Doing Quantitative Research 26E02900, 6 ECTS Lecture 2: Measurement Scales. OlliPekka Kauppila Rilana Riikkinen


 Briana Miles
 1 years ago
 Views:
Transcription
1 Doing Quantitative Research 26E02900, 6 ECTS Lecture 2: Measurement Scales OlliPekka Kauppila Rilana Riikkinen
2 Learning Objectives 1. Develop the ability to assess a quality of measurement instruments 2. Deepen your knowledge of measure development process 3. Gain fundamental knowledge of SPSS software application 4. Perform an exploratory factor analysis 5. Inspect the exploratory factor analysis output 6. Construct factor solutions and check reliability of the scales 4/26/2016 2
3 Learning objectives 1. Develop ability to assess a quality of measurement instruments What to do  i.e. what to do when we have some responses 2. Deepen knowledge of measure development process 3. Gain foundational knowledge of application of SPSS software 4. Perform exploratory factor analysis How to do  i.e. how to understand the structure of measurement constructs and assess their quality 5. Inspect the output from exploratory factor analysis 6. Construct factor solutions and check reliability of the scales (International Business) 3
4 Research process Research problem / question What might be the answer? Hypotheses & theoretical model Where and how to collect data? How and when to measure the variables? Evaluation of data and measure quality Testing the hypotheses Reporting the findings Answering the research question
5 Part 1: Measuring a latent construct
6 Why to be concerned about measurement scales? 1. The GIGO rule 2. Statistical properties Meaningful theories 3. Good measure is a half of success in developing a good theory 6
7 Attributes of a good measure 1. Reliability independent, but comparable measures of the same construct agree, i.e. the variance in scores is not attributed to random errors (Xr=0). 2. Validity differences in observed scores reflect true differences in the phenomenon (latent variable) we measure (Xo=Xt). 3. Generalizability measured effect is not samplespecific and could be applied to other contexts. 7
8 Why is a measure not good enough? Random error a lack of consistency of repeated measurements Systematic error a constant defect in measuring 8
9 Classroom exercise I Step 1: In groups of 23 people generate a list of potential measurement error antecedents. Step 2: Go through your list and divide the antecedents into systematic and random sources of error. Step 3: Think about potential ways to mitigate the effect of error sources. 9
10 Part 2: Measure development process
11 Procedure for developing measures 1. Specify domain of construct 2. Generate sample of items 3. Collect data 4. Purify measure Literature search Literature search Stimulating examples Critical incidents Focus groups Pilot survey => Content validity Factor analysis (EFA) Coefficient alpha (Cronbach alpha) 5. Collect data 6. Assess reliability 7. Assess validity 8. Develop norms Coefficient alpha Splithalf reliability Composite reliability CR (CFA) Construct validity: convergent/discriminant Predictive validity Average and other statistics summarizing distribution of scores Source: (Churchill 1979; Lee & Hooley 2005) 11
12 What should I check to ensure reliability & validity? Criteria Type Meaning Procedure Reliability Stability The extent the test scores correlate when being measured at two different points of time Validity Equivalence Predictive (criterion) Content (face validity) Construct Internal consistency of the items included in the scale (common variance) How well does a measure predict a criterion (dependent variable)? Adequacy with which the domain of the characteristic is captured by the measure. 1). Convergent  a measure correlates with other measures designed to evaluate the same construct. 2). Discriminant a measure does not correlate too highly with measures designed to evaluate different constructs 3). Nomological a measure behaves as theoretically expected with other constructs Testretest Splithalf reliability Cronbach alpha Composite reliability Correlations Evaluate the procedure of measure development Correlations Fornell & Lacker (1981) test of discriminant validity Correlation and regression analysis 12
13 Assess reliability Absence of random error types: Testretest reliability (stability)  test the same construct 2 times Splithalf/parallelforms reliability (consistency) split the items in 2 parts Internal consistency reliability (homogeneity) average interitem correlation (coefficient alpha) Interrater reliability (concordance) test the same construct by two researchers (judges) 4/26/
14 Assess validity 1. External validity of findings = generalizability 2. Internal validity of findings = if x actually causes y 3. Validity of measurement scales = absence of systematic error (bias) in measurement Predictive / criterion validity Content / face validity Construct validity (convergent, discriminant, nomological) = Are we actually measuring what we were supposed to measure? 4/26/
15 Part 3: Exploratory factor analysis (EFA)
16 When and why to use exploratory factor analysis? 1. To understand (explore) a structure of a set of variables; 2. To reduce a data set to a more manageable size while retaining as much of the original information as possible. Factor (or latent variable) = explanatory construct represented by a number of observed variables highly correlated with each other and explaining a common variance in the latent variable. 16
17 Interdependence of five variables In factor analysis we look to reduce the Rmatrix into smaller set of uncorrelated dimensions. 17
18 Interdependence of five variables Common variance, factor 1 Common variance, factor 2 All variance shared = communality equals 1 No variance shared = communality equals 0 18
19 Steps 1. Select variables 2. Check assumptions 3. Select factoring (extraction) method 4. Decide on the number of factors 5. Rotate 6. Interpret 7. Validate 8. Proceed to further analyses with new variables 19
20 Variable selection Continuous variables, correlation must make sense Interdependent but not causally related More observations than variables, recommended cases/variable, min 50 cases, normally around 100 cases Approximately normally distributed, no outliers 20
21 Assumptions 1. KaiserMeyerOlkin measure of sampling adequacy (KMO) Overall KMO is a measure of the correlation matrix s suitability for factor analysis KMO receives high value when partial correlations are small Kaiser s guidelines for interpreting KMO: 0.9 marvelous 0.8 meritorious 0.7 middling 0.6 mediocre 0.5 miserable < 0.5 unacceptable 21
22 Assumptions (Cont d) 2. Barlett s test of sphericity  Is our correlation matrix significantly different from an identity matrix, i.e. correlation coefficients are not zero?  Barlett s test will almost always be significant because of the sample size 3. Multicollinearity and singularity  Correlations higher than.80 may course multicollinearity problems  Check that correlation determinant is >
23 Extraction method Total variance of variables Principal components Common variance of variables Principal axis factoring Maximum likelihood 23
24 Extraction method (Cont d) 1. Total variance of variables (Principal component) Linear combinations in order to reduce N of variables Number of factors 1 k F1= a*x1+ b*x2 +.. F2= c*x1+ d*x2 +.. The first factor accounts for most of the variance, and the last factor least of the variance Retain only factors that account for more variance than a single variable 24
25 Extraction method (Cont d) 2. Common variance of variables (Principal axis; Maximum likelihood) We should know in advance the number and nature of latent dimensions The latent dimensions cause the variation in variables The variation in each variable can be divided into two components: common variance + unique (error) variance Factors are linear combinations of the common variance + error x1= a*f1+ b*f2 +..e1 x2= c*f1+ d*f2 +..e2 25
26 Number of factors Theoretical reasons Eigenvalue (latent root criterion) greater than 1 Scree plot => cut where the plot levels off Percentage of variance explained, e.g. 60% Meaningful interpretation 26
27 Rotation Orthogonal Oblique Factors are rotated but kept independent: varimax, quartimax, equamax Factors are allowed to correlate: direct oblimin, promax 27
28 Interpretation: Factor loadings Interpretation of the results of factor analysis is based on factor loadings Loading = correlation between factor and variable Range Squared loading indicates how many % of the variance of the variable the factor explains Ideally, each variable has a high ( >.4) loading on one factor and low loadings on other factors Rotation makes interpretation easier The common factor manifests an underlying latent dimension 28
29 Interpretation: Loadings significance substantial significance of the loading: min.30, preferably >.50 statistical significance: (loading + needed n of observations to make it significant at 5% level) Loading =.30 => n=350 Loading =.40 => n=200 Loading =.50 => n=120 Loading =.60 => n=85 Loading =.70 => n=60 29
30 Interpretation: Communality Computed for each variable, indicates how many % of the variance is explained by the extracted factors = sum of squared loadings Range 0 1 Should exceed.50 Small values indicate that a variable has little in common with other variables, and should be removed 30
31 Validation i.e. how stable and generalizable the solution is Randomly split your cases into two samples and run the same analysis for each partsample Try to rerun with different extraction methods and rotation methods Check that the factors are related to other external variables in the way they should 31
32 Further analysis Factor scores, summated scales, weighted scales Factor scores can be saved and used as any continuous normally distributed variable, e.g. in t tests, correlations or regression analyses Factor scores are standardized into zero mean and unit variance, therefore you cannot compare the overall level of factors with each other 32
33 Part 4: Let s get started with SPSS
34 Data sample 305 companies located in Europe Employees > 50 Manufacturing and service companies 46% response rate Data collected in 2014 Original questionnaire in English, backtranslated into three other European languages 34
35 Getting started Open the IBM SPSS Statistics program Choose: New dataset OK Choose: File Open Data open PP_2604.sav file (available from the MyCourses page) Open 35
36 Click: Analyze Dimension Reduction Factor 36
37 Click: Select the variables to include in the analysis and transfer them to the box Variables 37
38 Descriptives: Check the box with Univariate descriptives and Initial solution Check also Coefficients and Significance levels Determinant (singularity) and KMO Reproduced and Antiimage matrices => Continue 38
39 Extraction: Principal axis factoring Check the box with Correlation matrix Display unrotated factor solutions and Scree plot Extract based on Eigenvalue >1 => Continue 39
40 Rotation: Orthogonal rotation => Varimax Display rotated solution Maximum iterations for convergence 25 => Continue 40
41 Scores: Save scores as variables  If you want to ensure that factor scores are uncorrelated => AndersonRubin  If correlation between factor scores is acceptable => Regression => Continue 4/26/
42 Options: Exclude cases listwise (if you have missing values) Sort coefficients by size Suppress small coefficients (<.30) => Continue => OK (run the analysis) 4/26/
43 Interpreting the output Univariate descriptives: Means Standard deviations N of observations (we chose to exclude cases with missing values listwise, so we have 282 observations with no missing values) 4/26/
44 Correlation Matrix.30 < correlation coefficients <.90 4/26/
45 Correlation Matrix All correlations are significant Check the determinant of correlation matrix (> ) Consider eliminating the variables that may cause multicollinearity 45
46 KMO & Barlett s test Well above the min..50 Falls into the range of meritorious Check also the diagonal of antiimage correlation matrix >.50 Correlation coefficients are significantly different from zero. Perfect. If it is not significant, you certainly have a big problem! 4/26/
47 Antiimage Matrix Check also the diagonal of antiimage correlation matrix (KMO values of individual items) >.50 Offdiagonal are partial correlations, we want them to be small (high partial correlations indicate some diffusion in the pattern of correlations) 47
48 Communalities, Variance explained Check that communalities for each item are >.50 i.e. the extracted factor explains 50% of variance in the item Cumulative % of variance explained is approx. >.60 48
49 Scree plot Cut where the Eigenvalue is <1 (alternatively, one point before the plot levels off) consider also the amount of variance explained by each factor (see table Total Variance Explained ) 4/26/
50 Initial and Rotated solution Check that items load high (>.40) only on one dimension and low (<.40) on others; Loadings eliminating loadings that are <.40 4/26/
51 Reproduced correlation matrix The matrix reproduces correlations between the items based on the factor model, i.e. robservedrfrom model=residual (needs to be <.05) E.g. residual A1A2 = =.298 The diagonal displays communalities 4/26/
52 Reproduced correlation matrix The matrix reproduces correlations between the items based on the factor model, i.e. robservedrfrom model=residual (needs to be <.05) E.g. residual A1A2 = = % of our residuals have value higher than <.05 (if 50% of them are higher, be concerned!) There are communalities on the diagonal 4/26/
53 Factor correlation matrix Try another rotation method, e.g. oblique rotation (direct oblimin) and allow the factors correlate 53
54 Factor scores Now when we have calculated the factor scores you can find them added to your data file as three new columns: AR factor score 1 for analysis 1 AR factor score 2 for analysis 1 AR factor score 3 for analysis 1 The scores are standardized, have a mean of zero and a unit variance The scores of the items that load on a specific factor are usually summated and used as a summated scale (e.g. items ( ) / 5) 4/26/
55 Classroom exercise II 1. Based on our earlier results and provided guidelines for EFA, improve the factor structure of the Purchasing Performance construct. 2. Find an optimal (in your opinion) factor solution. 3. Do not forget to validate your factor solution by splitting the sample in two parts. 4/26/
56 Reliability assessment Reliability = a measure consistently reflects a construct that we are measuring Constructed for each subscale (factor) individually Subscale 1 (Quality): items 15 Subscale 2 (Cost): items 69 Subscale 3 (Flexibility) : items Analyze => Scale => Reliability Analysis Laitoksen nimi 56
57 Reliability assessment In Statistics check Scale if item deleted & Interitem correlations => OK 4/26/
58 Reliability assessment α (Subscale 1) =.856 α (Subscale 2) =.781 α (Subscale 3) =.671 Accepted value of α is above.60 (exploratory) and.70 (theory testing) Remove items with itemtotal correlation less than.50 4/26/
59 Reporting the results Assumptions: KMO; overall and range of interitem correlations Factor extraction method How did you decide about the number of factors? Rotation method 4/26/
60 Classroom exercise III Calculate the scale reliability for your optimal factor solution. Did constructs reliability become better or worse? 60
61 Critical assessment of measures 1. Reliability and coefficient alpha  Coefficient alpha increases when the number of items increases  Internal consistency (potentially equals redundancy) is not necessarily good for validity  Items are highly correlated to reach high alpha;  Items need to predict a true latent score to a high degree (the more they are correlated, the less is their predictive power);  Be aware that coefficient alpha does not ensure unidimensionality (use FA) 2. FA is not PCA!  FA accounts for common + unique variance  PCA creates a linear combination of items (index), it does not account for random error  Thus, it is also not generalizable to other samples 4/26/
62 Critical assessment of measures (2) 3. Selecting a number of factors  Scree plot and Kaiser criterion, but  Kaiser criterion was developed for PCA  In FA low communality => low eigenvalue (Kaiser criterion) => reason for elimination So what do I do?  Check several factor solutions (one more factor, one less, two less)  If there is a single item with high unique variance (low communality), think whether it is a poorly represented new construct 4/26/
63 Critical assessment of measures (3) 4. Dealing with factor rotation  No constructs in the real world are completely uncorrelated (orthogonal rotation)  And if they were, then a rotation method that allows correlation (oblique rotation) will return us an uncorrelated solution  Orthogonal (statistical simplicity): statistical simplicity, no multicollinearity, BUT potentially samplespecificity  Oblique (theoretical rigor): potential multicollinearity, but correctness of pattern discovered and constancy from one sample to another So what do I do?  Start with oblique rotation, use orthogonal when appropriate or necessary  Whatever rotation method is selected, it should be justified conceptually 4/26/
64 So what did we learn today? 1. Develop a measurement construct Borrow smart Use with theoretical rigor 2. To understand and interpret construct quality 3. The use of SPSS for factor analysis 4/26/
A Brief Introduction to SPSS Factor Analysis
A Brief Introduction to SPSS Factor Analysis SPSS has a procedure that conducts exploratory factor analysis. Before launching into a step by step example of how to use this procedure, it is recommended
More informationFACTOR ANALYSIS EXPLORATORY APPROACHES. Kristofer Årestedt
FACTOR ANALYSIS EXPLORATORY APPROACHES Kristofer Årestedt 20130428 UNIDIMENSIONALITY Unidimensionality imply that a set of items forming an instrument measure one thing in common Unidimensionality is
More informationFactor Analysis. Principal components factor analysis. Use of extracted factors in multivariate dependency models
Factor Analysis Principal components factor analysis Use of extracted factors in multivariate dependency models 2 KEY CONCEPTS ***** Factor Analysis Interdependency technique Assumptions of factor analysis
More informationFACTOR ANALYSIS NASC
FACTOR ANALYSIS NASC Factor Analysis A data reduction technique designed to represent a wide range of attributes on a smaller number of dimensions. Aim is to identify groups of variables which are relatively
More informationCommon factor analysis
Common factor analysis This is what people generally mean when they say "factor analysis" This family of techniques uses an estimate of common variance among the original variables to generate the factor
More information4. There are no dependent variables specified... Instead, the model is: VAR 1. Or, in terms of basic measurement theory, we could model it as:
1 Neuendorf Factor Analysis Assumptions: 1. Metric (interval/ratio) data 2. Linearity (in the relationships among the variablesfactors are linear constructions of the set of variables; the critical source
More informationTtest & factor analysis
Parametric tests Ttest & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives Continue
More informationExploratory Factor Analysis and Principal Components. Pekka Malo & Anton Frantsev 30E00500 Quantitative Empirical Research Spring 2016
and Principal Components Pekka Malo & Anton Frantsev 30E00500 Quantitative Empirical Research Spring 2016 Agenda Brief History and Introductory Example Factor Model Factor Equation Estimation of Loadings
More informationChapter 7 Factor Analysis SPSS
Chapter 7 Factor Analysis SPSS Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often
More informationFactor Analysis Example: SAS program (in blue) and output (in black) interleaved with comments (in red)
Factor Analysis Example: SAS program (in blue) and output (in black) interleaved with comments (in red) The following DATA procedure is to read input data. This will create a SAS dataset named CORRMATR
More information2. Linearity (in relationships among the variablesfactors are linear constructions of the set of variables) F 2 X 4 U 4
1 Neuendorf Factor Analysis Assumptions: 1. Metric (interval/ratio) data. Linearity (in relationships among the variablesfactors are linear constructions of the set of variables) 3. Univariate and multivariate
More informationPrincipal Components Analysis (PCA)
Principal Components Analysis (PCA) Janette Walde janette.walde@uibk.ac.at Department of Statistics University of Innsbruck Outline I Introduction Idea of PCA Principle of the Method Decomposing an Association
More informationLecture 7: Factor Analysis. Laura McAvinue School of Psychology Trinity College Dublin
Lecture 7: Factor Analysis Laura McAvinue School of Psychology Trinity College Dublin The Relationship between Variables Previous lectures Correlation Measure of strength of association between two variables
More informationFactor Analysis. Chapter 420. Introduction
Chapter 420 Introduction (FA) is an exploratory technique applied to a set of observed variables that seeks to find underlying factors (subsets of variables) from which the observed variables were generated.
More informationOverview of Factor Analysis
Overview of Factor Analysis Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 354870348 Phone: (205) 3484431 Fax: (205) 3488648 August 1,
More informationFactor Analysis. Advanced Financial Accounting II Åbo Akademi School of Business
Factor Analysis Advanced Financial Accounting II Åbo Akademi School of Business Factor analysis A statistical method used to describe variability among observed variables in terms of fewer unobserved variables
More informationExploratory Factor Analysis of Demographic Characteristics of Antenatal Clinic Attendees and their Association with HIV Risk
Doi:10.5901/mjss.2014.v5n20p303 Abstract Exploratory Factor Analysis of Demographic Characteristics of Antenatal Clinic Attendees and their Association with HIV Risk Wilbert Sibanda Philip D. Pretorius
More information5.2 Customers Types for Grocery Shopping Scenario
 CHAPTER 5: RESULTS AND ANALYSIS 
More informationData analysis process
Data analysis process Data collection and preparation Collect data Prepare codebook Set up structure of data Enter data Screen data for errors Exploration of data Descriptive Statistics Graphs Analysis
More informationExploratory Factor Analysis Brian Habing  University of South Carolina  October 15, 2003
Exploratory Factor Analysis Brian Habing  University of South Carolina  October 15, 2003 FA is not worth the time necessary to understand it and carry it out. Hills, 1977 Factor analysis should not
More informationA PARADIGM FOR DEVELOPING BETTER MEASURES OF MARKETING CONSTRUCTS
A PARADIGM FOR DEVELOPING BETTER MEASURES OF MARKETING CONSTRUCTS Gilber A. Churchill (1979) Introduced by Azra Dedic in the course of Measurement in Business Research Introduction 2 Measurements are rules
More informationIntroduction to Principal Components and FactorAnalysis
Introduction to Principal Components and FactorAnalysis Multivariate Analysis often starts out with data involving a substantial number of correlated variables. Principal Component Analysis (PCA) is a
More informationAn introduction to. Principal Component Analysis & Factor Analysis. Using SPSS 19 and R (psych package) Robin Beaumont robin@organplayers.co.
An introduction to Principal Component Analysis & Factor Analysis Using SPSS 19 and R (psych package) Robin Beaumont robin@organplayers.co.uk Monday, 23 April 2012 Acknowledgment: The original version
More informationFactor Analysis Using SPSS
Factor Analysis Using SPSS The theory of factor analysis was described in your lecture, or read Field (2005) Chapter 15. Example Factor analysis is frequently used to develop questionnaires: after all
More informationDATA ANALYSIS AND INTERPRETATION OF EMPLOYEES PERSPECTIVES ON HIGH ATTRITION
DATA ANALYSIS AND INTERPRETATION OF EMPLOYEES PERSPECTIVES ON HIGH ATTRITION Analysis is the key element of any research as it is the reliable way to test the hypotheses framed by the investigator. This
More informationSPSS ADVANCED ANALYSIS WENDIANN SETHI SPRING 2011
SPSS ADVANCED ANALYSIS WENDIANN SETHI SPRING 2011 Statistical techniques to be covered Explore relationships among variables Correlation Regression/Multiple regression Logistic regression Factor analysis
More informationA Beginner s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis
Tutorials in Quantitative Methods for Psychology 2013, Vol. 9(2), p. 7994. A Beginner s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis An Gie Yong and Sean Pearce University of Ottawa
More informationFACTOR ANALYSIS. Factor Analysis is similar to PCA in that it is a technique for studying the interrelationships among variables.
FACTOR ANALYSIS Introduction Factor Analysis is similar to PCA in that it is a technique for studying the interrelationships among variables Both methods differ from regression in that they don t have
More informationTo do a factor analysis, we need to select an extraction method and a rotation method. Hit the Extraction button to specify your extraction method.
Factor Analysis in SPSS To conduct a Factor Analysis, start from the Analyze menu. This procedure is intended to reduce the complexity in a set of data, so we choose Data Reduction from the menu. And the
More informationRachel J. Goldberg, Guideline Research/Atlanta, Inc., Duluth, GA
PROC FACTOR: How to Interpret the Output of a RealWorld Example Rachel J. Goldberg, Guideline Research/Atlanta, Inc., Duluth, GA ABSTRACT THE METHOD This paper summarizes a realworld example of a factor
More informationFactor Analysis Using SPSS
Psychology 305 p. 1 Factor Analysis Using SPSS Overview For this computer assignment, you will conduct a series of principal factor analyses to examine the factor structure of a new instrument developed
More informationResearch Methodology: Tools
MSc Business Administration Research Methodology: Tools Applied Data Analysis (with SPSS) Lecture 02: Item Analysis / Scale Analysis / Factor Analysis February 2014 Prof. Dr. Jürg Schwarz Lic. phil. Heidi
More informationWhat is Rotating in Exploratory Factor Analysis?
A peerreviewed electronic journal. Copyright is retained by the first or sole author, who grants right of first publication to the Practical Assessment, Research & Evaluation. Permission is granted to
More informationThe president of a Fortune 500 firm wants to measure the firm s image.
4. Factor Analysis A related method to the PCA is the Factor Analysis (FA) with the crucial difference that in FA a statistical model is constructed to explain the interrelations (correlations) between
More informationExploratory Factor Analysis
Exploratory Factor Analysis Definition Exploratory factor analysis (EFA) is a procedure for learning the extent to which k observed variables might measure m abstract variables, wherein m is less than
More informationCanonical Correlation
Chapter 400 Introduction Canonical correlation analysis is the study of the linear relations between two sets of variables. It is the multivariate extension of correlation analysis. Although we will present
More informationPractical Considerations for Using Exploratory Factor Analysis in Educational Research
A peerreviewed electronic journal. Copyright is retained by the first or sole author, who grants right of first publication to the Practical Assessment, Research & Evaluation. Permission is granted to
More informationPRINCIPAL COMPONENTS AND THE MAXIMUM LIKELIHOOD METHODS AS TOOLS TO ANALYZE LARGE DATA WITH A PSYCHOLOGICAL TESTING EXAMPLE
PRINCIPAL COMPONENTS AND THE MAXIMUM LIKELIHOOD METHODS AS TOOLS TO ANALYZE LARGE DATA WITH A PSYCHOLOGICAL TESTING EXAMPLE Markela Muca Llukan Puka Klodiana Bani Department of Mathematics, Faculty of
More informationExploratory Factor Analysis
Exploratory Factor Analysis ( 探 索 的 因 子 分 析 ) Yasuyo Sawaki Waseda University JLTA2011 Workshop Momoyama Gakuin University October 28, 2011 1 Today s schedule Part 1: EFA basics Introduction to factor
More informationTopic 10: Factor Analysis
Topic 10: Factor Analysis Introduction Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called
More informationINTRODUCTION DATA SCREENING
EXPLORATORY FACTOR ANALYSIS ORIGINALLY PRESENTED BY: DAWN HUBER FOR THE COE FACULTY RESEARCH CENTER MODIFIED AND UPDATED FOR EPS 624/725 BY: ROBERT A. HORN & WILLIAM MARTIN (SP. 08) The purpose of this
More informationHow to report the percentage of explained common variance in exploratory factor analysis
UNIVERSITAT ROVIRA I VIRGILI How to report the percentage of explained common variance in exploratory factor analysis Tarragona 2013 Please reference this document as: LorenzoSeva, U. (2013). How to report
More informationStatistics in Psychosocial Research Lecture 8 Factor Analysis I. Lecturer: Elizabeth GarrettMayer
This work is licensed under a Creative Commons AttributionNonCommercialShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationFactor Analysis and Structural equation modelling
Factor Analysis and Structural equation modelling Herman Adèr Previously: Department Clinical Epidemiology and Biostatistics, VU University medical center, Amsterdam Stavanger July 4 13, 2006 Herman Adèr
More informationDoes organizational culture cheer organizational profitability? A case study on a Bangalore based Software Company
Does organizational culture cheer organizational profitability? A case study on a Bangalore based Software Company S Deepalakshmi Assistant Professor Department of Commerce School of Business, Alliance
More informationPRINCIPAL COMPONENT ANALYSIS
1 Chapter 1 PRINCIPAL COMPONENT ANALYSIS Introduction: The Basics of Principal Component Analysis........................... 2 A Variable Reduction Procedure.......................................... 2
More informationFactor Analysis: Statnotes, from North Carolina State University, Public Administration Program. Factor Analysis
Factor Analysis Overview Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It reduces attribute space from a larger number of variables to a smaller number of
More informationThis chapter will demonstrate how to perform multiple linear regression with IBM SPSS
CHAPTER 7B Multiple Regression: Statistical Methods Using IBM SPSS This chapter will demonstrate how to perform multiple linear regression with IBM SPSS first using the standard method and then using the
More informationReview Jeopardy. Blue vs. Orange. Review Jeopardy
Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 03 Jeopardy Round $200 How could I measure how far apart (i.e. how different) two observations, y 1 and y 2, are from each other?
More informationPull and Push Factors of Migration: A Case Study in the Urban Area of Monywa Township, Myanmar
Pull and Push Factors of Migration: A Case Study in the Urban Area of Monywa Township, Myanmar By Kyaing Kyaing Thet Abstract: Migration is a global phenomenon caused not only by economic factors, but
More informationTHE USING FACTOR ANALYSIS METHOD IN PREDICTION OF BUSINESS FAILURE
THE USING FACTOR ANALYSIS METHOD IN PREDICTION OF BUSINESS FAILURE Mary Violeta Petrescu Ph. D University of Craiova Faculty of Economics and Business Administration Craiova, Romania Abstract: : After
More informationMultivariate Analysis (Slides 13)
Multivariate Analysis (Slides 13) The final topic we consider is Factor Analysis. A Factor Analysis is a mathematical approach for attempting to explain the correlation between a large set of variables
More informationRESEARCH METHODS IN I/O PSYCHOLOGY
RESEARCH METHODS IN I/O PSYCHOLOGY Objectives Understand Empirical Research Cycle Knowledge of Research Methods Conceptual Understanding of Basic Statistics PSYC 353 11A rsch methods 01/17/11 [Arthur]
More informationDimensionality Reduction: Principal Components Analysis
Dimensionality Reduction: Principal Components Analysis In data mining one often encounters situations where there are a large number of variables in the database. In such situations it is very likely
More informationUNDERSTANDING MULTIPLE REGRESSION
UNDERSTANDING Multiple regression analysis (MRA) is any of several related statistical methods for evaluating the effects of more than one independent (or predictor) variable on a dependent (or outcome)
More informationCHAPTER 8 FACTOR EXTRACTION BY MATRIX FACTORING TECHNIQUES. From Exploratory Factor Analysis Ledyard R Tucker and Robert C.
CHAPTER 8 FACTOR EXTRACTION BY MATRIX FACTORING TECHNIQUES From Exploratory Factor Analysis Ledyard R Tucker and Robert C MacCallum 1997 180 CHAPTER 8 FACTOR EXTRACTION BY MATRIX FACTORING TECHNIQUES In
More informationUnivariate Regression
Univariate Regression Correlation and Regression The regression line summarizes the linear relationship between 2 variables Correlation coefficient, r, measures strength of relationship: the closer r is
More informationPsychology 7291, Multivariate Analysis, Spring 2003. SAS PROC FACTOR: Suggestions on Use
: Suggestions on Use Background: Factor analysis requires several arbitrary decisions. The choices you make are the options that you must insert in the following SAS statements: PROC FACTOR METHOD=????
More informationFactor Analysis  SPSS
Factor Analysis  SPSS First Read Principal Components Analysis. The methods we have employed so far attempt to repackage all of the variance in the p variables into principal components. We may wish to
More informationQuestionnaire Evaluation with Factor Analysis and Cronbach s Alpha An Example
Questionnaire Evaluation with Factor Analysis and Cronbach s Alpha An Example  Melanie Hof  1. Introduction The pleasure writers experience in writing considerably influences their motivation and consequently
More informationReliability Analysis
Measures of Reliability Reliability Analysis Reliability: the fact that a scale should consistently reflect the construct it is measuring. One way to think of reliability is that other things being equal,
More informationPATTERNS OF ENVIRONMENTAL MANAGEMENT IN THE CHILEAN MANUFACTURING INDUSTRY: AN EMPIRICAL APPROACH
PATTERS OF EVIROMETAL MAAGEMET I THE CHILEA MAUFACTURIG IDUSTRY: A EMPIRICAL APPROACH Dr. Maria Teresa RuizTagle Research Associate, University of Cambridge, UK Research Associate, Universidad de Chile,
More informationNCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
More informationRegression Analysis Using ArcMap. By Jennie Murack
Regression Analysis Using ArcMap By Jennie Murack Regression Basics How is Regression Different from other Spatial Statistical Analyses? With other tools you ask WHERE something is happening? Are there
More informationMeasurement Error 2: Scale Construction (Very Brief Overview) Page 1
Measurement Error 2: Scale Construction (Very Brief Overview) Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised January 22, 2015 This handout draws heavily from Marija
More informationPerform hypothesis testing
Multivariate hypothesis tests for fixed effects Testing homogeneity of level1 variances In the following sections, we use the model displayed in the figure below to illustrate the hypothesis tests. Partial
More informationExploratory Factor Analysis
Introduction Principal components: explain many variables using few new variables. Not many assumptions attached. Exploratory Factor Analysis Exploratory factor analysis: similar idea, but based on model.
More informationUsing Principal Components Analysis in Program Evaluation: Some Practical Considerations
http://evaluation.wmich.edu/jmde/ Articles Using Principal Components Analysis in Program Evaluation: Some Practical Considerations J. Thomas Kellow Assistant Professor of Research and Statistics Mercer
More informationExploratory Factor Analysis: rotation. Psychology 588: Covariance structure and factor models
Exploratory Factor Analysis: rotation Psychology 588: Covariance structure and factor models Rotational indeterminacy Given an initial (orthogonal) solution (i.e., Φ = I), there exist infinite pairs of
More informationCanonical Correlation Analysis
Canonical Correlation Analysis LEARNING OBJECTIVES Upon completing this chapter, you should be able to do the following: State the similarities and differences between multiple regression, factor analysis,
More informationSimple Linear Regression Chapter 11
Simple Linear Regression Chapter 11 Rationale Frequently decisionmaking situations require modeling of relationships among business variables. For instance, the amount of sale of a product may be related
More informationPrincipal Component Analysis
Principal Component Analysis Principle Component Analysis: A statistical technique used to examine the interrelations among a set of variables in order to identify the underlying structure of those variables.
More informationThe Effectiveness of Ethics Program among Malaysian Companies
2011 2 nd International Conference on Economics, Business and Management IPEDR vol.22 (2011) (2011) IACSIT Press, Singapore The Effectiveness of Ethics Program among Malaysian Companies Rabiatul Alawiyah
More information, then the form of the model is given by: which comprises a deterministic component involving the three regression coefficients (
Multiple regression Introduction Multiple regression is a logical extension of the principles of simple linear regression to situations in which there are several predictor variables. For instance if we
More informationA Brief Introduction to Factor Analysis
1. Introduction A Brief Introduction to Factor Analysis Factor analysis attempts to represent a set of observed variables X 1, X 2. X n in terms of a number of 'common' factors plus a factor which is unique
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationEFA II. Review of Conceptual Model Steps in EFA Sample Size Conceptual Issues in Interpretation Presenting Results. 09SEM2a 1
EFA II Review of Conceptual Model Steps in EFA Sample Size Conceptual Issues in Interpretation Presenting Results 09SEM2a 1 Review of Conceptual Model Variables of interest typically can't be measured
More informationChapter Seven. Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS
Chapter Seven Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS Section : An introduction to multiple regression WHAT IS MULTIPLE REGRESSION? Multiple
More informationAPPRAISAL OF FINANCIAL AND ADMINISTRATIVE FUNCTIONING OF PUNJAB TECHNICAL UNIVERSITY
APPRAISAL OF FINANCIAL AND ADMINISTRATIVE FUNCTIONING OF PUNJAB TECHNICAL UNIVERSITY In the previous chapters the budgets of the university have been analyzed using various techniques to understand the
More informationMultivariate Analysis
Table Of Contents Multivariate Analysis... 1 Overview... 1 Principal Components... 2 Factor Analysis... 5 Cluster Observations... 12 Cluster Variables... 17 Cluster KMeans... 20 Discriminant Analysis...
More informationMULTIVARIATE DATA ANALYSIS i.*.'.. ' 4
SEVENTH EDITION MULTIVARIATE DATA ANALYSIS i.*.'.. ' 4 A Global Perspective Joseph F. Hair, Jr. Kennesaw State University William C. Black Louisiana State University Barry J. Babin University of Southern
More informationEFFECT OF ENVIRONMENTAL CONCERN & SOCIAL NORMS ON ENVIRONMENTAL FRIENDLY BEHAVIORAL INTENTIONS
169 EFFECT OF ENVIRONMENTAL CONCERN & SOCIAL NORMS ON ENVIRONMENTAL FRIENDLY BEHAVIORAL INTENTIONS Joshi Pradeep Assistant Professor, Quantum School of Business, Roorkee, Uttarakhand, India joshipradeep_2004@yahoo.com
More informationpsyc3010 lecture 8 standard and hierarchical multiple regression last week: correlation and regression Next week: moderated regression
psyc3010 lecture 8 standard and hierarchical multiple regression last week: correlation and regression Next week: moderated regression 1 last week this week last week we revised correlation & regression
More informationHow to get more value from your survey data
IBM SPSS Statistics How to get more value from your survey data Discover four advanced analysis techniques that make survey research more effective Contents: 1 Introduction 2 Descriptive survey research
More informationFactor Analysis  SPSS
First Read Principal Components Analysis. Analysis  SPSS The methods we have employed so far attempt to repackage all of the variance in the p variables into principal components. We may wish to restrict
More informationRESEARCH METHODS IN I/O PSYCHOLOGY
RESEARCH METHODS IN I/O PSYCHOLOGY Objectives Understand Empirical Research Cycle Knowledge of Research Methods Conceptual Understanding of Basic Statistics PSYC 353 11A rsch methods 09/01/11 [Arthur]
More informationValidation of the Core SelfEvaluations Scale research instrument in the conditions of Slovak Republic
Validation of the Core SelfEvaluations Scale research instrument in the conditions of Slovak Republic Lenka Selecká, Jana Holienková Faculty of Arts, Department of psychology University of SS. Cyril and
More informationProc. of Int. Conf. on Computing, Communication & Manufacturing 2014
Proc. of Int. Conf. on Computing, Communication & Manufacturing 2014 Technology Acceptance Model implementation in Open Source Learning Management System Parantap Chatterjee MCKV Institute Of Engineering/MCA,
More informationSTA 4107/5107. Chapter 3
STA 4107/5107 Chapter 3 Factor Analysis 1 Key Terms Please review and learn these terms. 2 What is Factor Analysis? Factor analysis is an interdependence technique (see chapter 1) that primarily uses metric
More informationEXPLORATORY FACTOR ANALYSIS IN MPLUS, R AND SPSS. sigbert@wiwi.huberlin.de
EXPLORATORY FACTOR ANALYSIS IN MPLUS, R AND SPSS Sigbert Klinke 1,2 Andrija Mihoci 1,3 and Wolfgang Härdle 1,3 1 School of Business and Economics, HumboldtUniversität zu Berlin, Germany 2 Department of
More information12/31/2016. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Understand when to use multiple Understand the multiple equation and what the coefficients represent Understand different methods
More informationMultiple Regression: What Is It?
Multiple Regression Multiple Regression: What Is It? Multiple regression is a collection of techniques in which there are multiple predictors of varying kinds and a single outcome We are interested in
More informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 15 scale to 0100 scores When you look at your report, you will notice that the scores are reported on a 0100 scale, even though respondents
More informationAfter considering the above three factors, it should also be very clear in your mind what you want to achieve.
SPSS Tutorial Which Statistical test? Introduction Irrespective of the statistical package that you are using, deciding on the right statistical test to use can be a daunting exercise. In this document,
More informationWriting Up A Factor Analysis. Table of Contents
Writing Up A Factor Analysis James Neill Centre for Applied Psychology University of Canberra 30 March, 2008 Creative Commons Attribution 2.5 Australia http://creativecommons.org/licenses/by/2.5/au/ Table
More informationHatice Camgöz Akdağ. findings of previous research in which two independent firm clusters were
Innovative Culture and Total Quality Management as a Tool for Sustainable Competitiveness: A Case Study of Turkish Fruit and Vegetable Processing Industry SMEs, Sedef Akgüngör Hatice Camgöz Akdağ Aslı
More informationUnit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression
Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a
More informationWHAT IS A JOURNAL CLUB?
WHAT IS A JOURNAL CLUB? With its September 2002 issue, the American Journal of Critical Care debuts a new feature, the AJCC Journal Club. Each issue of the journal will now feature an AJCC Journal Club
More informationStatistics for Business Decision Making
Statistics for Business Decision Making Faculty of Economics University of Siena 1 / 62 You should be able to: ˆ Summarize and uncover any patterns in a set of multivariate data using the (FM) ˆ Apply
More informationA Introduction to Matrix Algebra and Principal Components Analysis
A Introduction to Matrix Algebra and Principal Components Analysis Multivariate Methods in Education ERSH 8350 Lecture #2 August 24, 2011 ERSH 8350: Lecture 2 Today s Class An introduction to matrix algebra
More information