Doing Quantitative Research 26E02900, 6 ECTS Lecture 2: Measurement Scales. Olli-Pekka Kauppila Rilana Riikkinen
|
|
- Briana Miles
- 7 years ago
- Views:
Transcription
1 Doing Quantitative Research 26E02900, 6 ECTS Lecture 2: Measurement Scales Olli-Pekka 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 sample-specific 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 2-3 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 Split-half 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 Test-retest Split-half 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: Test-retest reliability (stability) - test the same construct 2 times Split-half/parallel-forms reliability (consistency) split the items in 2 parts Internal consistency reliability (homogeneity) average inter-item correlation (coefficient alpha) Inter-rater 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 R-matrix 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. Kaiser-Meyer-Olkin 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 part-sample 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, back-translated 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 Anti-image 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 => Anderson-Rubin - 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 anti-image 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 Anti-image Matrix Check also the diagonal of anti-image correlation matrix (KMO values of individual items) >.50 Off-diagonal 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. robserved-rfrom 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. robserved-rfrom 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: A-R factor score 1 for analysis 1 A-R factor score 2 for analysis 1 A-R 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 1-5 Subscale 2 (Cost): items 6-9 Subscale 3 (Flexibility) : items Analyze => Scale => Reliability Analysis Laitoksen nimi 56
57 Reliability assessment In Statistics check Scale if item deleted & Inter-item 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 item-total correlation less than.50 4/26/
59 Reporting the results Assumptions: KMO; overall and range of inter-item 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 sample-specificity - 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 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 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 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 informationT-test & factor analysis
Parametric tests T-test & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives Continue
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 variables--factors are linear constructions of the set of variables; the critical source
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 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 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 variables--factors 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 variables--factors are linear constructions of the set of variables) 3. Univariate and multivariate
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 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 informationOverview of Factor Analysis
Overview of Factor Analysis 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 August 1,
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 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 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 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 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 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 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 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 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 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 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 informationRachel J. Goldberg, Guideline Research/Atlanta, Inc., Duluth, GA
PROC FACTOR: How to Interpret the Output of a Real-World Example Rachel J. Goldberg, Guideline Research/Atlanta, Inc., Duluth, GA ABSTRACT THE METHOD This paper summarizes a real-world example of a factor
More informationWhat is Rotating in Exploratory Factor Analysis?
A peer-reviewed 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 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 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 informationA Beginner s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis
Tutorials in Quantitative Methods for Psychology 2013, Vol. 9(2), p. 79-94. A Beginner s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis An Gie Yong and Sean Pearce University of Ottawa
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 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 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 informationPractical Considerations for Using Exploratory Factor Analysis in Educational Research
A peer-reviewed 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 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 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 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 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 informationStatistics in Psychosocial Research Lecture 8 Factor Analysis I. Lecturer: Elizabeth Garrett-Mayer
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationPRINCIPAL COMPONENT ANALYSIS
1 Chapter 1 PRINCIPAL COMPONENT ANALYSIS Introduction: The Basics of Principal Component Analysis........................... 2 A Variable Reduction Procedure.......................................... 2
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 informationReview Jeopardy. Blue vs. Orange. Review Jeopardy
Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 0-3 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 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 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 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: Lorenzo-Seva, U. (2013). How to report
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 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 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 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 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 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 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 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 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 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 informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents
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 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 Ruiz-Tagle Research Associate, University of Cambridge, UK Research Associate, Universidad de Chile,
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 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 informationValidation of the Core Self-Evaluations Scale research instrument in the conditions of Slovak Republic
Validation of the Core Self-Evaluations 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 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 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 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 informationMultivariate Analysis
Table Of Contents Multivariate Analysis... 1 Overview... 1 Principal Components... 2 Factor Analysis... 5 Cluster Observations... 12 Cluster Variables... 17 Cluster K-Means... 20 Discriminant Analysis...
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 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 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 informationHYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
More informationa. Will the measure employed repeatedly on the same individuals yield similar results? (stability)
INTRODUCTION Sociologist James A. Quinn states that the tasks of scientific method are related directly or indirectly to the study of similarities of various kinds of objects or events. One of the tasks
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
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 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 informationFactor Analysis. Sample StatFolio: factor analysis.sgp
STATGRAPHICS Rev. 1/10/005 Factor Analysis Summary The Factor Analysis procedure is designed to extract m common factors from a set of p quantitative variables X. In many situations, a small number of
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 informationCHAPTER VI ON PRIORITY SECTOR LENDING
CHAPTER VI IMPACT OF PRIORITY SECTOR LENDING 6.1 PRINCIPAL FACTORS THAT HAVE DIRECT IMPACT ON PRIORITY SECTOR LENDING 6.2 ASSOCIATION BETWEEN THE PROFILE VARIABLES AND IMPACT OF PRIORITY SECTOR CREDIT
More informationPart III. Item-Level Analysis
Part III Item-Level Analysis 6241-029-P3-006-2pass-r02.indd 169 1/16/2013 9:14:56 PM 6241-029-P3-006-2pass-r02.indd 170 1/16/2013 9:14:57 PM 6 Exploratory and Confirmatory Factor Analysis Rex Kline 6.1
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 informationChoosing the Right Type of Rotation in PCA and EFA James Dean Brown (University of Hawai i at Manoa)
Shiken: JALT Testing & Evaluation SIG Newsletter. 13 (3) November 2009 (p. 20-25) Statistics Corner Questions and answers about language testing statistics: Choosing the Right Type of Rotation in PCA and
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 informationMultivariate Analysis of Variance (MANOVA)
Chapter 415 Multivariate Analysis of Variance (MANOVA) Introduction Multivariate analysis of variance (MANOVA) is an extension of common analysis of variance (ANOVA). In ANOVA, differences among various
More informationX = T + E. Reliability. Reliability. Classical Test Theory 7/18/2012. Refers to the consistency or stability of scores
Reliability It is the user who must take responsibility for determining whether or not scores are sufficiently trustworthy to justify anticipated uses and interpretations. (AERA et al., 1999) Reliability
More informationRelationship Quality as Predictor of B2B Customer Loyalty. Shaimaa S. B. Ahmed Doma
Relationship Quality as Predictor of B2B Customer Loyalty Shaimaa S. B. Ahmed Doma Faculty of Commerce, Business Administration Department, Alexandria University Email: Shaimaa_ahmed24@yahoo.com Abstract
More information1 2 3 1 1 2 x = + x 2 + x 4 1 0 1
(d) If the vector b is the sum of the four columns of A, write down the complete solution to Ax = b. 1 2 3 1 1 2 x = + x 2 + x 4 1 0 0 1 0 1 2. (11 points) This problem finds the curve y = C + D 2 t which
More informationFactor analysis. Angela Montanari
Factor analysis Angela Montanari 1 Introduction Factor analysis is a statistical model that allows to explain the correlations between a large number of observed correlated variables through a small number
More informationPARTIAL LEAST SQUARES IS TO LISREL AS PRINCIPAL COMPONENTS ANALYSIS IS TO COMMON FACTOR ANALYSIS. Wynne W. Chin University of Calgary, CANADA
PARTIAL LEAST SQUARES IS TO LISREL AS PRINCIPAL COMPONENTS ANALYSIS IS TO COMMON FACTOR ANALYSIS. Wynne W. Chin University of Calgary, CANADA ABSTRACT The decision of whether to use PLS instead of a covariance
More informationEXPLORATORY FACTOR ANALYSIS IN MPLUS, R AND SPSS. sigbert@wiwi.hu-berlin.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, Humboldt-Universität zu Berlin, Germany 2 Department of
More informationFactor Rotations in Factor Analyses.
Factor Rotations in Factor Analyses. Hervé Abdi 1 The University of Texas at Dallas Introduction The different methods of factor analysis first extract a set a factors from a data set. These factors are
More informationSimple linear regression
Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between
More informationResearch Methods & Experimental Design
Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and
More informationData Mining for Model Creation. Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.
Sept 03-23-05 22 2005 Data Mining for Model Creation Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.com page 1 Agenda Data Mining and Estimating Model Creation
More informationPrincipal Component Analysis
Principal Component Analysis ERS70D George Fernandez INTRODUCTION Analysis of multivariate data plays a key role in data analysis. Multivariate data consists of many different attributes or variables recorded
More informationAN ANALYSIS OF FOOD SAFETY MANAGEMENT SYSTEMS CERTIFICATION: THE PORTUGUESE CASE
AN ANALYSIS OF FOOD SAFETY MANAGEMENT SYSTEMS CERTIFICATION: THE PORTUGUESE CASE Sofia Teixeira, MSc Student, University of Porto, sofiatteixeira@gmail.com Paulo Sampaio, University of Minho, paulosampaio@dps.uminho.pt
More informationHow To Run Factor Analysis
Getting Started in Factor Analysis (using Stata 10) (ver. 1.5) Oscar Torres-Reyna Data Consultant otorres@princeton.edu http://dss.princeton.edu/training/ Factor analysis is used mostly for data reduction
More informationA STUDY OF CONSUMER ATTITUDE TOWARDS ADVERTISING THROUGH MOBILE PHONES
A STUDY OF CONSUMER ATTITUDE TOWARDS ADVERTISING THROUGH MOBILE PHONES Sunny Dawar*, Dr. Anil Kothari** Abstract Advanced technology plays a significant role in analysis of consumers psychology and their
More informationChapter 15. Mixed Models. 15.1 Overview. A flexible approach to correlated data.
Chapter 15 Mixed Models A flexible approach to correlated data. 15.1 Overview Correlated data arise frequently in statistical analyses. This may be due to grouping of subjects, e.g., students within classrooms,
More informationBest Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis
A peer-reviewed 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 informationRunning head: ONLINE VALUE AND SELF-EFFICACY SCALE
Online Value and Self-Efficacy Scale Running head: ONLINE VALUE AND SELF-EFFICACY SCALE Development and Initial Validation of the Online Learning Value and Self-Efficacy Scale Anthony R. Artino Jr. and
More information