Squared Multiple Correlation Research Articles

Factor analysis of hepcidin on cardiac iron overload and fibrosis among thalassemia major children

BackgroundCardiac iron overload is a common cause of death in thalassemia major and is associated with hepcidin, which is primary iron homeostasis. Therapeutics that could increase hepcidin levels or act as hepcidin agonists might help treat the abnormal iron absorption in individuals with β-thalassemia and related disorders. We aimed to verify the structure and study the relationship of release variables, hepcidin, cardiac iron overload, or fibrosis.MethodsA cross-sectional study was conducted among thalassemia majors, aged 6–18 years at Rumah Sakit Anak dan Bunda Harapan Kita Indonesia, between January 2019 and May 2020. Clinical data, hepcidin-25, interacting variables laboratory test, MRIT2* used to assess cardiac iron overload, late gadolinium enhancement (LGE), and soluble suppression of tumoregenicity2 (ST2) to assess fibrosis were studied. The correlation test was performed with SPSS version 20, Amos 22 was used to assess confirmatory factor analysis (CFA), and squared multiple correlation (SMC) was used to determine the proportion of total variation explained by the model.ResultsWe recruited 80 patients, of those 8 (10%) were cardiac iron overload, 5 (5.25%) were fibrosis, and 3 (3,75%) were ST2 > 35 mg/dL. CFA showed variables that interact with hepcidin release were Hb, reticulocyte-He (Re-He), HIF-1α, Immature granulocyte (IG), hs-CRP, IL-6, ferritin, and transferrin saturation. There was no direct hepcidin role in cardiac iron overload and fibrosis. No interacting variable role on hepcidin. SMC of hepcidin on cardiac iron overload was 20%.ConclusionFactor analysis of hepcidin was Hb, Ret-He, HIF-1α, IG, hs-CRP, IL-6, ferritin, and transferrin saturation. No direct hepcidin role in cardiac iron overload and fibrosis. Hepcidin explains approximately 20% of the total variation in cardiac iron overload.

An International Classification of Functioning, Disability and Health Model-Based Analysis of Suicidal Ideation among 9920 Community-Dwelling Korean Older Adults.

Many complex factors contribute to suicide in older adults. The suicidal ideation that precedes suicide is an especially direct predictor of suicide. This study aimed to identify the effects between variables affecting suicidal ideation among older adults using the International Classification of Functioning, Disability and Health (ICF) model and understand the causal relationships to systematize complex factors. This study used data from 9920 community-dwelling older adults who completed a national survey in 2020 to classify predictors of suicidal ideation (e.g., depression, subjective health status, sociodemographic factors, health factors, social support, instrumental activities of daily living (IADL), and social participation) by using the ICF model. To determine the causal relationship between variables, this study examined significance based on the critical ratio (C.R.) and squared multiple correlation (SMC) by using a path model. Gender, education level, economic level, age, IADL, relationship satisfaction with a child, depression, and the number of chronic diseases significantly affected suicidal ideation, while age, employment status, participation in social groups, formal and informal support, satisfaction with a friend/neighbor relationship, and subjective health status did not significantly influence it. Moreover, depression mediated the relationship between each of these variables and suicidal ideation. It was found that depression was the most direct and mediating factor in suicidal ideation among many factors affecting the suicidal ideation of community-dwelling older adults. Additional studies should be conducted to develop community-level strategies based on these factors and understand causal relationships.

Stroke volume and cardiac output during 6 minute-walk tests are strong predictors of maximal oxygen uptake in people after stroke.

The 6-minute walk test (6MWT) is a field test commonly used to predict peak oxygen consumption (VO2peak) in people after stroke. Inclusion of cardiodynamic variables measured by impedance cardiography (ICG) during a 6MWT has been shown to improve prediction of VO2peak in healthy adults but these data have not been considered in people after stroke. This study investigates whether the prediction of VO2peak can be improved by the inclusion of cardiovascular indices derived by impedance cardiography (ICG) during the 6MWT in people after stroke. This was a cross-sectional study. Patients diagnosed with stroke underwent in random order, a maximal cardiopulmonary exercise test (CPET) and 6MWT in separate dates. Heart rate (HR), stroke volume (SV) and cardiac output (CO) were measured by ICG during all tests. Oxygen consumption was recorded by a metabolic cart during the CPET. Recorded data were subjected to multiple regression analyses to generate VO2peak prediction equations. Fifty-nine patients, mean age 50.0±11.7 years were included in the analysis. The mean distance covered in the 6MWT (6MWD) was 294±13 m, VO2peak was 19.2±3.2 ml/min/kg. Mean peak HR, SV and CO recorded during 6MWT were 109±6 bpm, 86.3±8.8 ml, 9.4±1.2 L/min and during CPET were 135±14 bpm, 86.6±9 ml, 11.7±2 L/min respectively. The prediction equation with inclusion of cardiodynamic variables: 16.855 + (-0.060 x age) + (0.196 x BMI) + (0.01 x 6MWD) + (-0.416 x SV6MWT) + (3.587 x CO 6MWT) has a higher squared multiple correlation (R2) and a lower standard error of estimate (SEE) and SEE% compared to the equation using 6MWD as the only predictor. Inclusion of SV and CO measured during the 6MWT in stroke patients further improved the VO2peak prediction power compared to using 6MWD as a lone predictor.

Compliance with Company and Allied Matters Act (CAMA) Evincing Financial Reporting Quality Impact on Performance of Small and Medium Enterprises (SMEs) in Nigeria

This study is on the compliance with regulatory framework by Small and Medium Enterprises (SMEs) in Nigeria evincing financial reporting quality impact on firm performance. The design is an exploratory survey employing structural equation modelling in model specification. The empirical analysis of this study went through exploratory factor analysis (EFA) and then confirmed by confirmatory factor analysis (CFA). This study examined the impact of compliance with companies and allied Matters Act through financial reporting quality on performance of SMEs in Nigeria. The outcome revealed that the Financial Reporting Quality (exogenous variables) accounted for 39% while the Firm Performance (endogenous variables) accounted for 61% of the total variance. Thus, the Squared Multiple Correlations (SMC) fraction result confirmed that financial reporting quality has direct relationship with SMEs performance. Thus, Financial Reporting Quality (FRQ) impact on firm performance at (β=0.875) is significant. The study recommends that since FRQ is having a direct and significant relationship with SMEs performance the need for enforcing compliance to statutory and regulatory framework on SMEs by SMEDAN, CBN and all other stakeholders is necessary. Keywords: compliance, reporting quality, performance, SMEs, Nigeria. DOI : 10.7176/RJFA/10-6-08 Publication date :March 31 st 2019

On Correctly Adjusting the Squared Multiple Correlation Coefficient in Linear Regression: Effect Size Estimation and Significance Testing with Application to Substance Abuse Research

Linear regression analysis is ubiquitous in many areas of scholarly inquiry, including substance abuse research. In linear regression it is common practice to test whether the squared multiple correlation coefficient, R2, differs significantly from zero. However, this test is misleading because the expected value of R2 is not zero under the null hypothesis. In this brief methodological note I discuss the implications of this realization for calculating and interpreting the squared multiple correlation coefficient, R2. In addition, I discuss and offer freely available software that calculates the expected value of R2 under the null hypothesis that ρ-the population value of the multiple correlation coefficient-equals zero, an adjusted R2 value and effect size measure that both take into account the expected value of R2, and an F statistic that tests the significance of difference between the obtained R2 and the expected value of R2 under the null hypothesis that ρ=0.

Development of the Internet addiction scale based on the Internet Gaming Disorder criteria suggested in DSM-5

This study was conducted to develop and validate a standardized self-diagnostic Internet addiction (IA) scale based on the diagnosis criteria for Internet Gaming Disorder (IGD) in the Diagnostic and Statistical Manual of Mental Disorder, 5th edition (DSM-5). Items based on the IGD diagnosis criteria were developed using items of the previous Internet addiction scales. Data were collected from a community sample. The data were divided into two sets, and confirmatory factor analysis (CFA) was performed repeatedly. The model was modified after discussion with professionals based on the first CFA results, after which the second CFA was performed. The internal consistency reliability was generally good. The items that showed significantly low correlation values based on the item-total correlation of each factor were excluded. After the first CFA was performed, some factors and items were excluded. Seven factors and 26 items were prepared for the final model. The second CFA results showed good general factor loading, Squared Multiple Correlation (SMC) and model fit. The model fit of the final model was good, but some factors were very highly correlated. It is recommended that some of the factors be refined through further studies.

The reliability and validity of Chinese translation of mood and anxiety symptoms questionnaire in high school students

Objective To develop a Chinese translation of the Mood and Anxiety Symptoms Questionnaire-Short Form (MASQ-SF) and evaluate the reliability and the validity in a sample of Chinese middle school students. Methods The questionnaire was administered to 682 middle school students. Internal consistency, testretest and confirmatory factor analyses were analyzed. Results The internal consistency reliability for the total scale was 0.95, and for the four factors ranged from 0.84 to 0.92 and the one month test-retest reliability coefficient for the total questionnaire was 0.78. The mean inter-item correlation coefficient for the MASQ-SF was 0.24,and the mean inter-item correlation coefficient for the four factors ranged from 0. 20 to 0.51. And the results of confirmatory factor analyses (IFI 0.91, CFI 0.92, TLI 0.91, RMSEA 0.06). The factors loadings ranged from 0. 28 to 0.71. The squared multiple correlations were 0.12 to 0.53. Indicated that the four-factor structure of the MASQ-SF was suitable for the Chinese middle school sample. Conclusion The Chinese version of the MASQ-SF with acceptable psychometric quality,and appropriate for assessing anxiety and depression in Chinese adolescence. Key words: Depression ; Anxiety ; Adolescence ; Reliability ; Validity ;

Sample Size Planning for the Squared Multiple Correlation Coefficient: Accuracy in Parameter Estimation via Narrow Confidence Intervals

Methods of sample size planning are developed from the accuracy in parameter approach in the multiple regression context in order to obtain a sufficiently narrow confidence interval for the population squared multiple correlation coefficient when regressors are random. Approximate and exact methods are developed that provide necessary sample size so that the expected width of the confidence interval will be sufficiently narrow. Modifications of these methods are then developed so that necessary sample size will lead to sufficiently narrow confidence intervals with no less than some desired degree of assurance. Computer routines have been developed and are included within the MBESS R package so that the methods discussed in the article can be implemented. The methods and computer routines are demonstrated using an empirical example linking innovation in the health services industry with previous innovation, personality factors, and group climate characteristics.

Confidence Intervals for the Squared Multiple Semipartial Correlation Coefficient

The squared multiple semipartial correlation coefficient is the increase in the squared multiple correlation coefficient that occurs when two or more predictors are added to a multiple regression model. Coverage probability was investigated for two variations of each of three methods for setting confidence intervals for the population squared multiple semipartial correlation coefficient. Results indicated that the procedure that provides coverage probability in the [.925, .975] interval for a 95% confidence interval depends primarily on the number of added predictors. Guidelines for selecting a procedure are presented.

Improved Shrinkage Estimation of Squared Multiple Correlation Coefficient and Squared Cross-Validity Coefficient

The sample squared multiple correlation coefficient is widely used for describing the usefulness of a multiple linear regression model in many areas of science. In this article, the author considers the problem of estimating the squared multiple correlation coefficient and the squared cross-validity coefficient under the assumption that the response and predictor variables have a joint multinormal distribution. Detailed numerical investigations are conducted to assess the exact bias and mean square error of the proposed modifications of established estimators. Notably, the positive-part Pratt estimator and the synthesis of Browne and positive-part Pratt estimators are recommended in the estimation of squared multiple correlation coefficient and squared cross-validity coefficient, respectively, for their overall advantages of incurring the least amount of statistical discrepancy and computational requirement.

Asymptotic Expansion and Conditional Robustness for the Sample Multiple Correlation Coefficient Under Nonnormality

ABSTRACT Asymptotic distributions of the standardized estimators of the squared and non squared multiple correlation coefficients under nonnormality were obtained using Edgeworth expansion up to O(1/n). Conditions for the normal-theory asymptotic biases and variances to hold under nonnormality were derived with respect to the parameter values and the weighted sum of the cumulants of associated variables. The condition for the cumulants indicates a compensatory effect to yield the robust normal-theory lower-order cumulants. Simulations were performed to see the usefulness of the formulas of the asymptotic expansions using the model with the asymptotic robustness under nonnormality, which showed that the approximations by Edgeworth expansions were satisfactory.

Confidence Intervals, Power Calculation, and Sample Size Estimation for the Squared Multiple Correlation Coefficient under the Fixed and Random Regression Models: A Computer Program and Useful Standard Tables

In this article, the authors introduce a computer package written for Mathematica, the purpose of which is to perform a number of difficult iterative functions with respect to the squared multiple correlation coefficient under the fixed and random models. These functions include, among others, computation of confidence interval upper and lower bounds, power calculation, calculation of sample size required for a specified power level, and providing estimates of shrinkage in cross validating the squared multiple correlation under both the random and fixed models. Attention is given to some of the technical issues regarding the selection of, and working with, these two types of models as well as to issues concerning the construction of confidence intervals.

Sample Sizes for Confidence Intervals on the Increase in the Squared Multiple Correlation Coefficient

The increase in the squared multiple correlation coefficient (δ R2) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. The probability that an asymptotic confidence interval will include δρ2 was investigated. With sample sizes typically used in regression analyses, when δρ2 = 0.00 and the confidence level is .95 or greater, the probability will be at least .999. For δρ2≥ .01 and a confidence level of .95 or greater, the probability will be smaller than the nominal confidence level. For δρ2≥ .05 and a confidence level of .95, tables are provided for the sample size necessary for the probability to be at least .925 and to be at least .94.

Determining Sample Size for Accurate Estimation of the Squared Multiple Correlation Coefficient

While several resources are available to help researchers determine the minimum sample size needed to achieve target power for a wide variety of hypothesis tests, such resources are generally not available for determining the sample size when accurate parameter estimation is of interest. Sample size tables and procedures used to determine sample size for hypothesis tests should not be used for estimation because providing evidence that a parameter is not equal to some specific value is a fundamentally different task than accurately estimating the parameter. In particular, the necessary sample size required for hypothesis testing declines as the difference between the parameter and the specified value increases, but this difference does not have the same relationship to the sample size needed for accurate estimation. As interest in reporting estimates of effect sizes increases, sample size guidelines are needed for accurate estimation of these parameters. The present article focuses on the squared multiple correlation coefficient and presents regression equations that permit determination of sample size for estimating this parameter for up to 20 predictor variables. A comparison of the sample sizes reported here with those needed to test the hypothesis of no relationship between the predictor and criterion variables demonstrates the need for researchers to consider the purpose of their research and what is to be reported when determining the sample size for the study.

A Comparison of Methods for Constructing Confidence Intervals for the Squared Multiple Correlation Coefficient

Four methods for constructing 100(1 - a)% confidence intervals for the population squared multiple correlation coefficient (r2) were compared. In one method the confidence interval was constructed by using the distribution of R². Provided r² > 0 the coverage probability for this method is exactly 1 - a when the data are multivariate normal. The other three methods are based on results in Olkin and Finn (1995) and are approximate. Results show that each of the approximate methods works very poorly for some combinations of r². The method based on the distribution of R² is recommended.

Effect of Intracluster Correlation on theR-Square Statistic

R2-statistic is a popular and very widely used statistic in regression analysis to estimate the square multiple correlation (SMC), ρ2, between a response variable Y and p predictor variables, X1, …, Xp. Numerous articles are available in the statistical literature on the properties of R2 as an estimator of ρ2 when the observations are uncorrelated. However, relatively little is known about the behavior of R2 when the available observations are correlated such as the data that result from complex sampling schemes. In this paper, we study the behavior R2 in the presence of two-stage sampling data. An approximate expressions for the variance and the bias of R2 in the presence of two-stage cluster sampling data with positive intracluster correlation (ρ*) are obtained. It is evident from these formulas and from a simulation study that R2 is a poor estimator of ρ2 except when ρ* is small. As such, we consider several alternative estimators of ρ2 and evaluate their theoretical properties and finite sample performance using a simulation study.

Comments on Huberty’s Test of the Squared Multiple Correlation Coefficient

It is argued that Huberty’s (1994) test for inferring a better-than-“chance” performance of the observed R2 value is inconsistent. Because there is a hidden change of the null distribution of R2, the “chance” value and the observed R2 value are not comparable in Huberty’s test. A new test is proposed for resolving these difficulties. It is shown that the new test is equivalent to an adjustment of the α-level of the classical test of the squared multiple correlation coefficient.

Comments on Huberty's Test of the Squared Multiple Correlation Coefficient

Comments on Huberty's Test of the Squared Multiple Correlation Coefficient

An Evaluation of Two Techniques for Constructing Confidence Intervals for the Squared Multiple Correlation Coefficient

Fowler's (1986) and Helland's (1987) techniques for constructing confidence intervals for the squared multiple correlation coefficient are discussed. It is shown how the techniques may be applied using F tables and a calculator. Recalculation of Fowler's and Helland's examples shows that Helland's limits are closer to exact values than Fowler's limits. Using realistic simulation results it is shown that, generally speaking, the lower limits of .95 confidence intervals are about the same for both techniques. Fowler's upper limits are much lower than Helland's, however. From further examination it is concluded that Helland's upper limits nevertheless are somewhat too low. Finally, many iterations are required if both N and R1 are small.

Factor analytic evaluation of differences between black and caucasian emotionally disturbed children on the children's depression inventory

Scores on the Children's Depression Inventory (CDI) for 392 Caucasian and 159 black psychiatric inpatients between 6 and 18 years of age were subjected to principal-components factor extraction with Varimax rotation. Factors not contributing to variance were eliminated using the scree test (Cattell, 1966) and the degree of variance accounted for by remaining factors for both groups was examined through respective squared multiple correlations. A comparison of CDI factors for blacks and Caucasians indicated some differences, with blacks be ing less suicidal yet higher on other behavioral dimensions, such as oppositionality, and Caucasians higher on affective dimensions, such as sadness. The results support research on black-Caucasian differences and suggest the need to validate further the CDI with minority populations.