Means Of Several Groups Research Articles

Experimental Research Methods“Anava and Factorial Experiment Psychology Research Design”

The experimental research method is an approach used to examine the effect of certain treatments on other variables under controlled conditions. In experiments, researchers manipulate a stimulus or treatment, then observe its impact on the variable under study. This research discusses experimental design and its purpose, including ANOVA and factorial experimental psychology research designs. Experimental design allows researchers to test hypotheses with well-defined steps before conducting experiments. There are several types of experimental designs, including pre-experimental, true experimental, factorial experimental, and quasi-experimental. Analysis of Variance (ANOVA) is a statistical method for comparing the means of several groups of data with respect to their variances. Factorial design involves the simultaneous manipulation of several factors to study the effect on the dependent variable. This study describes the characteristics, objectives, and types of factorial designs as well as their advantages and disadvantages. The experimental design and statistical analysis used help researchers understand and analyze data more comprehensively and objectively

Multivariate analysis of covariance with potentially singular covariance matrices and non-normal responses

In applied research, it is often sensible to account for one or several covariates when testing for differences between multivariate means of several groups. However, the “classical” parametric multivariate analysis of covariance (MANCOVA) tests (e.g., Wilks’ Lambda) are based on quite restrictive assumptions (homoscedasticity and normality of the errors), which might be difficult to justify. Furthermore, existing potential remedies (e.g., heteroskedasticity-robust approaches) become inappropriate in cases where the covariance matrices are singular or close to singular. Nevertheless, such scenarios are frequently encountered in the life sciences and other fields, when, for example, in the context of standardized assessments, a summary performance measure as well as its corresponding subscales are analyzed. Moreover, computational issues may also lead to singular covariance structures. In the present manuscript, we consider a general MANCOVA model, allowing for potentially heteroskedastic and even singular covariance matrices as well as non-normal errors. We combine heteroskedasticity-consistent covariance matrix estimation methods with our proposed modified MANCOVA ANOVA-type statistic (MANCATS) and apply two different bootstrap approaches. We provide the proofs of the asymptotic validity of the respective testing procedures as well as the results from an extensive simulation study, which indicate that especially the parametric bootstrap version of the MANCATS outperforms its competitors in most small-sample scenarios, both in terms of type I error rates and power. These considerations are further illustrated and substantiated by examining real-life data from standardized achievement tests. Yet, for large sample sizes, Wald-type approaches are an attractive alternative option. Moreover, the choice between Wald-type and MANCATS-based tests might depend on the particular setting (e.g., the covariance structure, or the alternative under consideration).

Detection of skin thickness and echo density in healthy adults by using high-frequency ultrasound

Objective To measure the thickness and echo density of the skin at multiple sites of healthy adults by using high-frequency ultrasound. Methods A total of 50 healthy volunteers were enrolled from Department of Dermatology and Venereology, China-Japan Friendship Hospital between June and December in 2018, including 33 females and 17 males aged 22-69 years. The thickness and echo density of the epidermis, dermis and epidermis-dermis layer were detected by using high-frequency ultrasound at 12 sites, including the forehead, cheek, chest, abdomen, and the inner and outer sides of the upper arm, forearm, thigh and leg. The means of two groups were compared by using t test, and means of several groups were compared by using one-way analysis of variation. Results High-frequency skin ultrasound images differed among different anatomical sites. There were significant differences in the thickness and echo density of the epidermis, dermis and epidermis-dermis layer among the 12 sites (P < 0.05) . The epidermis was thickest at the inner side of the thigh (160.68 μm ± 25.71 μm) , the dermis was thickest at the cheek (1 828.78 μm ± 399.10 μm) , and the epidermis-dermis layer was thickest at the cheek (1 943.48 μm ± 402.4 μm) . The echo density of the epidermis, dermis and epidermis-dermis layer was highest at the inner side of the leg (152.27 ± 21.56) , forearm (52.71 ± 15.57) and forearm (62.56 ± 15.76) respectively. The thickness of the epidermis, dermis and epidermis-dermis layer at the forehead, cheek and the inner side of the forearm was significantly higher in male volunteers than in female volunteers (P ≤ 0.05 or < 0.01) , while the echo density of the dermis and epidermis-dermis layer at the forehead, cheek and the outer side of the leg was significantly lower in male volunteers than in female volunteers (P < 0.05 or 0.01) . Conclusion Differences exist in skin thickness and echo density among different anato mical sites and between male and female healthy adults. Key words: Ultrasonography; Epidermis; Dermis; Skin thickness; Skin echo density

Investigation of the Relationship between Asthma and Visceral Obesity by Epicardial Fat Thickness Measurement.

Obesity is a risk factor defined in recent years for asthma. It is associated not only with asthma but also with many cardiovascular diseases. Visceral obesity is a more significant risk factor than general obesity in cardiovascular diseases. Although the association of visceral obesity with cardiovascular diseases is well known, the relationship in patients with asthma is not fully understood. The aim of the present study was to investigate whether there is a relationship between asthma and visceral fat by using epicardial fat thickness (EFT) measurement. A total of 401 subjects (229 patients with persistent asthma and 172 controls) were enrolled in the study. In our study, EFT was measured, recorded by echocardiography, and was evaluated whether there was a statistical significant difference between the two groups. The mean EFT was 5.84±0.79 mm in the patient group and 5.71±0.93 mm in the control group. There was no statistically significant difference between the groups (p=0.145). Similarly, when we compared control and asthma severity subgroups, we did not find statistically significant differences (control group mean 5.71±0.93 mm, mild group mean 5.86±0.81 mm, moderate group mean 5.8±0.84 mm, and severe group mean 5.83±0.67 mm, p=0.505). In the present study, we observed that the EFT did not increase in patients with asthma compared with the normal population. Based on our results, we suggest that visceral obesity may not be a significant risk factor for asthma.

Characterization and Quantification of the Phases Concentration of Cu60–Zn40 Alloy as Candidate Reference Material

This study describes the process of evaluating a commercially available copper–zinc alloy with the goal of creating an internal reference material. The intended use of this candidate reference material is to evaluate the percentage of alpha and beta phases to verify the accuracy in quantifying phase percentages according to the ASTM E562-11 “Standard Test Method for Determining Volume Fraction by Systematic Manual Point Count.” The candidate also will be useful to validate some commercial programs designed for the quantification of phases. The characterized alloy had a commercial origin and was purchased in the domestic market. For the intended purposes, the alloy must have high chemical and microstructural homogeneity. Therefore, the evaluation of homogeneity was done by optical microscopy, scanning electron microscopy, x-ray fluorescence, and x-ray diffraction and by the method in ASTM E562-11, to finally determine the phase content. Analysis of variance (ANOVA) provides a statistical test of whether or not the means of several groups are equal, and therefore generalizes the t test to more than two groups. ANOVAs are useful for comparing (testing) three or more means (groups or variables) for statistical significance. ANOVA was applied to evaluate the homogeneity of the alloy. This statistical tool is recommended in the ISO Guide 35:2006 to evaluate the variability among and within units. Precision was estimated under repeatability conditions. Precision obtained was of 5% and, according to the results of the ANOVA, it was found that the material is homogenous.

An Analysis of Variance of Food Security by its Main Determinants Among the Urban Poor in the City of Tshwane, South Africa

This paper discusses households’ food insecurity among low income, poor urban households in and around the City of Tshwane, South Africa’s capital city. Using systematic random sampling with sampling interval of three, primary data were collected from 900 selected households, though only data from 827 households were analyzed following a rigorous coherence tests. The survey was conducted in Attridgeville, Soshanguve, and Tembisa. In the process, the study employed the use of two-way analyses of variance to explain differences between actual and expected household food security perceptions and those of severe, moderate and mild food insecurity. A favourable (adverse) variance could be interpreted to imply that means for achieving household food security are lower (higher) than predicted or that food security is higher (lower) than expected given the same level of main determinants. The observed variance is partitioned into components attributable to different sources of variation. ANOVA provides a statistical test of whether or not the means of several groups experiencing favourable (adverse) variances are equal. The main findings are that variances in the population means of households’ experiences of food insecurity vary by income class of the head of household, engagement in formal or informal income sources and by categories of social grants received. Poorer households that depend largely on cash income for food purchases experience highest food security variances and those receiving state pension. As such, timely receipt of household income under conditions of unimpeded access to social grants will improve urban poor households’ food security. The level of educational attainment has a very strong impact on a household’s food security. Those with “no schooling” have the lowest level of food security. Experiencing high variances in access to child grants, and low incomes, younger female household heads experience the highest degree of variances in food security and should be particularly targeted in an effective food security policy plan. Negative food security variance among these categories of South Africans could be devastating.

Testing the homogeneity of the means of several groups of count data in the presence of unequal dispersions

Extra-dispersion (overdispersion or underdispersion) is a common phenomenon in practice when the variance of count data differs from that of a Poisson model. This can arise when the data come from different subpopulations or when the assumption of independence is violated. This paper develops a procedure for testing the equality of the means of several groups of counts, when extra-dispersions among the treatment groups are unequal, based on the adjusted counts using the concept of the design and size effects employed by Rao and Scott, [Rao, J.N.K., Scott, A.J., 1999. A simple method for analyzing overdispersion in clustered Poisson data. Statist. Med. 18, 1373–1385]. We also obtain the score-type test statistics based on quasi-likelihoods using the mean-variance structure of the negative binomial model, and study the properties and performance characteristics of these. The simulation results indicate that the statistic based on the adjusted count data, which has a very simple form and does not require the estimates of the extra-dispersion parameters, performs best among all the statistics considered in this paper. Finally, the proposed test statistic and the score-type statistic based on double-extended quasi-likelihood are illustrated by an analysis of a set of fetal implants in mice arising from a developmental toxicity study.

THE ANALYSIS OF COUNT DATA IN A ONE-WAY LAYOUT

An efficient score statistic for testing the equality of the means of several groups of count data in the presence of a common dispersion parameter is introduced and a new approximation to its distribution is given. The performance of the efficient score statistic using this approximation, the original efficient score statistic approximated by X 2 (t -1), the likelihood ratio statistic and four more ANOVA methods based on raw data or transformed data are compared in terms of size and power by using Monte Carlo simulations. The efficient score statistic with its new approximation is recommended. An application is given.

Size performance of some tests in one-way anova

Tsui and Weerahandi (1989) introduced the notion of generalized p-values and since then this idea is used to solve many statistical testing problems. Heteroskedasticity is one of the major practical problems encountered in ANOVA problems. To compare the means of several groups under heteroskedasticity approximate tests are used in the literature. Weerahandi (1995a) introduced a test using the notion of generalized p-values for comparing the means of several populations when the variances are not equal. This test is referred to as a generalized F-test. In this paper we compare the size performance of the Generalized F-test and four other widely used procedures: the Classical F-test for ANOVA, the F-test obtained by the weighted least-squares to adjust for heteroskedasticity, the Brown-Forsythe-test, and the Welch-test. The comparison is based on a simulation study of size performance of tests applied to the balanced one-way model. The intended level of the tests is set at 0.05. While the Generalized F-test was found to have size not exceeding the intended level, as heteroskedasticity becomes severe the other tests were found to have poor size performance. With mild heteroskedasticity the Welch-test and the classical ANOVA F-test have the intended levels, and the Welch-test was found to perform better than the latter. Widely used (due to computational convenience) weighted F-test was found to have very serious size problems. The size advantage of the generalized F-test was also found to be robust even under severe deviations from the assumption of normality.

Analysis of one-way layout of count data with negative binomial variation

Two C(α) statistics (Neyman, 1959) for testing the equality of the means of several groups of count data in the presence of a common dispersion parameter are derived. The performance of the two C(α) statistics, the likelihood ratio statistic and two more statistics based on transformed data (Anscombe, 1948) is then compared in terms of size and power by using Monte Carlo simulations. The C(α) statistics are recommended. The C(α) statistics for testing the equality of the dispersion parameters of several groups of count data are also derived. Two applications are given.

Comparing the Means of Several Groups

Comparing the Means of Several Groups