Rasch Rating Scale Modeling of Data from the Standardized Letter of Recommendation
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A Short Note on the Rasch Model and Latent Trait Model Yen-Chi Chen University of Washington April 10, 2020
A short note on the Rasch model and latent trait model Yen-Chi Chen University of Washington April 10, 2020 The item response theory (IRT) is a popular model in psychometrics and educational statistics. In this note, we will briefly discuss the simplest IRT model called the Rasch model, which is named after Danish statistician Georg Rasch. A useful reference of this note is Chapter 12 of the following book: Sundberg, R. (2019). Statistical modelling by exponential families (Vol. 12). Cambridge Uni- versity Press. Suppose that we have i = 1;··· ;n individuals and j = 1;··· ;k items and the data is summarized as a n × k binary table/matrix Y = fYi jg, i.e., Yi j 2 f0;1g. The Rasch model assumes that every cell (i.e., Yi j) is independently drawn from a probability model: aib j P(Yi j = 1;a;b) = : 1 + aib j The parameter ai 2 R denotes the i-th individual’s ability and b j 2 R denotes the j-th item difficulty. The goal is to infer the underlying parameters from the observed data table Y. 1 Joint Rasch Model The Rasch model aims at finding both a and b jointly. Since the Rasch model is essentially a collection of Bernoulli random variables, the joint PDF is n k (a b )yi j p(y;a;b) = ∏∏ i j ; i=1 j=1 1 + aib j which belongs to the exponential family. Thus, after reparametrization, we obtain n k yi j p(y;a;b) ∝ ∏∏(aib j) i=1 j=1 n ! k ! k y n ∑ j=1 i j ∑i=1 yi j = ∏ai ∏ b j i=1 j=1 n ! k ! yi+ y+ j = ∏ai ∏ b j ; i=1 j=1 1 where k n yi+ = ∑ yi j; y+ j = ∑ yi j j=1 i=1 are the row sum and column sum of the table y. -
Statistics Anxiety Rating Scale (STARS) Use in Psychology Students: a Review and Analysis with an Undergraduate Sample Rachel J
DARTP bursary winners Statistics Anxiety Rating Scale (STARS) use in Psychology students: A review and analysis with an undergraduate sample Rachel J. Nesbit & Victoria J. Bourne Statistics anxiety is extremely common in undergraduate psychology students. The Statistics Anxiety Rating Scale (STARS) is at present the most widely used measure to assess statistics anxiety, measuring six distinct scales: test and class anxiety, interpretation anxiety, fear of asking for help, worth of statistics, fear of statistics teachers and computational self-concept. In this paper we first review the existing research that uses the STARS with psychology undergraduates. We then provide an analysis of the factor and reliability analysis of the STARS measure using a sample of undergraduate psychology students (N=315). Factor analysis of the STARS yielded nine factors, rather than the six it is intended to measure, with some items indicating low reliability, as demonstrated by low factor loadings. On the basis of these data, we consider the further development and refinement of measures of statistics anxiety in psychology students. Keywords: STARS, psychology, statistics anxiety. Introduction asking for help. The second section of the N THE UK, psychology is an increas- STARS measures attitudes towards statis- ingly popular subject for undergrad- tics and consists of three scales (28 items): Iuate students, with more than 80,000 worth of statistics, fear of statistics tutors and undergraduate students across the UK in computational self-concept. 2016–2017 (HESA). Despite the popularity The initial factor structure of the STARS of psychology in higher education many identified six distinct factors (Cruise et new entrants are unaware of the statistical al., 1985), as did the UK adaption (Hanna components of their course (Ruggeri et al., et al., 2008), validating the six scales 2008). -
The Rasch-Rating Scale Model to Identify Learning Difficulties of Physics Students Based on Self-Regulation Skills
International Journal of Evaluation and Research in Education (IJERE) Vol. 8, No. 4, December 2019, pp. 659~665 ISSN: 2252-8822, DOI: 10.11591/ijere.v8i4.20292 659 The Rasch-rating scale model to identify learning difficulties of physics students based on self-regulation skills Habibi Habibi1, Jumadi Jumadi2, Mundilarto Mundilarto3 1Doctor Candidate, Universitas Negeri Yogyakarta, Indonesia 1Department of Physics Education, IKIP Mataram (Undikma), Indonesia 2,3Department of Physics Education, Universitas Negeri Yogyakarta, Indonesia Article Info ABSTRACT Article history: This study applies the unidimensional Rasch Model assumptions in identifying physics learning difficulties based on students' self-regulation Received Aug 16, 2019 abilities. A total of 126 physics teacher candidates have been observed for Revised Oct 24, 2019 one semester. Self-project as a learning strategy has been used. Data were Accepted Nov 12, 2019 collected using 20 items in rating scales and then analyzed quantitatively to get feasibility in measuring self-regulation skills. The results have shown that the profile items analyzed by the Rasch Model are feasible to measure self- Keywords: regulation skills through a self-sustaining project strategy. Most physics teacher candidates have a medium ability of 51% in the process of self- Rasch-model regulation, high = 33%, and low = 16%. The implications of applying self- Rating scales projects to the processes of self-regulation are discussed in this article. Self-regulation Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Habibi Habibi, Department of Physics Education, IKIP Mataram (Undikma), Indonesia Pemuda Street No. 59A, Mataram, Postcode: 83125, Indonesia Email: [email protected] 1. -
Scale Construction Utilising the Rasch Unidimensional Measurement Model: a Measurement of Adolescent Attitudes Towards Abortion
Australasian Medical Journal [AMJ 2012, 5, 5, 251-261] Scale construction utilising the Rasch unidimensional measurement model: A measurement of adolescent attitudes towards abortion Jacqueline Hendriks 1,2 , Sue Fyfe 1, Irene Styles 3, S. Rachel Skinner 2,4 , Gareth Merriman 1 1. School of Public Health, Curtin University, Australia 2. School of Paediatrics and Child Health, University of Western Australia, Australia 3. Graduate School of Education, University of Western Australia, Australia 4. Sydney University Discipline of Paediatrics and Child Health, Children’s Hospital at Westmead, Australia Results RESEARCH Rasch analysis of the original dataset initially demonstrated Please cite this paper as: Hendriks J, Fyfe S, Styles I, Skinner that some items did not fit the model. Rescoring of one item SR, Merriman G. Scale construction utilising the Rasch (B5) and removal of another (L31) resulted in fit, as shown unidimensional measurement model: A measurement of by a non-significant item-trait interaction total chi-square adolescent attitudes towards abortion. AMJ 2012, 5, 5, 251- and a mean log residual fit statistic for items of -0.05 261. http//dx.doi.org/10.4066/AMJ.2012.952. (SD=1.43). No DIF existed for the revised scale. However, items did not distinguish as well amongst persons with the Corresponding Author: most intense attitudes as they did for other persons. A Jacqueline Hendriks person separation index of 0.82 indicated good reliability. School of Public Health, Curtin University Conclusion GPO Box U1987 Application of the Rasch model produced a valid and Perth, Western Australia 6845 reliable scale measuring adolescent attitudes towards Australia abortion, with stable measurement properties. -
Design of Rating Scales in Questionnaires
GESIS Survey Guidelines Design of Rating Scales in Questionnaires Natalja Menold & Kathrin Bogner December 2016, Version 2.0 Abstract Rating scales are among the most important and most frequently used instruments in social science data collection. There is an extensive body of methodological research on the design and (psycho)metric properties of rating scales. In this contribution we address the individual scale-related aspects of questionnaire construction. In each case we provide a brief overview of the current state of research and practical experience, and – where possible – offer design recommendations. Citation Menold, N., & Bogner, K. (2016). Design of Rating Scales in Questionnaires. GESIS Survey Guidelines. Mannheim, Germany: GESIS – Leibniz Institute for the Social Sciences. doi: 10.15465/gesis-sg_en_015 This work is licensed under a Creative Commons Attribution – NonCommercial 4.0 International License (CC BY-NC). 1. Introduction Since their introduction by Thurstone (1929) and Likert (1932) in the early days of social science research in the late 1920s and early 1930s, rating scales have been among the most important and most frequently used instruments in social science data collection. A rating scale is a continuum (e.g., agreement, intensity, frequency, satisfaction) with the help of which different characteristics and phenomena can be measured in questionnaires. Respondents evaluate the content of questions and items by marking the appropriate category of the rating scale. For example, the European Social Survey (ESS) question “All things considered, how satisfied are you with your life as a whole nowadays?” has an 11-point rating scale ranging from 0 (extremely dissatisfied) to 10 (extremely satisfied). -
Downloaded the Reviews Related to the Food and the Service of the Restaurant from This Website
Multimodal Technologies and Interaction Article Decision Aids in Online Review Portals: An Empirical Study Investigating Their Effectiveness in the Sensemaking Process of Online Information Consumers Amal Ponathil 1,* , Anand Gramopadhye 2 and Kapil Chalil Madathil 1,2 1 Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA 2 Department of Industrial Engineering, Clemson University, Clemson, SC 29634, USA * Correspondence: [email protected] Received: 18 March 2020; Accepted: 17 June 2020; Published: 23 June 2020 Abstract: There is an increasing concern about the trustworthiness of online reviews as there is no editorial process for verification of their authenticity. This study investigated the decision-making process of online consumers when reacting to a review, with the reputation score of the reviewer and the number of previous reviews incorporated along with anonymous and non-anonymous reviews. It recruited 200 participants and developed a 3 2 2 2 2 mixed experimental study, with the × × × × independent variables being the reaction to a review of a restaurant at 3 levels, the reputation score at 2 levels, the number of previous reviews at 2 levels, the valence of the reviews at 2 levels, and the level of anonymity at 2 levels. Five dependent variables were analyzed: level of trust, likelihood of going to the restaurant, a choice question of whether to go to the restaurant, confidence in the decision and the NASA-TLX workload. This study found that the reputation scores complemented the reaction to a review, improving the trust in the information and confidence in the decision made. The findings suggest that incorporating a user rating scale such as the reputation score of a user deters people from writing false or biased reviews and helps improve their accuracy. -
Converting Likert Scales Into Behavioral Anchored Rating
Journal of University Teaching & Learning Practice Volume 16 | Issue 3 Article 9 2019 Converting Likert Scales Into Behavioral Anchored Rating Scales(Bars) For The vE aluation of Teaching Effectiveness For Formative Purposes Luis Matosas-López Rey Juan Carlos University, [email protected] Santiago Leguey-Galán Rey Juan Carlos University, [email protected] Luis Miguel Doncel-Pedrera Rey Juan Carlos University, [email protected] Follow this and additional works at: https://ro.uow.edu.au/jutlp Recommended Citation Matosas-López, Luis; Leguey-Galán, Santiago; and Doncel-Pedrera, Luis Miguel, Converting Likert Scales Into Behavioral Anchored Rating Scales(Bars) For The vE aluation of Teaching Effectiveness For Formative Purposes, Journal of University Teaching & Learning Practice, 16(3), 2019. Available at:https://ro.uow.edu.au/jutlp/vol16/iss3/9 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: [email protected] Converting Likert Scales Into Behavioral Anchored Rating Scales(Bars) For The vE aluation of Teaching Effectiveness For Formative Purposes Abstract Likert scales traditionally used in student evaluations of teaching (SET) suffer from several shortcomings, including psychometric deficiencies or ambiguity problems in the interpretation of the results. Assessment instruments with Behavioral Anchored Rating Scales (BARS) offer an alternative to Likert-type questionnaires. This paper describes the construction of an appraisal tool with BARS generated with the participation of 974 students and 15 teachers. The er sulting instrument eliminates ambiguity in the interpretation of results and gives objectivity to the evaluation due to the use of unequivocal behavioral examples in the final scale. -
Rasch Model to Measure Change 1 Applying the Rasch Model to Measure Change in Student Performance Over Time Jessica D. Cunningha
Rasch Model to Measure Change Applying the Rasch Model to Measure Change in Student Performance over Time Jessica D. Cunningham; Western Carolina University Kelly D. Bradley1; University of Kentucky 1 Please use Kelly D. Bradley as author of contact. 131 Taylor Education Building, Lexington, KY 40506- 0001, [email protected] or (859)257-4923 1 Rasch Model to Measure Change Applying the Rasch Model to Measure Change in Student Performance over Time Abstract Newton’s Universe (NU), an innovative teacher training program, strives to obtain measures from rural, middle school science teachers and their students to determine the impact of its distance learning course on understanding of temperature. Although limited research exists examining the usefulness of the Rasch model in measuring change over time, this study applied the method of racking and stacking the NU data to examine change in student performance over time. Locally, researchers involved in the NU course development can use these findings to further improve the course and the student assessment instrument. Researchers across various fields can use the methods shown in this study to improve assessment instruments, course development, and measures of change over time. DESCRIPTORS: Rasch Model, Racking and Stacking, Measuring Change in Student Performance, Newton’s Universe teacher training 2 Rasch Model to Measure Change Applying the Rasch Model to Measure Change in Student Performance over Time Challenges regarding student achievement in math and science education remain an issue for rural educators in general and particularly in the rural regions of Appalachia. Newton’s Universe (NU)2 is an inquiry-based teacher training program striving to obtain measures from rural, middle school science teachers and their students to determine the impact of its distance learning course on understanding of temperature. -
UCLA Electronic Theses and Dissertations
UCLA UCLA Electronic Theses and Dissertations Title Identifying DIF for Latent Classes with the Dirichlet Process Permalink https://escholarship.org/uc/item/5h24c5k6 Author Chen, Miles S. Publication Date 2015 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California University of California Los Angeles Identifying DIF for Latent Classes with the Dirichlet Process A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics by Miles Satori Chen 2015 c Copyright by Miles Satori Chen 2015 Abstract of the Dissertation Identifying DIF for Latent Classes with the Dirichlet Process by Miles Satori Chen Doctor of Philosophy in Statistics University of California, Los Angeles, 2015 Professor Peter Bentler, Chair In Item Response Theory (IRT), Differential Item Functioning (DIF) occurs when individuals who have the same ability, but belong to different groups, have differ- ent probabilities of answering an item correctly. Traditional DIF methods require that the grouping variable be observable, like gender or ethnicity. Latent class IRT, on the other hand, allows for the fitting of IRT models where the grouping variable is unobserved. Current latent class IRT methods (e.g. mixed Rasch models) require that the number of mixing components be defined in the estimation process. This dissertation proposes two latent class models, each with a Markov chain Monte Carlo algorithm, that can be used to fit IRT data without the need to specify the number of latent classes. The models employ a Dirichlet process or stick-breaking prior to allow an undefined number of mixing components to be fit. -
What Is Item Response Theory?
What is Item Response Theory? Nick Shryane Social Statistics Discipline Area University of Manchester [email protected] 1 What is Item Response Theory? 1. It’s a theory of measurement, more precisely a psychometric theory. – ‘Psycho’ – ‘metric’. • From the Greek for ‘mind/soul’ – ‘measurement’. 2. It’s a family of statistical models. 2 Why is IRT important? • It’s one method for demonstrating reliability and validity of measurement. • Justification, of the sort required for believing it when... – Someone puts a thermometer in your mouth then says you’re ill... – Someone puts a questionnaire in your hand then says you’re post-materialist – Someone interviews you then says you’re self- actualized 3 This talk will cover • A familiar example of measuring people. • IRT as a psychometric theory. – ‘Rasch’ measurement theory. • IRT as a family of statistical models, particularly: – A ‘one-parameter’ or ‘Rasch’ model. – A ‘two-parameter’ IRT model. • Resources for learning/using IRT 4 Measuring body temperature Using temperature to indicate illness Measurement tool: a mercury thermometer - a glass vacuum tube with a bulb of mercury at one end. 5 Measuring body temperature Thermal equilibrium Stick the bulb in your mouth, under your tongue. The mercury slowly heats up, matching the temperature of your mouth. 6 Measuring body temperature Density – temperature proportionality Mercury expands on heating, pushing up into the tube. Marks on the tube show the relationship between mercury density and an abstract scale of temperature. 7 Measuring body temperature Medical inference Mouth temperature is assumed to reflect core body temperature, which is usually very stable. -
Chapter 5 Explanatory Item Response Models: a Brief Introduction
Chapter 5 Explanatory Item Response Models: A Brief Introduction Mark Wilson, Paul De Boeck, and Claus H. Carstensen In this chapter we illustrate the use of item response models to analyze data result- ing from the measurement of competencies. These item response models deal with data from an instrument such as a test, an inventory, an attitude scale, or a question- naire – we will call this “test data”. The main purpose of using item response models is the measurement of propensities: specifically, abilities, skills, achievement levels, traits, attitudes, etc. In standard item response modeling each item is modeled with its own set of parameters and in one way or another, the person parameters are also esti- mated – we will call this the “measurement” approach. In a broader approach, which does not contradict but complements the measurement approach, one might want to model how the properties of the items and the persons making the responses lead to the persons’ responses – we will call this the “explanatory” approach (De Boeck & Wilson, 2004). Understanding how the item responses are generated implies that the responses can be explained in terms of properties of items and of persons. We also take a broader statistical approach than that of standard item response models. It turns out that most extant item response models are special cases of what are called generalized linear or nonlinear mixed models (GLMMs and NLMMs; McCulloch & Searle, 2001). In this chapter, the perspective that is taken on these models is quite sim- ple in its general expression (although it is not necessarily simple in how it is applied to a specific situation) and it has several advantages. -
Overview of Classical Test Theory and Item Response Theory for the Quantitative Assessment of Items in Developing Patient-Reported Outcomes Measures
UCLA UCLA Previously Published Works Title Overview of classical test theory and item response theory for the quantitative assessment of items in developing patient-reported outcomes measures. Permalink https://escholarship.org/uc/item/5zw8z6wk Journal Clinical therapeutics, 36(5) ISSN 0149-2918 Authors Cappelleri, Joseph C Jason Lundy, J Hays, Ron D Publication Date 2014-05-05 DOI 10.1016/j.clinthera.2014.04.006 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Clinical Therapeutics/Volume 36, Number 5, 2014 Commentary Overview of Classical Test Theory and Item Response Theory for the Quantitative Assessment of Items in Developing Patient-Reported Outcomes Measures Joseph C. Cappelleri, PhD1; J. Jason Lundy, PhD2; and Ron D. Hays, PhD3 1Pfizer Inc, Groton, Connecticut; 2Critical Path Institute, Tucson, Arizona; and 3Division of General Internal Medicine & Health Services Research, University of California at Los Angeles, Los Angeles, California ABSTRACT Conclusion: Classical test theory and IRT can be useful in providing a quantitative assessment of items Background: The US Food and Drug Administra- and scales during the content-validity phase of PRO- ’ tion s guidance for industry document on patient- measure development. Depending on the particular fi reported outcomes (PRO) de nes content validity as type of measure and the specific circumstances, the “ the extent to which the instrument measures the classical test theory and/or the IRT should be consid- ” concept of interest (FDA, 2009, p. 12). According ered to help maximize the content validity of PRO to Strauss and Smith (2009), construct validity "is measures. (Clin Ther. 2014;36:648–662) & 2014 now generally viewed as a unifying form of validity Elsevier HS Journals, Inc.