Sentences with phrase «variable than analysis»

I think CO2 concentrations are likely higher and more variable than analysis shows.

Not exact matches

«The questionnaire collects 18 variables from mothers over a voice call, and our initial research has shown that 20 % of the data is more than 75 % accurate, can be identified automatically, and is sufficient to build a detailed analysis of the quality of care provided by different health facilities.

Several studies have also attempted to understand the role of breastfeeding on IQ, and although some authors conclude that the observed advantage of breastfeeding on IQ is related only to genetic and socioenvironmental factors, a recent meta - analysis showed that after adjustment for appropriate key co-factors, breastfeeding was associated with significantly higher scores for cognitive development than formula feeding.6 Longer duration of breastfeeding has also been positively associated with intelligence in adulthood.22 We also observed the benefits of long - term breastfeeding on mental indices, along with the indirect benefit of balancing the impact of exposure to p, p ′ DDE after adjustment for some socioeconomic variables.

Atkinson and her colleagues hope to refine their analyses to include other variables, such as information about extraction processes and the geology at individual well sites, «to help us understand why some areas seem much more prone to induced seismicity than others.»

In addition, the new study is the first to provide information on self - reported aggression of drivers in the Republic of Ireland and is also the first to support the proposed relationship between impulsivity and driving anger with more than correlational analysis, which provides only limited information about the relationships between variables.

Specifically, the reviewed data showed that analysis and discussion included sex as an independent variable in less than 33 % (432 of 1303) of surgical clinical research studies.

The performance differences, however, while statistically significant, are much smaller than those found in the NAEP analysis, which supports the view that control variables used in the NAEP models are biased against private schools.

However, the reduction of the number of cases (to fewer than 10 per variable for the regression analysis) limits the reliability of this result.

The relative non-competitiveness of the field arises, I think, out of the fact that our analysis focuses on different variables than almost everyone else's.

For example, in 2015, Deutsche Asset & Wealth Management conducted an analysis that aggregated evidence from more than 2000 empirical studies on ESG variables and financial performance.

The research results involve more than 200 variables, but for this analysis, we focused on the basic demographic player profile of each franchise to see which have found a sweet spot in terms of the audience they attract.

They discussed the effect of variables being non-iid on the extreme value analysis, and after taking that into account, propose that changes in extreme precipitation are likely to be larger than the corresponding changes in annual mean precipitation under a global warming.

They are simply a first estimate.Where multiple analyses of the biases in other climatological variables have been produced, for example tropospheric temperatures and ocean heat content, the resulting spread in the estimates of key parameters such as the long - term trend has typically been signicantly larger than initial estimates of the uncertainty suggested.

That is, (1) there is dO18 measurement, which I claim should be fairly precise, but you stated has large uncertainties, and then there is (2) derivation of temperature from dO18 values, where you have indeed pointed out that there could be a number of possibly confounding factors in that analysis if other variables than temperature are not controlled.

The statistical analyses (Table FE3) indicated that: (1) there are statistically significant differences in carbon dioxide emission factors across both coal rank and State of origin; (2) coal rank and State of origin each explain approximately 80 percent of the variation in carbon dioxide emission factors; and (3) State of origin combined with coal rank is a slightly more powerful explanatory variable than either coal rank or State of origin alone.

The report painstakingly captures wide geographic variations in the carbon contents of fuels used to generate electricity, among other variables, giving it a finer grain than many previous analyses of carbon tax incidence.

The p values using the nine climate variables (denoted as «Overall» in Table 4) of MMEs are larger than the threshold (0.05, significant level = 5 %), which means that, according to this analysis, these ensembles have not been shown to be unreliable.

In simple regression analyses examining the association between psychological distress, and demographic and disease variables, only the recipient of the questionnaire was statistically significant (B = 3.13, p = 0.03); partner recipient was significantly associated with higher psychological distress than patient recipient.

Covariates were those identified in previous analyses of this sample to independently contribute to child BMI status.16 Parent - reported child variables were gender (male or female), number of siblings in the household (0, 1, 2, or ≥ 3), and language other than English spoken at home by the child (yes or no).

Because the univariate effect of the representativeness of the sample was lost in multivariate analysis, we conclude that this effect was based on a confounding variable (the use of control groups rather than test norms in community - based studies with representative samples).

As a result, and together with incomplete data in some variables across subjects, the sample sizes for variables in the bivariate and multivariable analyses were less than those for the completed number of cross-sectional and registration - linked ACE score profiles.

In each of the three factorial analyses using one of the VIEW factors and stress condition as independent variables, participants rated the high stress vignette as significantly more stressful than the low stress vignette.

A sample - to - variable ratio of 10:1 (Osborne & Costello, 2005; Singh et al., 2016) or alternately more than 300 cases are generally considered adequate for factor analysis (Tabachnick & Fidell 1996; Comrey & Lee, 1992).

Although there was a lower response among families of minority groups (70 %) than among white families (84 %), (p <.01), t - tests and chi - square analyses indicated that minority - group respondents did not differ from minority - group nonrespondents on any birth status or sociodemographic variable (p >.05).

In terms of validity, we make a factor analysis of the variables and delete the measurement which the factor loading is less than 0.4 to make the average variance extracted (AVE) reach more than 0.5, and it shows that the convergent validity of each variable meets the requirements.

Regarding sample power, all three samples were far beyond the suggested 10 cases for each observed variable threshold (Osborne & Costello, 2005; Singh et al., 2016) and larger than 300 or 500 cases (Tabachnick & Fidell 1996; Comrey & Lee, 1992), suggested as sufficient sample size for factor analysis.

Possibly, mania is a more purely biologically driven phenomenon than bipolar depression, with onsets more readily attributable to medication inconsistency, sleep deprivation, circadian disruption, or behavioral activation.21,22,84 - 86 In contrast, social and familial support has been found to protect against depression in bipolar and unipolar affective disorders, but the role of these variables in manic recurrences is unclear.86 - 88 An analysis of laboratory interactional data from a subset of 44 families in this sample revealed that treatment - related improvements in family communication skills were more closely associated with reductions in patients» depressive than manic symptoms.56 Thus, manic and depressive symptoms may be influenced by different constellations of risk and protective factors.

In terms of variables, it is unlikely to see a factor analysis with fewer than 50 variables.

Not all her analyses use controls, but where she does, Kuperberg includes variables such as age at co-residence (or age at marriage, depending), education, race / ethnicity, family stability growing up, if one grew up religious or not, if one had previously cohabited with someone other than the mate (serial cohabitation), if the couple had moved in together while expecting a baby, and if there had been any birth prior to cohabiting (within the relationship or from a prior one).

Listwise deletion was used to handle missing data because there were no more than 5 % missing on any variable in the analysis (cf. Allison, 2010).

We conducted negative binomial regression analysis instead of ordinary least squares regression analysis because accuracy of surrogate estimation was a count variable displaying overdispersion (i.e., its variance is larger than its mean).

Apart from the aim to be consistent with the approach used in earlier articles in this special issue [3, 4], item response modeling differs from SEM in that (i) it models the actual response data rather than the covariance matrix among the variables, and hence, (ii) it allows a finer grain of interpretation and fit analysis.

Given the much larger number of women than men in this sample, preliminary analyses examined whether this variable should be controlled in subsequent statistical procedures.

Linear regression analyses revealed that different attitudinal and experiential variables predict MRT performance in male and female students and that the variance explained by them is higher in females than in males.

Although PAPCA uses a principal component analysis (PCA) as a tool to identify components of interest, this approach is different from PCA because its main goal is classifying individuals, rather than variables, by interpreting arrays of individuals» response patterns as latent profile patterns (most typical patterns of participants» item responses).

Second, regression analyses revealed that social support functioned as a moderator of the impact of autism severity on sibling adjustment rather than a mediator or compensatory variable.

In these analyses, the CU raw score was considered as the independent variable and covariates were also SES, children's sex and ethnicity, comorbidities other than ODD and the number of DSM - IV CD symptoms.

Respondents with more than two missing values on a variable were eliminated from further analysis (N = 7).

We chose to retain the whole sample for the analyses and deal with the missing values for the sexual peer norm variables of the participants who did not complete the online questionnaire, because it has been shown that this yields more accurate results than listwise deletion, even when data are not missing completely at random (Schafer & Graham, 2002).

Chi square analyses and t - tests were conducted to examine potential differences in the demographic, predictor, and outcome variables as a function of accelerometer wear adherence (wearing the accelerometer for less than 4 versus 4 or more days).

For this modeling, the measures of CU (ICU - total raw score) and ODD (binary diagnosis present / absent) were considered as the independent variables and the analyses were adjusted by the covariates family SES, children's sex and ethnicity, presence of comorbidities other than ODD and the number of DSM - IV CD symptoms.

Douglas was only rated as weak on controlling for confounds as the study reported that the index group in this study were significantly more likely to be younger, live in a more deprived area and have experienced parental separation, divorce or death than the control group, yet these variables were not discussed in the method or controlled for in the analyses.

It can handle more variables than that, but with more than five variables, it slows down, and above nine variables it goes into paralysis analysis.