Rethinking the Exploration of Dichotomous Data: Mokken Scale Analysis Versus Factorial Analysis

2018 ◽  
Vol 49 (4) ◽  
pp. 839-867 ◽  
Author(s):  
Mirko Antino ◽  
Jesús M. Alvarado ◽  
Rodrigo A. Asún ◽  
Paul Bliese
Keyword(s):  
Simulated Data ◽  
Principal Component ◽  
Data Sets ◽  
Scale Analysis ◽  
Analysis Model ◽  
Measurement Instruments ◽  
Mokken Scale Analysis ◽  
Dichotomous Data ◽  
The Social ◽  
Item Factor Analysis

The need to determine the correct dimensionality of theoretical constructs and generate valid measurement instruments when underlying items are categorical has generated a significant volume of research in the social sciences. This article presents two studies contrasting different categorical exploratory techniques. The first study compares Mokken scale analysis (MSA) and two-factor-based exploratory techniques for noncontinuous variables: item factor analysis and Normal Ogive Harmonic Analysis Robust Method (NOHARM). Comparisons are conducted across techniques and in reference to the common principal component analysis model using simulated data under conditions of two-dimensionality with different degrees of correlation ( r = .0 to .6). The second study shows the theoretical and practical results of using MSA and NOHARM (the factorial technique which functioned best in the first study) on two nonsimulated data sets. The nonsimulated data are particularly interesting because MSA was used to solve a theoretical debate. Based on the results from both studies, we show that the ability of NOHARM to detect dimensionality and scalability is similar to MSA when the data comprise two uncorrelated latent dimensions; however, NOHARM is preferable when data are drawn from instruments containing latent dimensions weakly or moderately correlated. This article discusses the theoretical and practical implications of these findings.

scholarly journals IMPROVED ESTIMATION OF COVARIANCE MATRIX IN HOTELLING’S T2 FOR MICROARRAY DATA

2016 ◽  
Vol 8 (2) ◽  
pp. 26 ◽  
Author(s):  
Suryaefiza Karjanto ◽  
Norazan Mohamed Ramli ◽  
Nor Azura Md Ghaninor Azura Md Ghani
Keyword(s):  
Covariance Matrix ◽  
Microarray Data ◽  
Simulated Data ◽  
Principal Component ◽  
Data Sets ◽  
Similar Data ◽  
Shrinkage Methods ◽  
Differential Gene ◽  
The Relationship ◽  
Simulated Data Sets

<p class="lead">The relationship between genes in gene set analysis in microarray data is analyzed using Hotelling’s <em>T</em><sup>2</sup> but the test cannot be applied when the number of samples is larger than the number of variables which is uncommon in the microarray. Thus, in this study, we proposed shrinkage approaches to estimating the covariance matrix in Hotelling’s <em>T<sup>2</sup></em> particularly to cater high dimensionality problem in microarray data. Three shrinkage covariance methods were proposed in this study and are referred as Shrink A, Shrink B and Shrink C. The analysis of the three proposed shrinkage methods was compared with the Regularized Covariance Matrix Approach and Kong’s Principal Component Analysis. The performances of the proposed methods were assessed using several cases of simulated data sets. In many cases, the Shrink A method performed the best, followed by the Shrink C and RCMAT methods. In contrast, both the Shrink B and KPCA methods showed relatively poor results. The study contributes to an establishment of modified multivariate approach to differential gene expression analysis and expected to be applied in other areas with similar data characteristics.</p>

scholarly journals Exploring Within-Rater Category Ordering: A Simulation Study Using Adjacent-Categories Mokken Scale Analysis

2017 ◽  
Vol 78 (5) ◽  
pp. 887-904 ◽  
Author(s):  
Stefanie A. Wind ◽  
Randall E. Schumacker
Keyword(s):  
Rating Scale ◽  
Simulated Data ◽  
Real Data ◽  
Performance Assessments ◽  
Theory And Practice ◽  
Educational Performance ◽  
Scale Analysis ◽  
Mokken Scale Analysis ◽  
Measurement Models ◽  
Scale Category

The interpretation of ratings from educational performance assessments assumes that rating scale categories are ordered as expected (i.e., higher ratings correspond to higher levels of judged student achievement). However, this assumption must be verified empirically using measurement models that do not impose ordering constraints on the rating scale category thresholds, such as item response theory models based on adjacent-categories probabilities. This study considers the application of an adjacent-categories formulation of polytomous Mokken scale analysis (ac-MSA) models as a method for evaluating the degree to which rating scale categories are ordered as expected for individual raters in performance assessments. Using simulated data, this study builds on the preliminary application of ac-MSA models to rater-mediated performance assessments, in which a real data analysis suggested that these models can be used to identify disordered rating scale categories. The results suggested that ac-MSA models are sensitive to disordered categories within individual raters. Implications are discussed as they relate to research, theory, and practice for rater-mediated educational performance assessments.

scholarly journals Gap-Filling of NDVI Satellite Data Using Tucker Decomposition: Exploiting Spatio-Temporal Patterns

2021 ◽  
Vol 13 (19) ◽  
pp. 4007
Author(s):  
Andri Freyr Þórðarson ◽  
Andreas Baum ◽  
Mónica García ◽  
Sergio M. Vicente-Serrano ◽  
Anders Stockmarr
Keyword(s):  
Missing Data ◽  
Iberian Peninsula ◽  
Satellite Data ◽  
Simulated Data ◽  
Principal Component ◽  
Decomposition Methods ◽  
Data Sets ◽  
Gap Filling ◽  
Data Set ◽  
Spatio Temporal

Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tucker to NDVI satellite data from the Iberian Peninsula in order to gap-fill missing information. EM Tucker belongs to a family of tensor decomposition methods that are known to offer a number of interesting properties, including the ability to directly analyze data stored in multidimensional arrays and to explicitly exploit their multiway structure, which is lost when traditional spatial-, temporal- and spectral-based methods are used. In order to evaluate the gap-filling accuracy of EM Tucker for NDVI images, we used three data sets based on advanced very-high resolution radiometer (AVHRR) imagery over the Iberian Peninsula with artificially added missing data as well as a data set originating from the Iberian Peninsula with natural missing data. The performance of EM Tucker was compared to a simple mean imputation, a spatio-temporal hybrid method, and an iterative method based on principal component analysis (PCA). In comparison, imputation of the missing data using EM Tucker consistently yielded the most accurate results across the three simulated data sets, with levels of missing data ranging from 10 to 90%.

scholarly journals Confidence Intervals and Power Calculations for Within-Person Biological Variation: Effect of Analytical Imprecision, Number of Replicates, Number of Samples, and Number of Individuals

2012 ◽  
Vol 58 (9) ◽  
pp. 1306-1313 ◽  
Author(s):  
Thomas Røraas ◽  
Per H Petersen ◽  
Sverre Sandberg
Keyword(s):  
Simulated Data ◽  
Biological Variation ◽  
Fixed Number ◽  
Data Sets ◽  
Analysis Model ◽  
Reference Change Value ◽  
Reliability Estimates ◽  
Balanced Designs ◽  
Analytical Imprecision ◽  
Study Designs

Abstract BACKGROUND Reliable estimates of within-person biological variation and reference change value are of great importance when interpreting test results, monitoring patients, and setting quality specifications. Little information has been published regarding what experimental design is optimal to achieve the best estimates of within-person biological variation. METHOD Expected CIs were calculated for different balanced designs for a 2-level nested variance analysis model with varying analytical imprecision. We also simulated data sets based on the model to calculate the power of different study designs for detection of within-person biological variation. RESULTS The reliability of an estimate for biological variation and a study's power is very much influenced by the study design and by the ratio between analytical imprecision and within-person biological variation. For a fixed number of measurements, it is preferable to have a high number of samples from each individual. Shortcomings in analytical imprecision can be controlled by increasing the number of replicates. CONCLUSIONS The design of an experiment to estimate biological variation should take into account the analytical imprecision of the method and focus on obtaining the highest possible reliability. Estimates of biological variation should always be reported with CIs.

scholarly journals Exploring Incomplete Rating Designs With Mokken Scale Analysis

2016 ◽  
Vol 78 (2) ◽  
pp. 319-342 ◽  
Author(s):  
Stefanie A. Wind ◽  
Yogendra J. Patil
Keyword(s):  
Writing Assessment ◽  
Simulated Data ◽  
Real Data ◽  
Performance Assessments ◽  
Limiting Factor ◽  
Scale Analysis ◽  
Missing Data Imputation ◽  
Mokken Scale Analysis ◽  
Nonparametric Approach ◽  
Simple Imputation

Recent research has explored the use of models adapted from Mokken scale analysis as a nonparametric approach to evaluating rating quality in educational performance assessments. A potential limiting factor to the widespread use of these techniques is the requirement for complete data, as practical constraints in operational assessment systems often limit the use of complete rating designs. In order to address this challenge, this study explores the use of missing data imputation techniques and their impact on Mokken-based rating quality indicators related to rater monotonicity, rater scalability, and invariant rater ordering. Simulated data and real data from a rater-mediated writing assessment were modified to reflect varying levels of missingness, and four imputation techniques were used to impute missing ratings. Overall, the results indicated that simple imputation techniques based on rater and student means result in generally accurate recovery of rater monotonicity indices and rater scalability coefficients. However, discrepancies between violations of invariant rater ordering in the original and imputed data are somewhat unpredictable across imputation methods. Implications for research and practice are discussed.

scholarly journals Foreign E-commerce Practitioners in “Taobao Village”: Isolation and Integration

2021 ◽  
Vol 5 (1) ◽  
pp. 40
Author(s):  
Wang Zeyu
Keyword(s):  
Social Integration ◽  
Field Survey ◽  
Social Factor ◽  
Principal Component ◽  
Analysis Model ◽  
Individual Factor ◽  
Logistic Analysis ◽  
Time Space ◽  
The Social ◽  
Life Factor

This research mainly analyzes the actual situation of social integration of migrant E-commerce practitioners in Xintang Town of Guangdong Province through field survey. According to the survey, the overall level of social integration is relatively low, and there are three isolation barriers, namely time, space and psychology.Regarding the influencing factors of social integration level, it is concluded through the principal component analysis that five main factors impacting the social integration of migrant E-commerce practitioners are life factor, business factor, housing factor, social factor and individual factor. By establish a multiple logistic analysis model, it is found that type of friends, interest protection and frequency of recreational activities would impact the social integration of migrant E-commerce practitioners most significantly among all factors.

Evaluating Social Participation of Pupils with Special Needs in Regular Primary Schools

2009 ◽  
Vol 25 (4) ◽  
pp. 213-222 ◽  
Author(s):  
Marloes Koster ◽  
Marieke E. Timmerman ◽  
Han Nakken ◽  
Sip Jan Pijl ◽  
Els J. van Houten
Keyword(s):  
Special Needs ◽  
Social Participation ◽  
Primary Schools ◽  
Ordinal Scale ◽  
Response Analysis ◽  
Scale Analysis ◽  
Social Self ◽  
Mokken Scale Analysis ◽  
Item Functioning ◽  
The Social

The study addresses the psychometric qualities of a new teacher questionnaire, the Social Participation Questionnaire (SPQ), to assess the social participation of pupils with special needs in regular primary education. The SPQ initially consisted of 34 statements related to four key themes of social participation: “friendships/relationships,” “contacts/interactions,” “pupil’s social self-perception,” and “acceptance by classmates,” yielding four respective subscales. A nonparametric item response analysis (Mokken scale analysis) was used to examine the quality of the SPQ. Based on the Mokken scale analysis results, ten statements were removed. The resulting four subscales appeared intermediate to strong. Because the double monotonicity model, based on the Mokken scale analysis, turned out to be well-fitted for each subscale, the subscale scores are on an ordinal scale, and the separate statements are invariantly ordered. The subscale scores are comparable across pupils with and without special needs, differential item functioning appearing to be absent. Subsequent analyses supported the division of statements into the four subscales. The SPQ as a whole and its subscales were found to be reliable. Finally, as regards social participation, differences between pupils with and without special needs were clearly found.

An Input–Process–Output Model of Pilot Core Competencies

2017 ◽  
Vol 7 (2) ◽  
pp. 78-85 ◽  
Author(s):  
Heikki Mansikka ◽  
Don Harris ◽  
Kai Virtanen
Keyword(s):  
Path Analysis ◽  
Team Performance ◽  
Core Competencies ◽  
Core Competency ◽  
Principal Component ◽  
Analysis Model ◽  
Input Process ◽  
The Core ◽  
Performance Framework ◽  
Process Output

Abstract. The aim of this study was to investigate the relationship between the flight-related core competencies for professional airline pilots and to structuralize them as components in a team performance framework. To achieve this, the core competency scores from a total of 2,560 OPC (Operator Proficiency Check) missions were analyzed. A principal component analysis (PCA) of pilots’ performance scores across the different competencies was conducted. Four principal components were extracted and a path analysis model was constructed on the basis of these factors. The path analysis utilizing the core competencies extracted adopted an input–process–output’ (IPO) model of team performance related directly to the activities on the flight deck. The results of the PCA and the path analysis strongly supported the proposed IPO model.

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

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