique to a specific relationship, and (3) measurement error.

In addition to measuring relationship satisfaction and partner satisfaction, we included a measure of self - esteem in our study to control for this variable's influence on our results.

In other words, measures of psychological adjustment correlated positively with each other as did indicators of psychological maladjustment, and negative relationships appeared between these two types of variables.

Coparentality can be considered a variable that measures the marital relationship and parentality, once the collaboration between the couple members might influence the way they interact with the child, evidencing how much marital relationships affect the relationships between parents and children (Margolin, Gordis, & John, 2001).

Abstract: This study evaluates the psychometric properties of an industry - based employee measure of employer responses to injuries (i.e., organizational support and return - to - work policies) and explores the relationship of these variables to post-injury job satisfaction.

Further, the CI was examined in relation to various demographic variables and various measures of other relationship constructs.

To minimize confounding variables that exist when assessing married couples on relationship satisfaction (such as possible financial barriers and stigma of divorce), the sample primarily consisted of unmarried students, who were assessed using two distinct measures of relationship satisfaction.

To discern the effect of age on the relationship among the main variables, analyses by intelligence measure (Mullen and Leiter - R) was conducted, and findings were shown in separate tables for each group.

When the combined data set was separated by intelligence measure, only the younger group (Mullen) displayed moderate correlation between parental depression and adaptive behavior, suggesting that the strength of a linear relationship between these two variables is much stronger in younger deaf children as compared to older deaf children.

To test for associations among the various measures, we used one - tailed, bivariate Pearson's correlations, which measure the strength and direction, as well as the significance of linear relationships between pairs of variables.

Using structural equation modeling, measures of family relationships, such as a person's own and his / her partner's marital adjustment, the amount of contact with children (and grandchildren, if applicable), and being a grandparent or not, served as independent variables to predict each partner's satisfaction with life.

The increased smoking probability induced by the lack of trust in peers somewhat counters the findings for relationship quality and suggests that both variables measure different relationship aspects.

Structural equation modeling (SEM) allows for the simultaneous examination of the relationships between latent constructs defined by multiple measures as well as directly observed variables (e.g., gender, HbA1C) while reducing the effect of measurement error on results.

Of note, we treat these variables as time invariant, while fully acknowledging that these constructs may in other situations be conceived and measured as time - varying characteristics (e.g., daily relationship satisfaction).

I just think it's important to note that correlation measures the strength of the relationship between variables, but does not neccasarily measure anything pertaining to cause / affect.

It tests the strength of a relationship between two variables, no matter if you're measuring inches, pounds, housing prices, or forum votes.