Identifying the Component Structure of Satisfaction Scales by Nonlinear Principal Components Analysis
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Education Quarterly Reviews
Education Quarterly Reviews Allanson, Patricia E., and Notar, Charles E. (2020), Statistics as Measurement: 4 Scales/Levels of Measurement. In: Education Quarterly Reviews, Vol.3, No.3, 375-385. ISSN 2621-5799 DOI: 10.31014/aior.1993.03.03.146 The online version of this article can be found at: https://www.asianinstituteofresearch.org/ Published by: The Asian Institute of Research The Education Quarterly Reviews is an Open Access publication. It May be read, copied, and distributed free of charge according to the conditions of the Creative ComMons Attribution 4.0 International license. The Asian Institute of Research Education Quarterly Reviews is a peer-reviewed International Journal. The journal covers scholarly articles in the fields of education, linguistics, literature, educational theory, research, and methodologies, curriculum, elementary and secondary education, higher education, foreign language education, teaching and learning, teacher education, education of special groups, and other fields of study related to education. As the journal is Open Access, it ensures high visibility and the increase of citations for all research articles published. The Education Quarterly Reviews aiMs to facilitate scholarly work on recent theoretical and practical aspects of education. The Asian Institute of Research Education Quarterly Reviews Vol.3, No.3, 2020: 375-385 ISSN 2621-5799 Copyright © The Author(s). All Rights Reserved DOI: 10.31014/aior.1993.03.03.146 Statistics as Measurement: 4 Scales/Levels of Measurement 1 2 Patricia E. Allanson , Charles E. Notar 1 Liberty University 2 Jacksonville State University. EMail: [email protected] (EMeritus) Abstract This article discusses the basics of the “4 scales of MeasureMent” and how they are applicable to research or everyday tools of life. -
Improving Your Exploratory Factor Analysis for Ordinal Data: a Demonstration Using FACTOR
Practical Assessment, Research, and Evaluation Volume 19 Volume 19, 2014 Article 5 2014 Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR James Baglin Follow this and additional works at: https://scholarworks.umass.edu/pare Recommended Citation Baglin, James (2014) "Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR," Practical Assessment, Research, and Evaluation: Vol. 19 , Article 5. DOI: https://doi.org/10.7275/dsep-4220 Available at: https://scholarworks.umass.edu/pare/vol19/iss1/5 This Article is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Practical Assessment, Research, and Evaluation by an authorized editor of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Baglin: Improving Your Exploratory Factor Analysis for Ordinal Data: A De A peer-reviewed electronic journal. Copyright is retained by the first or sole author, who grants right of first publication to Practical Assessment, Research & Evaluation. Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms. Volume 19, Number 5, June 2014 ISSN 1531-7714 Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR James Baglin, RMIT University, Melbourne, Australia Exploratory factor analysis (EFA) methods are used extensively in the field of assessment and evaluation. Due to EFA’s widespread use, common methods and practices have come under close scrutiny. -
A Practical Guide to Instrument Development and Score Validation in the Social Sciences: the MEASURE Approach
Practical Assessment, Research, and Evaluation Volume 26 Article 1 2021 A Practical Guide to Instrument Development and Score Validation in the Social Sciences: The MEASURE Approach Michael T. Kalkbrenner New Mexico State University - Main Campus Follow this and additional works at: https://scholarworks.umass.edu/pare Recommended Citation Kalkbrenner, Michael T. (2021) "A Practical Guide to Instrument Development and Score Validation in the Social Sciences: The MEASURE Approach," Practical Assessment, Research, and Evaluation: Vol. 26 , Article 1. DOI: https://doi.org/10.7275/svg4-e671 Available at: https://scholarworks.umass.edu/pare/vol26/iss1/1 This Article is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Practical Assessment, Research, and Evaluation by an authorized editor of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. A Practical Guide to Instrument Development and Score Validation in the Social Sciences: The MEASURE Approach Cover Page Footnote Special thank you to my colleague and friend, Ryan Flinn, for his consultation and assistance with copyediting. This article is available in Practical Assessment, Research, and Evaluation: https://scholarworks.umass.edu/pare/ vol26/iss1/1 Kalkbrenner: The MEASURE Approach A peer-reviewed electronic journal. Copyright is retained by the first or sole author, who grants right of first publication to Practical Assessment, Research & Evaluation. Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms. -
Measuring Learners' Attitudes Toward Team Projects: Scale Development
American Journal of Engineering Education – December 2017 Volume 8, Number 2 Measuring Learners’ Attitudes Toward Team Projects: Scale Development Through Exploratory And Confirmatory Factor Analyses Seung Youn (Yonnie) Chyung, Boise State University, USA Donald J. Winiecki, Boise State University, USA Gary Hunt, Boise State University, USA Carol M. Sevier, Boise State University, USA ABSTRACT Team projects are increasingly used in engineering courses. Students may develop attitudes toward team projects from prior experience, and their attitudinal responses could influence their performance during team project-based learning in the future. Thus, instructors need to measure students’ attitudes toward team projects during their learner analysis to better understand students’ characteristics and be proactive in providing effective strategies to help students succeed in a team project environment. The purpose of our study was to develop a survey instrument that measures students’ attitudes toward team projects to be used as a learner analysis tool, derived from our local practical needs and due to the lack of appropriate existing instruments. The study was conducted at a mid-sized university in the northwestern United States during 2015-2016. After we generated an initial pool of 50 items, we administered the survey to 225 undergraduate engineering students, performed exploratory factor analysis on the data, and arrived at a four-factor solution of 20 items and a three-factor solution of 14 items. We tested the two competing solutions with another set of 330 undergraduate engineering students. Based on our confirmatory factor analysis results, we arrived at a three-factor model of 12 items as the finalized scale, which measures: (a) professional capacity building, (b) learning and problem-solving skills development, and (c) workload challenges. -
Career Values Scale Manual and User's Guide Includes Bibliographic References
MANUAL & USER’S GUIDE DONALD MACNAB SHAWN BAKKER GEORGE FITZSIMMONS CVS-M Career Values Scale Manual and User's Guide Includes bibliographic references. © 2005 Psychometrics Publishing. All rights reserved. No portion of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Publisher. Published and distributed by Psychometrics Canada Ltd. 7125-77 Avenue, Edmonton AB T6B 0B5 CANADA www.psychometrics.com ISBN 0-929022-42-4 Printed in Canada Myers-Briggs Type Indicator and MBTI are registered trademarks of the Myers-Briggs Type Indicator Trust in the United States and other countries. CONTENTS Chapter 1 • Introduction Conceptualization of career values in the Career Values scale . 1 Overview of the Career Values Scale . 3 Chapter 2 • Administration Procedures . 6 Appropriate Populations for Administration . 7 Chapter 3 • Using the Career Values Scale Scoring and Reporting . 8 Illustrative Examples . 9 Exercises using the Career Values Scale . 12 Chapter 4 • Development & Norming of the Career Values Scale Development Process . 18 Scale development and item writing . 18 Norming of the CVS . 19 Sten Scores . 21 Means and Standard Deviations . 22 Chapter 5 • Reliability and Validity Reliability . 23 Validity . 25 Concurrent Validity . .25 Construct Validity Evidence . 29 Values and Occupational Interests . 28 Values and Personality . 28 Inter-correlation between WPI Scale . 30 Factor Analysis . 31 Feedback Study . 32 Bibliography • Research on Values . .33 Appendix A • Sample Report . .35 TABLES & FIGURES Chapter 1 Figure 1.1 Career Values Scale Factors and Scale . 3 Chapter 4 Table 4.1 Ethnic Origin of Norming Sample . -
Management Measurement Scale As a Reference to Determine Interval in a Variable
ATM, Vol 2, No. 1, January 2018 e-ISSN: 2622-6804 p-ISSN: 2622-6812 ■45 Management Measurement Scale As A Reference To Determine Interval In A Variable 1 2 3 Qurotul Aini , Siti Ria Zuliana , Nuke Puji Lestari Santoso 1-3STMIK Raharja, Jl. Jendral Sudirman No.40 Modernland, Cikokol, Tangerang e-mail: [email protected], [email protected], [email protected] Abstract The scale is usually used to check and determine the value of a qualitative factor in quantitative measures. The measurement scale is a management in agreement that is used as a reference to determine the short length of the interval that is in the measuring instrument, so that the measuring instrument when used in measurements will produce quantitative data. The results of the scale management calculation must be interpreted carefully because in addition to producing a rough picture, the respondent's answers are not just straightforward to be trusted. Types of measurement scales: Likert scale, Guttman scale, semantic differential scale, rating scale, Thurstone scale, Borgadus scale, and various other measurement management scales. One of the most difficult jobs for information technology researchers faced with the necessity of measuring variables is: finding directions in the midst of many existing sizes. If there is a good size for a particular variable, it seems that there are not many reasons to compile a new size yourself. Keywords: Scale, Measurement, Variables. 1. Introduction The scale is usually used to check and determine the value of a qualitative factor in quantitative measures. Scale is a tool that is compiled and used by researchers to change the response about a qualitative variable to quantitative data (Mahmud, 2011: 181) [1]. -
Summated Rating Scale Construction : an Introduction Sage University Papers Series
title : Summated Rating Scale Construction : An Introduction Sage University Papers Series. Quantitative Applications in the Social Sciences ; No. 07-082 author : Spector, Paul E. publisher : Sage Publications, Inc. isbn10 | asin : 0803943415 print isbn13 : 9780803943414 ebook isbn13 : 9780585217154 language : English subject Scaling (Social sciences) publication date : 1992 lcc : H61.27.S66 1992eb ddc : 300/.72 subject : Scaling (Social sciences) Summated Rating Scale Construction An Introduction SAGE UNIVERSITY PAPERS Series: Quantitative Applications in the Social Sciences Series Editor: Michael S. Lewis-Beck, University of Iowa Editorial Consultants Richard A. Berk, Sociology, University of California, Los Angeles William D. Berry, Political Science, Florida State University Kenneth A. Bollen, Sociology, University of North Carolina, Chapel Hill Linda B. Bourque, Public Health, University of California, Los Angeles Jacques A. Hagenaars, Social Sciences, Tilburg University Sally Jackson, Communications, University of Arizona Richard M. Jaeger, Education, University of North Carolina, Greensboro Gary King, Department of Government, Harvard University Roger E. Kirk, Psychology, Baylor University Helena Chmura Kraemer, Psychiatry and Behavioral Sciences, Stanford University Peter Marsden, Sociology, Harvard University Helmut Norpoth, Political Science, SUNY, Stony Brook Frank L. Schmidt, Management and Organization, University of Iowa Herbert Weisberg, Political Science, The Ohio State University Publisher Sara Miller McCune, Sage Publications, Inc. INSTRUCTIONS TO POTENTIAL CONTRIBUTORS For guidelines on submission of a monograph proposal to this series, please write Michael S. Lewis-Beck, Editor Sage QASS Series Department of Political Science University of Iowa Iowa City, IA 52242 Page i Series/Number 07-082 Summated Rating Scale Construction An Introduction Paul E. Spector University of South Florida SAGE PUBLICATIONS The International Professional Publishers Newbury Park London New Delhi Page ii Copyright © 1992 by Sage Publications, Inc. -
Factor Analysis and Scale Revision
Psychological Assessment Copyright 2000 by the American Psychological Association, Inc. 2000, Vol. 12, No. 3, 287-297 1040-3590/00/S5.00 DOI: 10.1037//1040-3590.12.3.287 Factor Analysis and Scale Revision Steven P. Reise Niels G. Waller University of California, Los Angeles Vanderbilt University Andrew L. Comrey University of California, Los Angeles This article reviews methodological issues that arise in the application of exploratory factor analysis (EFA) to scale revision and refinement. The authors begin by discussing how the appropriate use of EFA in scale revision is influenced by both the hierarchical nature of psychological constructs and the motivations underlying the revision. Then they specifically address (a) important issues that arise prior to data collection (e.g., selecting an appropriate sample), (b) technical aspects of factor analysis (e.g., determining the number of factors to retain), and (c) procedures used to evaluate the outcome of the scale revision (e.g., determining whether the new measure functions equivalently for different populations). Personality measurement by self-report questionnaire is a thriv- principal-components analysis (see Velicer & Jackson, 1990) or ing enterprise of critical importance to theory development and multidimensional scaling (Davison, 1994). Second, we discuss the testing in many psychological disciplines such as clinical psychol- use of confirmatory factor analysis (CFA) in scale revision only ogy. At least three journals focus on statistical analyses of ques- sparingly because more extended treatments of this topic are tionnaire data: Psychological Assessment, Journal of Personality available elsewhere (see Finch & West, 1997, or Floyd & Wida- Assessment, and Assessment. Many of the articles in these journals man, 1995). -
Development of Scales to Measure and Analyse the Relationship Of
International Journal of Environmental Research and Public Health Article Development of Scales to Measure and Analyse the Relationship of Safety Consciousness and Safety Citizenship Behaviour of Construction Workers: An Empirical Study in China Xiangcheng Meng 1 , Huaiyuan Zhai 2,* and Alan H. S. Chan 1 1 School of System Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China; [email protected] (X.M.); [email protected] (A.H.S.C.) 2 School of Economics and Engineering Management, Beijing Jiaotong University, Beijing 100044, China * Correspondence: [email protected]; Tel.: +86-152-1057-6816 Received: 9 April 2019; Accepted: 17 April 2019; Published: 19 April 2019 Abstract: China’s construction industry has experienced a long period of development and reform but compared to developed countries, safety on construction sites in China continues to present serious problems. Safety consciousness and safety citizenship behaviour are influential factors related to safety issues in the construction industry and may play a direct role in improving the safety of personnel on construction sites. However, recently no research has been focused on the relationship between safety consciousness and safety citizenship behaviour. Therefore, this paper aimed to investigate the relationship between safety consciousness and safety citizenship behaviour for personnel working on construction sites in China by using a questionnaire survey and statistical analysis, so that correlation between safety consciousness and safety citizenship can be demonstrated and effective measures suggested to improve the safety of construction workers in China, and perhaps in other countries as well. Keywords: construction workers; safety consciousness; safety citizenship behaviour; questionnaire survey 1. -
Stages of Psychometric Measure Development: the Example of the Generalized Expertise Measure (GEM)
893 Stages of Psychometric Measure Development: The example of the Generalized Expertise Measure (GEM) Marie-Line Germain Barry University & City College This paper chronicles the steps, methods, and presents hypothetical results of quantitative and qualitative studies being conducted to develop a Generalized Expertise Measure (GEM). Per Hinkin (1995), the stages of scale development are domain and item generation, content expert validation, and pilot test. Content/face validity and internal consistency of the scores of the GEM are discussed, as well as directions to ensure that the psychometric properties of the scale are theoretically and empirically sound. Keywords: Measure, Research Methods, Expertise Since its humble beginnings in the late 1950s, expertise has slowly permeated the human resource development literature. This seemingly obscure concept became an intriguing research topic as artificial intelligence and cognitive psychology further developed in the mid- to late sixties. Swanson & Holton (2001) define expertise as a “displayed behavior within a specialized domain and / or related domain in the form of consistently demonstrated actions of an individual that are both optimally efficient in their execution and effective in their results.” (p. 241). The absence of empirical evidence may well be the main reason of the gradual development of understanding of expertise in the last three decades (cf. Bédard & Chi, 1992). The meticulous standards of scientific methodologies invalidate the pervasive existence of anecdotal evidence in the popular press of expertise in organizational settings. The past 15 years, however, have seen an upsurge in the pace of expertise research, as indicated in the growing number of peer-reviewed publications in the area (Holton & Swanson, 2001). -
Scale Construction Notes
Scale Construction Notes Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 35487-0348 Phone: (205) 348-4431 Fax: (205) 348-8648 June 5, 2000 These notes were prepared with the support of a grant from the Dutch Science Foundation to GÄunSemin. I would like to thank Heather Claypool and Dick Heslin for comments made on earlier versions of these notes. If you wish to cite the contents of this document, the APA reference for them would be DeCoster, J. (2005). Scale Construction Notes. Retrieved <month, day, and year you downloaded this ¯le> from http://www.stat-help.com/notes.html For future versions of these notes or help with data analysis visit http://www.stat-help.com ALL RIGHTS TO THIS DOCUMENT ARE RESERVED. Contents 1 Introduction 1 2 Creating Items 2 3 Data Collection and Entry 5 4 Validity and Reliability 7 5 Measuring More than One Variable with a Scale 13 6 The Role of Revision 15 7 Reporting a Scale 17 i Chapter 1 Introduction ² The purpose of scale construction is to design a questionnaire that provides a quantitative measurement of an abstract theoretical variable. ² Scale construction can be seen as a specialized area of survey design. Not all surveys are scales, however. Before trying to apply the procedures presented here to the construction of your questionnaire you should decide whether it really is a scale. The most obvious key is that a scale uses a moderately large number of items to measure a single construct. The items within a scale are typically interchangeable, meaning that the response to one item has exactly the same theoretical meaning as the response to any other item within the scale. -
Developing Likert-Scale Questionnaires
Reference Data Nemoto, T., & Beglar, D. (2014). Developing Likert-scale questionnaires. In N. Sonda & A. Krause (Eds.), Developing JALT2013 Conference Proceedings. Tokyo: JALT. Likert-scale questionnaires are the most commonly used type of instrument for measuring affective vari- Likert-Scale ables such as motivation and self-efficacy, given that they allow researchers to gather large amounts of data with relative ease. However, despite their widespread use, there is relatively little information in the second language literature concerning the development and analysis of Likert-scale questionnaires. The purpose of this paper is to present a set of five guidelines for constructing Likert-scale instruments: Questionnaires understanding the construct, developing items, determining the outcome space, specifying the measure- ment model, and gathering feedback and piloting the questionnaire. The use of these guidelines can lead to the development of Likert-scale questionnaires that are more likely to yield reliable data that lead to Tomoko Nemoto valid interpretations. リッカート尺度によるアンケートは、比較的容易に極めて多くのデータ収集を可能とするため、動機づけや自己効力感のよう Temple University, Japan な情意変数の測定としてもっとも一般的に利用されている手段のひとつである。しかしこうした状況下においても、リッカート 尺度によるアンケートの開発や分析に関しての情報は第二言語習得学分野では比較的限られている。この論文の目的は、リッ Campus カート尺度による測定手段構築のための5つのガイドライン(構成概念の理解、アンケート項目の作成、標本結果空間の決定、 測定モデルの特定、そしてフィードバックの収集とアンケートの試験調査の実施)を提示することである。これらのガイドライ David Beglar ンを用いることが、より信頼性の高い、妥当なデータを生み出すリッカート尺度によるアンケート作成へとつながる。 Temple University, Japan DUCATIONAL RESEARCH has three primary purposes. The first is to contribute