"P Value" or "Number Needed to Treat": Which One Is Better for Evaluating Clinical Treatments’ Benefits?

Statistical significance does not necessarily mean clinical significance. A P value less than 0.05 does not guarantee the clinical effectiveness of a treatment. To assess the clinical valuable of a treatment, the effect size must be calculated. The number needed to treat (NNT) is an example of an effect size measure that can be very helpful in determining the clinical significance of a treatment. Therefore, it is recommended for all researchers and physicians to look beyond the P value and calculate the NNT for assessing the clinical significance of therapeutic measures and agents.

Detection of Differential Item Functioning Using Mantel-Haenszel, Standardization Proportion and BILOG-MG Procedures

Differential item functioning (DIF) is a procedure to identify whether an item favours a particular group of respondents once they are matched on respective ability levels. There are numerous procedures reported in the literature to detect DIF, but the Mantel-Haenszel (MH), Standardized Proportion Difference (SPD), and BILOG-MG are frequently used to ensure the fairness of assessments. The aim of the present study was to compare procedural characteristics using empirical data. We found Mantel-Haenszel and standardized proportion difference provide comparable results while BILOG-MG has flagged a large number of items, but the magnitude of DIF was trivial from a test development perspective. The results also showed Mantel-Haenszel and standardized proportion difference index provide the effect size measure of DIF, which facilitates for further necessary actions, especially for item writers and practitioners.

Are my measures comparable across groups? A unified item- and scale-score-level approach to quantifying scalar non-invariance bias

When scalar invariance does not hold, which is often the case in application scenarios, the amount of non-invariance bias may either be consequential for observed mean comparisons or not. So far, only a few attempts have been made to quantify the extent of bias due to measurement non-invariance. Building on Millsap and Olivera-Aguilar (2012), we derived a new effect size measure, called Measurement Invariance Violation Index (MIVI), from first principles. MIVI merely assumes partial scalar invariance for a set of items forming a scale and quantifies the intercept difference of one non-invariant item (at the item-score level) or several non-invariant items (at the scale-score level) as the share (i.e., proportion) of the total observed scale score difference between groups. One can inspect the cancelation effects of item bias at the scale-score level when using directional instead of absolute terms. We provide computational code and exemplify MIVI in simulated contexts.

A robust effect size measure Aw for MANOVA with non-normal and non-homogenous data

A common research question in psychology entails examining whether significant group differences (e.g. male and female) can be found in a list of numeric variables that measure the same underlying construct (e.g. intelligence). Researchers often use a multivariate analysis of variance (MANOVA), which is based on conventional null-hypothesis significance testing (NHST). Recently, a number of quantitative researchers have suggested reporting an effect size measure (ES) in this research scenario because of the perceived shortcomings of NHST. Thus, a number of MANOVA ESs have been proposed (e.g. generalized eta squared [Formula: see text], generalized omega squared [Formula: see text]), but they rely on two key assumptions—multivariate normality and homogeneity of covariance matrices—which are frequently violated in psychological research. To solve this problem we propose a non-parametric (or assumptions-free) ES ( Aw) for MANOVA. The new ES is developed on the basis of the non-parametric A in ANOVA. To test Aw we conducted a Monte-Carlo simulation. The results showed that Aw was accurate (robust) across different manipulated conditions—including non-normal distributions, unequal covariance matrices between groups, total sample sizes, sample size ratios, true ES values, and numbers of dependent variables—thereby providing empirical evidence supporting the use of Aw, particularly when key assumptions are violated. Implications of the proposed Aw for psychological research and other disciplines are also discussed.

A non-parametric effect size measure capturing changes in central tendency and shape of data distributions more flexibly than Cohen’s d

Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treatment-related effects on the data if the effects were not reflected by the central tendency. "Impact” is a novel nonparametric measure of effect size obtained as the sum of two separate components and includes (i) the change in the central tendency of the group-specific data, normalized to the overall variability, and (ii) the difference in the probability density of the group-specific data. Results obtained on artificial data and empirical biomedical data showed that impact outperforms Cohen's d by this additional component. It is shown that in a multivariate setting, while standard statistical analyses and Cohen’s d are not able to identify effects that lead to changes in the form of data distribution, “Impact” correctly captures them. The proposed effect size measure shares the ability to observe such an effect with machine learning algorithms. It is numerically stable even for degenerate distributions consisting of singular values. Therefore, the proposed effect size measure is particularly well suited for data science and artificial intelligence-based knowledge discovery from (big) and heterogeneous data.

Minat Beli Produk Ramah Lingkungan Mahasiswa Pecinta Lingkungan di Surakarta

Konsumsi produk ramah lingkungan kini menjadi gaya hidup yang popular yang mulai diikuti oleh pemuda. Beberapa penelitian membuktikan bahwa perilaku konsumsi produk ramah lingkungan lebih didorong oleh motivasi sosial dan emosional daripada motivasi fungsional. Keterlibatan pemuda dalam kegiatan dan organisasi ramah lingkungan mendorong dia untuk melakukan kegiatan yang ramah lingkungan dalam kehidupan sehari-harinya. Penelitian ini melaporan hasil studi minat beli terhadap produk ramah lingkungan kepada 124 mahasiswa di tiga Perguruan Tinggi Negeri di Surakarta. Tiga variabel yang akan diuji adalah, keikutsertaaan mahasiswa dalam organisasi ramah lingkungan, consumer guilt jika tidak melakukan kegiatan ramah lingkungan, dan minat beli terhadap produk ramah lingkungan. Hasil path analysis dan effect size measure untuk menguji mediasi menunjukkan bahwa, consumer guilt mampu menjadi mediator dari hubungan keikutsertaan mahasiswa dalam organisasi ramah lingkungan dengan minat beli mahasiswa terhadap produk ramah lingkungan.

Reconstruction of Water Infiltration Rate Reducibility in Response to Suspended Solid Characteristics Using Singular Spectrum Analysis: An Application to the Caspian Sea Coast of Nur, Iran

Drawing a distinction between the suspended solid size and concentration impacts on physical clogging process in the Managed Aquifer Recharge (MAR) systems has been fraught with difficulties. Therefore, the current study was then aimed to statistically investigate and differentiate the impacts of clay-, silt- and sand-sized suspended solids at three concentration levels including 2, 5 and 10 g/L, compared with the clean water (0 g/L), on infiltration rate reducibility. The treatments were compared by virtue of Cohen’s d effect size measure. Furthermore, the competency of Singular Spectrum Analysis (SSA) was evaluated in reconstruction of infiltration rate. Results showed that clay-sized suspended solids were found to be the most important determining factor in physical clogging occurrence. The effect size measure highlighted that a lower concentration level of clay-sized suspended solids, that is, 2 g/L could be more important in trigging the physical clogging than a higher concentration level of silt-sized suspended solids namely 5 g/L. Also, we recognized that concentration level of clay-sized suspended sediments could non-linearly decrease the infiltrability. Also, findings revealed that SSA represented a high level of competency in reconstruction of the infiltration rate under all treatments. Hence, SSA can be quite beneficial to MAR systems for forecasting applications.