A Comparison of Adjacent Categories and Cumulative Differential Step Functioning Effect Estimators

Several statistical procedures have been suggested for detecting differential item functioning (DIF) and differential step functioning (DSF) in polytomous items. However, standard procedures are designed for the comparison of pre-specified reference and focal groups, such as males and females. Here, we propose a framework for the detection of DIF and DSF in polytomous items under the rating scale and partial credit model, that employs a model-based recursive partitioning algorithm. In contrast to existing procedures, with this approach no pre-specification of reference and focal groups is necessary, because they are detected in a data-driven way. The resulting groups are characterized by (combinations of) covariates and thus directly interpretable. The statistical background and construction of the new procedures are introduced along with an instructive example. Four simulation studies illustrate and compare their statistical properties to the well-established likelihood ratio test (LRT). While both the LRT and the new procedures respect a given significance level, the new procedures are in most cases equally (simple DIF groups) or more powerful (complex DIF groups) and can also detect DSF. The sensitivity to model misspecification is investigated. An application example with empirical data illustrates the practical use. A software implementation of the new procedures is freely available in the R system for statistical computing.

Identifying items with differential item functioning (DIF) in an assessment is a crucial step for achieving equitable measurement. One critical issue that has not been fully addressed with existing studies is how DIF items can be detected when data are multilevel. In the present study, we introduced a Lord's Wald test-based procedure for detecting both uniform and non-uniform DIF with polytomous items in the presence of the ubiquitous multilevel data structure. The proposed approach is a multilevel extension of a two-stage procedure, which identifies anchor items in its first stage and formally evaluates candidate items in the second stage. We applied the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm to estimate multilevel polytomous item response theory (IRT) models and to obtain accurate covariance matrices. To evaluate the performance of the proposed approach, we conducted a preliminary simulation study that considered various conditions to mimic real-world scenarios. The simulation results indicated that the proposed approach has great power for identifying DIF items and well controls the Type I error rate. Limitations and future research directions were also discussed.

System-based testing of a voltage differential protection scheme for shunt capacitor bank

There has been growing use of ideal point models to develop scales measuring important psychological constructs. For meaningful comparisons across groups, it is important to identify items on such scales that exhibit differential item functioning (DIF). In this study, the authors examined several methods for assessing DIF on polytomous items generated by an ideal point process. Two paradigms (i.e., null hypothesis significance testing [NHST] and effect size quantification) were utilized, and three test statistics (i.e., the log-likelihood ratio [LR], the Akaike information criterion [AIC], and Lord’s chi-square) and two approaches to DIF testing (i.e., the constrained and free baseline methods) were evaluated. In addition, the authors investigated three levels of impact. The results revealed that DIF effect sizes were moderately large for the .50 uniform DIF conditions and small for nonuniform DIF; moreover, the LR test in general yielded the best results. When there was small to moderate impact, the free baseline approach combined with an item linking implementation produced the most satisfactory results.

Rebecca Zwick, Dorothy T. Thayer, Evaluating the Magnitude of Differential Item Functioning in Polytomous Items, Journal of Educational and Behavioral Statistics, Vol. 21, No. 3 (Autumn, 1996), pp. 187-201

Liu and Agresti (1996) proposed a Mantel and Haenszel‐type (1959) estimator of a common odds ratio for several 2 × J tables, where the J columns are ordinal levels of a response variable. This article applies the Liu‐Agresti estimator to the case of assessing differential item functioning (DIF) in items having an ordinal response variable. A simulation study was conducted to investigate the accuracy of the Liu‐Agresti estimator in relation to other statistical DIF detection procedures. The results of the simulation study indicate that the Liu‐Agresti estimator is a viable alternative to other DIF detection statistics.

Practitioners are increasingly becoming interested in Differential Item Functioning (DIF) forimproving the validity of test and scale interpretations. Among the statistical procedures availableto assess DIF in dichotomous and polytomous items, the Mantel-Haenszel chi-square (1959) andother standardization proceduresmay beparticularly attractive to practitioners.Some of the statis-tics associated with Mantel-Haenszel procedure can be performed using specific software such asEZ-DIF (Waller, 1998), DIFAS (Penfield, 2005), and MH-DIF (Fidalgo, 1994). However, usingthese programs requires being familiar with the statistics of DIF procedures in order to understandoutput. In addition, key characteristics of the Mantel-Haenszel procedures, such as the matchingstrategy (thick or thin), purification of the matching criteria, and so on, are not currently availablein all statistical software packages. As a result, EASY-DIF was developed to provide easy-to-usesoftware for performing the most common and useful MH and standardization procedures, witha view to guiding practitioners through the analyses and helping them interpret the output.EASY-DIFanalyzesuniformandnonuniformDIFforthetotalsampleandseparatelyforlow-performing and high-performing groups (Clauser, Mazor, & Hambleton, 1994). Users canexplore possible cancellation and amplification DIF effects by establishing different cut scoresfor each group. Up to six different matching strategies based on total score distributions can beimplemented: thinmatching,equal interval, percentage of totalsample, percentage of focalsam-ple, censored matching, and minimum cell frequency (Donoghue & Allen, 1993). In addition,users can use a procedure purification of matching criteria through the selection of a validsubtest.For dichotomous items, EASY-DIF computes the Mantel-Haenszel chi-square (Holland T Mantel & Haenszel, 1959), the Mantel-Haenszel common odds ratio (CamilliSMantel&Haenszel,1959),theMH-DeltaDIF,thestandarderrorofMH-DeltaComputer Program Exchange

"As estatísticas englobadas sob a denominação métodos Mantel-Haenszel (MH), por suas simplicidades, baixo custo computacional e bons resultados, são os métodos mais empregadas para detectar o funcionamento diferencial dos itens (DIF). Os métodos MH podem ser usados para detectar o DIF tanto em itens dicotômicos como em itens politômicos, permitindo comparar dois ou mais grupos simultaneamente, e aplicam-se tanto no âmbito da Teoria Clássica dos Testes (TCT) como da Teoria da Resposta ao Item (TRI). Este artigo fornece uma visão completa e integrada dos métodos MH e apresenta um programa que permite aplicar essas estatísticas no estudo do DIF. O programa é gratuito e está disponível em Espanhol, Inglês e Português."

Addressing individual perceptions: An application of the unified theory of acceptance and use of technology to building information modelling

This study aims to determine the following: 1) The effect of work discipline on employee performance in the Regional Secretariat of Kota Pariaman. 2) The influence of competence on employee performance in the Regional Secretariat of Kota Pariaman. 3) The effect of integrity on employee performance in the Regional Secretariat of Kota Pariaman. 4) The effect of work discipline on employee integrity in the Regional Secretariat of Kota Pariaman. The data collection method is a part of the data collection instrument that determines the success or failure of a study. This type of research uses an associative quantitative approach. To get the data needed in this study, the authors used techniques in data collection. The data analysis technique used in this research is quantitative analysis. The types of data collected in this study were taken from two sources, namely primary data and secondary data. Hypothesis testing in this study was carried out by path analysis. Based on the results of analysis and discussion, it is known that 1) Work discipline has an effect on employee performance (Px1y) is 0.232 with a significance value of 0.007. 2) Job competence has a significant effect on employee performance (Px2y) is 0.462 with a significance value of 0.000. 3) Integrity has a significant effect on employee performance (Px3y) is 0.230 with a significance value of 0.009. 4) Work discipline has no significant effect on integrity (Px1x3) is 0.175 with a significance value of 0.096. 5) Competence has a significant effect on integrity (Px2x3) is 0.508 with a significance value of 0.000. Hypothesis testing in this study was carried out by path analysis. Based on the results of analysis and discussion, it is known that 1) Work discipline has an effect on employee performance (Px1y) is 0.232 with a significance value of 0.007. 2) Job competence has a significant effect on employee performance (Px2y) is 0.462 with a significance value of 0.000. 3) Integrity has a significant effect on employee performance (Px3y) is 0.230 with a significance value of 0.009. 4) Work discipline has no significant effect on integrity (Px1x3) is 0.175 with a significance value of 0.096. 5) Competence has a significant effect on integrity (Px2x3) is 0.508 with a significance value of 0.000. Hypothesis testing in this study was carried out by path analysis. Based on the results of analysis and discussion, it is known that 1) Work discipline has an effect on employee performance (Px1y) is 0.232 with a significance value of 0.007. 2) Job competence has a significant effect on employee performance (Px2y) is 0.462 with a significance value of 0.000. 3) Integrity has a significant effect on employee performance (Px3y) is 0.230 with a significance value of 0.009. 4) Work discipline has no significant effect on integrity (Px1x3) is 0.175 with a significance value of 0.096. 5) Competence has a significant effect on integrity (Px2x3) is 0.508 with a significance value of 0.000. it is known that 1) Work discipline has an effect on employee performance (Px1y) is 0.232 with a significance value of 0.007. 2) Job competence has a significant effect on employee performance (Px2y) is 0.462 with a significance value of 0.000. 3) Integrity has a significant effect on employee performance (Px3y) is 0.230 with a significance value of 0.009. 4) Work discipline has no significant effect on integrity (Px1x3) is 0.175 with a significance value of 0.096. 5) Competence has a significant effect on integrity (Px2x3) is 0.508 with a significance value of 0.000. it is known that 1) Work discipline has an effect on employee performance (Px1y) is 0.232 with a significance value of 0.007. 2) Job competence has a significant effect on employee performance (Px2y) is 0.462 with a significance value of 0.000. 3) Integrity has a significant effect on employee performance (Px3y) is 0.230 with a significance value of 0.009. 4) Work discipline has no significant effect on integrity (Px1x3) is 0.175 with a significance value of 0.096. 5) Competence has a significant effect on integrity (Px2x3) is 0.508 with a significance value of 0.000. 230 with a significance value of 0.009. 4) Work discipline has no significant effect on integrity (Px1x3) is 0.175 with a significance value of 0.096. 5) Competence has a significant effect on integrity (Px2x3) is 0.508 with a significance value of 0.000. 230 with a significance value of 0.009. 4) Work discipline has no significant effect on integrity (Px1x3) is 0.175 with a significance value of 0.096. 5) Competence has a significant effect on integrity (Px2x3) is 0.508 with a significance value of 0.000.

This study aims to determine and analyze the effect of the monetary policy transmission mechanism on the interest rate channel and asset prices in Indonesia in achieving the ultimate goal of price stability throughout the period 2002:Q1 – 2020:Q4. This study uses secondary data related to policy interest rate variables, operational target interest rates, information variables, and final target variables for each channel of the monetary policy transmission mechanism. The analytical tool used in this research is the Vector Error Correction Model (VECM) which is processed using statistical software Eviews 10. The results of the analysis on the monetary policy transmission mechanism model of the interest rate channel indicate that in the short term, investment lending variables have a negative and significant effect on The CPI, meanwhile, the investment credit interest rate variable has a negative and significant effect. The results of the long-term analysis of the monetary policy transmission mechanism model of the interest rate channel show that the interest rate for investment credit, consumption credit, and the BI7DRR interest rate have a positive and significant effect on the CPI, while the variables for consumption credit interest rates, investment credit distribution, and real GDP negative and significant effect on the CPI. The results of the VECM analysis for the monetary policy transmission mechanism model for asset price channels confirm that the positive Error Correction Term (ECT) coefficient indicates that there is no adjustment mechanism towards long-term equilibrium. The results of the classical assumption test for the monetary policy transmission mechanism model of asset price channels also confirm deviations in the assumptions of normality and heteroscedasticity. Therefore, the economic interpretation of the VECM estimation on the monetary policy transmission mechanism model of the asset price channel cannot be carried out and requires the use of other analytical tools to produce a valid model .

Satellites can complement terrestrial networks in Internet service provision by addressing the market which lies out of their reach. Despite of the global coverage, satellite networks entail high propagation delays and usually limited user bandwidth. VoIP is an example of an application which is subject to performance impairments due to these characteristics. VoIP trials over existing broadband satellite offers reveal that the application can be carried out with an acceptable quality. However results also reveal the need for further improvement of the bandwidth on demand scheme in order to reduce delay or improve bandwidth utilization. This paper presents a novel capacity request mechanism based on the accurate modeling of VoIP traffic as well as cross-layer design. Our analysis and simulation results show that our approach outperforms fixed capacity allocation mechanisms in terms of bandwidth efficiency or traditional bandwidth on demand schemes in terms of QoS.

The revolution towards digitalization has changed most aspects of people's lives. Along with technological developments, new innovations are also needed that can make various activities more practical, efficient and comfortable, especially in the financial sector. New innovations are also needed, especially in the financial sector. The Covid-19 pandemic has limited all direct contact activities and has had an impact on changing people's habits towards the payment system from cash to non-cash. Technology Acceptance Model (TAM) is a research model conducted to analyze the factors that influence the use of information technology. This study aims to analyze the effect of perceived benefits, perceived convenience, perceived risk on interest in reusing Ovo e-wallet in this study using a quantitative research approach. The design of this study used survey measurements using a data collection tool using a questionnaire through a Likert scale. Based on the results of research that has been conducted online using Google Forms, questionnaires were distributed to respondents who have used the ovo e-wallet application at least 2 times or more than the last 6 months, then respondents are domiciled in DKI Jakarta and surrounding areas. Respondents who were collected in this study were 101 respondents, then the data was processed using the multiple linear regression method. The results of data analysis carried out with the help of SPSS software version 28.0.0.0 Based on the results of data analysis to measure the validity test in this study, the variables of perceived benefits, perceived convenience, perceived risk and interest in reusing OVO e-wallet were declared valid with significant values and reliability tests. by using Cronbach's Alpha coefficient that the four variables are declared reliable with a significance value. Then the classical assumption test is carried out which includes the Normality Test, Multicollinearity Test, and Heteroscedasticity Test. Based on the results of the analysis, it is known that the perceived benefits variable, perceived convenience simultaneously and partially have a positive and significant effect on the interest in reusing the ovo e-wallet, while the risk perception variable has no effect on interest. in reusing ovo e-wallet. Determination (R2) based on the value can be seen from the analysis of the effect of perceived benefits, perceived comfort, perceived risk of interest in reusing ovo e-wallet with a value of 0.524. The dependent variable to determine the results of the statistical test showed that the hypothesis was accepted, namely perceived benefits, perceived convenience, interest in reuse, while the hypothesis was rejected, namely perceived risk because it had no significant effect. Then from the analysis results obtained that the perceived benefit variable is the dominant variable..

Toward green transition in the post Paris Agreement era: The case of Taiwan

Harmonizing the scores obtained by different instruments that measure the same construct enable researchers to combine them in one analysis. An important step in harmonization is checking whether there is measurement invariance across populations. This study aimed to examine whether the harmonized scores for anxiety/depression and ADHD obtained by two different instruments (the Child Behaviour Check List (CBCL) and the Strength and Difficulties Questionnaire (SDQ)) are measurement invariant across other countries, languages, and age groups. We used cohorts from Australia (1330 children aged 10–11.5 years), the Netherlands (943 children aged 11–13.5 years) and the United Kingdom (4504 children aged 14–19). We used the Bayesian method for modeling measurement non-invariance proposed by Verhagen and Fox, 2013a that we adapted for using on polytomous items and in a relatively small number of groups (cohorts). Results showed that there is hardly any differential functioning of harmonized anxiety/depression and ADHD scores obtained by CBCL and SDQ across cohorts. The same model that harmonizes measures in Australian 10-year-old children can also be used in cohorts from the UK and the Netherlands.