(303C8) Social Research Methods on Psychology (Masters) Convenor: John Drury
|
|
- Warren Baldwin
- 8 years ago
- Views:
Transcription
1 (303C8) Social Research Methods on Psychology (Masters) Convenor: John Drury Exercise EXC (60%) The re-sit for coursework is an exercise in which you must attempt one of two tasks in analysis and data interpretation, corresponding to the factor analysis and coding assignments. Specifically, you will either complete a Results section of a study based on some SPSS output or you will do part of a coding/reliability exercise. Please see attached paper.
2 THE UNIVERSITY OF SUSSEX 303C8 SOCIAL RESEARCH METHODS IN PSYCHOLOGY RESIT EXERCISE (60% weighting) Answer EITHER Section A OR Section B SECTION A A research student was interested in challenging the common conception of online Social Network Services (SNSs) as having negative effects - for example through lowering self-esteem or fostering narcissism (e.g., Kraut et al. 1998; Mehdizadeh, 2009). Others point to SNSs as potentially beneficial for those who struggle with face-to-face interaction, due to the greater ease and control that online interaction allows (e.g., Livingston, 2008). SNS participation also has the potential to provide approval or afffirmation and a sense of connection with others, and these possible benefits have received little attention to date. She developed a questionnaire to investigate both negative and positive aspects of participation in the well-known online SNS Facebook. She expected to identify two underlying factors, one of which would capture the positive aspects of Facebook use (the benefits or advantages) and the other which would capture more negative aspects. She also designed some items to tap participants general attitude towards online communication. She devised the following the scales, which she administered to 152 participants: On a 1-7 scale, 1 being not at all and 7 being very much, please indicate how much you agree with these statements: Positive Aspects: 1. When people comment on my pictures it makes me feel happy 2. When people comment on my wall it makes me feel happy 3. When people comment on things I post online I feel connected 4. Sharing what I m thinking or doing with others on social networking sites makes me feel good. 5. It boosts my confidence when people respond to my posts/status updates 6. I feel popular when I m tagged in a friend s photos 7. When people comment on my updates it makes me feel appreciated 8. When one of my friends comments on my activity on a social networking site it gives me recognition. 9. I feel accepted when I get comments on status updates and posts Negative Aspects: 10. It doesn t bother me if no one responds to my online activity (reverse scored) 1
3 11. When people don t comment on my pictures it makes me feel sad 12. When people don t leave comments on my wall it makes me feel sad 13. I feel bad when I don t get a response to something I post 14. I feel ignored when I receive no comments on things I post 15. It feels as though no one is interested in me when they do not comment on my activities Attitude towards online communication: 16. I feel I can be my real self online 17. I have told someone something online that I would never tell them in person 18. I feel like people pay more attention to what I have to say online than offline 19. I prefer the time I have to think before writing a response to someone online. 20. A virtual friend is an adequate substitute for an offline friend She ran a factor analysis and reliability analysis on her questionnaire data; the output of these analyses are below. 2
4 Correlation Matrix a Correlation a. Determinant = 3.31E-007 3
5 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity.903 Approx. Chi-Square df 190 Sig Communalities Initial Extraction Extraction Method: Principal Axis Factoring. 4
6 Total Variance Explained Factor Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Axis Factoring. 5
7 Factor Matrix a Factor Extraction Method: Principal Axis Factoring. a. 4 factors extracted. 16 iterations required. Rotated Factor Matrix a Factor Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. Factor Transformation Matrix Factor Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization. 6
8 Case Processing Summary N % Cases Valid Excluded a 0.0 Total a. Listwise deletion based on all variables in the procedure. Reliability Statistics Cronbach's Alpha Based on Cronbach's Alpha Standardized Items N of Items Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item- Total Correlation Squared Multiple Correlation Cronbach's Alpha if Item Deleted Reliability Statistics Cronbach's Alpha Based on Cronbach's Alpha Standardized Items N of Items
9 Scale Mean if Scale Variance if Item Deleted Item Deleted Item-Total Statistics Corrected Item- Squared Multiple Cronbach's Alpha Total Correlation Correlation if Item Deleted Reliability Statistics Cronbach's Alpha N of Items Item-Total Statistics Scale Mean if Scale Variance Corrected Item- Cronbach's Item Deleted if Item Deleted Total Alpha if Item Correlation Deleted
10 Using the output above, write a Results section to report: a) the factor analysis of the questionnaire b) the reliability analysis of the resulting scales 303C8 Social Research Methods in Psychology - RESIT Finally, write a brief Discussion section commenting on the success of the questionnaire and make recommendations for improvement and/or further development of the scales. You are not expected to draw on any theoretical knowledge other than what is presented in the study description. Please state here if you disagree with any of the decisions made by the researcher and explain your reasons. 9
11 SECTION B Coding and Intra-rater Reliability (Re-sit) Introduction and background This re-sit assignment is provided for students needing to make up coursework offered during Spring term. Whereas in the term-time workshop, students calculated reliability estimates for two observers (inter-observer reliability), this series of exercises is designed around the calculation of reliability in the same observer, who observes the same behavioural records twice (intra-observer reliability). Otherwise, this re-sit assignment is mathematically identical to the assignment offered during the Spring term. You will learn to calculate to what extent the same coder (you) agrees on their coding of observational data, coding at two different time points. As you will see, coding of behaviour is laborious and time-consuming. Thus, when dealing with performance data (as opposed to questionnaires, for example), researchers often check the intra-coder reliability only for parts of their data. In this exercise, the behavioural records are relatively short, so reliability will be calculated on the whole sample of 24 videoclips. The videoclips are available on Study Direct. The key principle to bear in mind is that reliability assessment estimates the reliability of a coding scheme. It is easy to fall into the mental trap that reliability estimates are about the diligence of the observers; this is not necessarily the case. Diligence is usually assumed: Nobody wants to invest enormous blocks of their time into writing grant proposals, collecting behavioural records, and converting those behavioural records into usable data if they cannot, ultimately, publish their findings in the peer-reviewed literature, due to low reliability of their coding scheme. Reliability can be relatively low for a large number of reasons, here are some examples from my experience: 1. The behavioural record may be poor. My first paper was based on coding from oldfashioned videotapes, recording chimpanzee behaviour in a biomedical research center. We filmed indoors, and the videotapes were consequently dark. The animals are covered in dark fur, and were often backlit from sunlight streaming in through a connecting door to the outside. On top of all of this, we were filming through cage mesh. Our inter-observer reliability for some measures was downright mediocre (Cohen's kappas of.52 and.55 for the presence of vocalisations and gaze alternating behaviour, respectively). However, the majority of the behavioural measures were more reliable, so despite only "fair" agreement for these two measures, we were able to publish the research. 2. The coding scheme may not be detailed enough. For example, in the above study, I was one of two coders. We were interested in chimpanzee pointing. When I recorded every time the chimpanzees put their fingers through the cage mesh, I unconsciously ignored those occasions in which the animals were actually touching something with their fingertips. However, my instructions to the second coder did not have that as an explicit instruction, so the second coder recorded a small number of chimpanzee probes that I did not record. This was an easy problem to address (we simply deleted these probes from the analyses), but is an example of how important it is to clearly describe, in writing, the target behaviour. 3. The behaviour may be very subtle. Some behaviours are not obvious, and it is probably obvious that subtle behaviours are more easily overlooked than less subtle behaviours. There are several approaches to assessing reliability. Researchers may establish a training record, and train observers to acceptable levels of reliability before turning them loose on the primary records. Alternatively, researchers might schedule coding so that there is some overlap between multiple coders (typically, about 15% of the sample, although this varies quite a bit, in practice). Finally, the most risky method is to have one person code an entire record, then assign a separate observer to code a portion of the record (again, 15% of 10
12 the sample is typical); with this latter method, if reliability is unacceptably low, then the coding scheme must be revisited, and the entire behavioural record re-coded. In this re-sit exercise, you will code gaze aversion using 24 short videoclips. These are videoclips of psychotherapists working in southern California, who are advertising themselves and their therapeutic approaches to potential clients. The videoclips will be provided to you, either on Study Direct or another medium. When you have finished coding the 24 short videoclips, you should then code them a second time and compare your second set of scores with your first set of scores. Please calculate the percent agreement rate as well as Cohen s kappa (see below). Your exercise The coding sheet is provided on page 14, and an example worksheet appears below. Simply navigate to Study Direct (or alternative video source, if provided) and select the videoclip you wish to run from your computer. These videoclips are segments from longer clips in which therapists advertise themselves and their practices in the United States. For purposes of this exercise, I would like you to record whether or not the therapists averted their gaze from a central position. It is of hypothetical interest, for example, whether particular gaze orienting patterns in therapists are more or less attractive to people with different kinds of mental distress seeking therapy. Note that some therapists seem to primarily look directly at the camera, whereas some others seem to look just to one side of it. Perhaps, for example, depressed people might prefer therapists who avert their gaze frequently, whereas people with certain conduct disorders might prefer therapists who maintain more direct gaze. So, for this exercise, we are asking you to imagine that you are coding for a hypothetical study of gaze orienting patterns in psychotherapists. Whatever seems to be the preferred primary fixation point of any given therapist (either at camera or slightly offset), please simply note whether or not the therapist averted her gaze from this central position during each videoclip. A worksheet is provided for you to record your responses (see page 15). In the column labelled "First Obs." for each clip, please enter a "1" if the therapist averts her gaze, and a "0" if she does not avert her gaze. When the coding is complete, repeat this exercise, and enter this second set of observations under "Second Ob." In the column labelled "Agreement?" please write: "a" if you scored that gaze aversion occurred both times., "b" if you scored gaze aversion in the First Obs. ("1"), but scored it as not occurring in the Second Obs. ("0"). "c" if you scored that gaze aversion did not occur in the First Obs. ("0"), but scored gaze aversion as occurring in the Second Obs. ("1"). ; and, "d" if you recorded that gaze aversion did not occur during that clip during both observations. These notations are exemplified in the following hypothetical example. A Hypothetical Example In this hypothetical example, an observer viewed the same videoclips that you are about to code, with the following distribution of results (see next page). Clip First Obs. Second Obs. Avert gaze? Avert gaze? Agreement?* Sara Clip c Sara Clip a Sara Clip a Sara Clip c 11
13 Carla Clip a Carla Clip d Carla Clip b Carla Clip c Lexi Clip d Lexi Clip a Lexi Clip a Lexi Clip d Rhoni Clip a Rhoni Clip a Rhoni Clip b Rhoni Clip c Meghan Clip d Meghan Clip b Meghan Clip a Meghan Clip a Audrey Clip a Audrey Clip b Audrey Clip c Audrey Clip d 303C8 Social Research Methods in Psychology - RESIT In this example, then, an observer coded 24 short film clips for the presence or absence of gaze aversion. To compute kappa, we need to know how many times: a) the observer reported that gaze aversion (GA) occurred both times the clip was viewed (10 "a" codes), b) the observer reported that it did occurred on first viewing, but not the second (4 "b" codes), c) the observer reported that gaze aversion did not occur on first viewing, but that it did occur in the second viewing (5 "c" codes), and, d) the observer reported no GA in both viewings (5 "d" codes). Putting these data into a contingency table renders the following (cell identity in brackets): Table 1: Observed Frequencies. Second Obs. Gaze Aversion? Yes No First Obs. Yes (a) 10 (b) 4 14 Gaze Aversion? No (c) 5 (d) Percent Agreement: In this example, the observer agreed that GA occurred 10 times (cell a). He or she agreed that GA did not occur 5 times (cell d). Their percent agreement is, therefore: (a + d)/(a+b+c+d) or, more simply: (a + d)/(total number of observations) or: (10 + 5)/(24) 12
14 = (15/24) = = 62.5% (~63%) This is not very good intra-rater agreement between these two coding events (First Obs. compared with Second Obs.). Cohen's kappa: In contrast to the percent agreement rate, Cohen s kappa takes agreement occurring by random chance into account. The formula for kappa is: PA refers to the observed probability of agreement among raters (or, in the present case, between two independent codings of the same behavioural records by the same observer) and PC refers to the probability that agreement is due to chance. That is, you have to compare the observed agreements with the agreements that are due to chance. To calculate kappa, first translate Table 1, the observed frequencies, into a table of observed probabilities (or, in other words, proportions). Simply divide the frequencies in each cell of Table 1 (a, b, c, & d) by the total number of observations (24). In Table 2, I have done this and added some labels for the marginal totals (e, f, g, & h). From the kappa formula, above, notice that you need to compute only two terms: PA and PC. PA is simply the sum of the observed probabilities of agreement in Table 2, or the sum of Cells a + d (= =.625); compare this with your calculation of percent agreement, above. Note that the percent agreement is obtained by multiplying the observed probability of agreement (PA) by 100. Table 2: Observed (or Actual) Probabilities. Observer B Gaze Aversion? Yes No Observer A Yes (a).417 (b).167 (g).584 Gaze Aversion? No (c).208 (d).208 (h).416 (e).625 (f) To calculate PC, you have to work out what the expected probabilities are for Cells (a) and (d). Another way to look at the expected probability for Cell (a) is as the probability that an observer will say that gaze aversion (GA) occurred, by random chance alone. Conversely, the expected probability of agreement for Cell (d) is the probability that an observer will agree across two coding events that GA did not occur, by random chance alone. So, I will call the first probability, PYES (the probability of two "YES" responses; i.e., the overall probability that an observer said that GA occurred) and the second probability, PNO (the probability of two "NO" responses, or the overall probability that an observer said that GA did not occur over two coding events). Table 3: Expected Probability of Agreement by Random Chance 13
15 Alone Second Obs. Gaze Aversion? Yes No First Obs. Yes (a).365 (b) (g).584 Gaze Aversion? No (c) (d).156 (h).416 (e).625 (f) For these calculations, please refer to Table 3, and note that we do not need to calculate any entries for Cells (b) or (c). In other words, Cohen's kappa takes the observed agreement and revises downwards from that--if your kappa exceeds your observed probability of agreement, you have done something wrong. The formulae are very simple: PYES = (e)(g) = (.625)(.584) =.365 and, PNo = (f)(h) = (.375)(.416) =.156 To calculate PC, we simply take the sum of PYES + PNo (= =.521). Finally, now all we have to do is to insert PA (.625) and PC (.521) into our kappa formula: Substituting our values: κ = ( )/( ) =.104/.479 =.217 So, in this case, there is very poor agreement between the records created during the first coding, compared to the second coding; this signifies that the coder has 'drifted' in their application of the coding scheme, or possibly that one or both codings of the gaze behaviour was affected by distractions. Expanding kappa The example given is of a dichotomous measure, it takes one of only two values: (a) the behaviour occurred or (b) the behaviour did not occur. Cohen's kappa also works for behavioural measures that can take more than two values. For example, in our studies of ape gestures, we would analyse the dichotomous measure of whether or not any given subject displayed a manual gesture, but also we explore the handedness of their gestures. Usually, we measure inter-observer reliability, rather than intra-observer reliability, so we'll take the interobserver situation as an example, here. (However, note that the calculations are identical whether we're assessing inter-observer or intra-observer reliability. So, for any given gesture, two observers would code whether the gesture was displayed with the left, the right, or both hands. So, you might think that the kappa analysis for each observer might look something like the following, in Table 4: 14
16 Table 4: Hypothetical Variables 303C8 Social Research Methods in Psychology - RESIT Left hand Right hand Both hands No gesture But this actually confounds two different questions, from the standpoint of reliability assessment. The first question is, "Did a manual gesture occur?" and this would be assessed for reliability the same way we have been doing it in the example above: Table 5: Did a Gesture Occur? (Hypothetical example) Observer B Gesture? Yes Observer A Yes (a) 42 (b) 7 (g) 49 Gesture? No (c) 3 (d) 32 (h) 35 No (e) 45 (f) So, the first question about reliability is whether two observers agree that a gesture occurred. The second question relates to whether two observers agree about the hand used during manual gestures. For this analysis, only the observations in Cell (a) are used. This is because it is logically incoherent to agree about which hand was used when a subject did not gesture, or when two observers could not agree whether a gesture occurred. These 42 hypothetical agreements are distributed in the following way (see Table 6, next page): Table 6: Contingency table for handedness of gestures (hypothetical data). Left Right Both Left (a) 14 (b) 2 (c) 2 (m) 18 Right (d) 3 (e) 18 (f) 0 (n) 21 Both (g) 0 (h) 1 (i) 2 (o) 3 (j) 17 (k) 21 (l) 4 42 In Table 6, the rows represent Observer A and the columns represent Observer B. To calculate kappa simply convert the cell frequencies to proportions, as we did in Table 2. Table 7: Observed (Actual) Proportion Agreement Left Right Both Left (a).33 (b).05 (c).05 (m).43 Right (d).07 (e).43 (f).00 (n).50 Both (g).00 (h).02 (i).05 (o).07 (j).40 (k).50 (l) Note that the cell labels are more numerous, because we have more cells in this table, but the calculations are exactly the same. To get PA, we simply sum across the diagonal of agreement (a + e + i = =.81). (To get percent agreement, just multiply.81 times 100: 81%.) To get PC we need to sum the marginal cross-products: (j)(m) + (k)(n) + (l)(o) =
17 =.43. Now, to calculate kappa, we substitute these values into the kappa formula: Substituting our values: κ = ( )/(1 -.43) =.38/.57 =.67 So, in this case, there is moderately good inter-observer reliability. Your In-class Coding Practice Exercise. At this point, play the 24 videoclips in any order you like. Please record your responses in the worksheet, below ("1" for gaze aversion occurs during the clip, and "0" for no gaze aversion). Remember that some of the people do not look directly at the camera, but at somebody slightly to one side of the camera--this is their central focus, so please code gaze aversion if they glance away from this central focus. Please feel free to use and append additional sheets, if you require more space. Clip First Obs. Second Obs. Avert gaze? Avert gaze? Agreement?* Sara Clip 1 Sara Clip 2 Sara Clip 3 Sara Clip 4 Carla Clip 1 Carla Clip 2 Carla Clip 3 Carla Clip 4 Lexi Clip 1 Lexi Clip 2 Lexi Clip 3 Lexi Clip 4 Rhoni Clip 1 Rhoni Clip 2 Rhoni Clip 3 Rhoni Clip 4 Meghan Clip 1 Meghan Clip 2 Meghan Clip 3 Meghan Clip 4 Audrey Clip 1 Audrey Clip 2 Audrey Clip 3 Audrey Clip Enter your coding data in this table. (Exercise continues on next page)
18 2. Calculate the percent agreement between your first observation (First Obs.) and your second observation (Second Obs.); you may find it helpful to draw a contingency table of the observed frequencies, as in Table 1). 3. Calculate Cohen's kappa for your data. This response has 3 parts: Calculate PA, calculate PC, and calculate kappa. Space is given so you can draw the relevant contingency tables, as in the examples, above. 3a. Calculate the observed probability of agreement (PA) from the observed frequencies. 3b. Calculate the expected probability of agreement due to random chance (PC). 3c. Calculate Cohen's kappa. 17
19 4. Briefly explain how you would approach reliability assessment in this exercise if instead of simply coding the presence or absence of gaze aversion, you had coded both the presence and absence of gaze aversion and its direction. 5. From your answer in Question 4, above, draw and label a contingency table for assessment of reliability of gaze aversion direction. END OF PAPER 18
Factor 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 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 informationCHAPTER 4 KEY PERFORMANCE INDICATORS
CHAPTER 4 KEY PERFORMANCE INDICATORS As the study was focused on Key Performance Indicators of Information Systems in banking industry, the researcher would evaluate whether the IS implemented in bank
More informationA 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 informationMain Effects and Interactions
Main Effects & Interactions page 1 Main Effects and Interactions So far, we ve talked about studies in which there is just one independent variable, such as violence of television program. You might randomly
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 informationChapter VIII Customers Perception Regarding Health Insurance
Chapter VIII Customers Perception Regarding Health Insurance This chapter deals with the analysis of customers perception regarding health insurance and involves its examination at series of stages i.e.
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 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 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 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 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 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 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 informationHow to Get More Value from Your Survey Data
Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................2
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 information5.2 Customers Types for Grocery Shopping Scenario
------------------------------------------------------------------------------------------------------- CHAPTER 5: RESULTS AND ANALYSIS -------------------------------------------------------------------------------------------------------
More informationBusiness Performance Evaluation Model for the Taiwan Electronic Industry based on Factor Analysis and AHP Method
Proceedings of the World Congress on Engineering 200 Vol III WCE 200, June 0 - July 2, 200, London, U.K. Business Performance Evaluation Model for the Taiwan Electronic Industry based on Factor Analysis
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 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 informationTools for Excel Modeling. Introduction to Excel2007 Data Tables and Data Table Exercises
Tools for Excel Modeling Introduction to Excel2007 Data Tables and Data Table Exercises EXCEL REVIEW 2009-2010 Preface Data Tables are among the most useful of Excel s tools for analyzing data in spreadsheet
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 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 informationA STUDY ON ONBOARDING PROCESS IN SIFY TECHNOLOGIES, CHENNAI
A STUDY ON ONBOARDING PROCESS IN SIFY TECHNOLOGIES, CHENNAI ABSTRACT S. BALAJI*; G. RAMYA** *Assistant Professor, School of Management Studies, Surya Group of Institutions, Vikravandi 605652, Villupuram
More informationA Basic Guide to Analyzing Individual Scores Data with SPSS
A Basic Guide to Analyzing Individual Scores Data with SPSS Step 1. Clean the data file Open the Excel file with your data. You may get the following message: If you get this message, click yes. Delete
More informationEffectiveness of Performance Appraisal: Its Outcomes and Detriments in Pakistani Organizations
Effectiveness of Performance Appraisal: Its Outcomes and Detriments in Pakistani Organizations Hafiz Muhammad Ishaq Federal Urdu University of Arts, Science and Technology, Islamabad, Pakistan E-mail:
More informationJOB SATISFACTION DURING RECESSION PERIOD: A STUDY ON PUBLIC & PRIVATE INSURANCE IN PUNJAB
JOB SATISFACTION DURING RECESSION PERIOD: A STUDY ON PUBLIC & Hardeep Kaur* PRIVATE INSURANCE IN PUNJAB Abstract: This study is on the public and private sector employees of insurance sector to measure
More informationRecommend Continued CPS Monitoring. 63 (a) 17 (b) 10 (c) 90. 35 (d) 20 (e) 25 (f) 80. Totals/Marginal 98 37 35 170
Work Sheet 2: Calculating a Chi Square Table 1: Substance Abuse Level by ation Total/Marginal 63 (a) 17 (b) 10 (c) 90 35 (d) 20 (e) 25 (f) 80 Totals/Marginal 98 37 35 170 Step 1: Label Your Table. Label
More informationA STUDY ON FACTORS AFFECTING INDIVIDUALS INVESTMENT TOWARDS LIFE INSURANCE POLICIES
A STUDY ON FACTORS AFFECTING INDIVIDUALS INVESTMENT TOWARDS LIFE INSURANCE POLICIES *Heena Kothari Professor Altius Institute of Universal Studies, Indore (MP) India Email: heena.kothari@altius.ac.in **Roopam
More informationUsing MS Excel to Analyze Data: A Tutorial
Using MS Excel to Analyze Data: A Tutorial Various data analysis tools are available and some of them are free. Because using data to improve assessment and instruction primarily involves descriptive and
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 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 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 informationISSN: 2321-7782 (Online) Volume 2, Issue 3, March 2014 International Journal of Advance Research in Computer Science and Management Studies
ISSN: 2321-7782 (Online) Volume 2, Issue 3, March 2014 International Journal of Advance Research in Computer Science and Management Studies Research Article / Paper / Case Study Available online at: www.ijarcsms.com
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 informationData Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
More informationJanuary 26, 2009 The Faculty Center for Teaching and Learning
THE BASICS OF DATA MANAGEMENT AND ANALYSIS A USER GUIDE January 26, 2009 The Faculty Center for Teaching and Learning THE BASICS OF DATA MANAGEMENT AND ANALYSIS Table of Contents Table of Contents... i
More informationWhat Are Principal Components Analysis and Exploratory Factor Analysis?
Statistics Corner Questions and answers about language testing statistics: Principal components analysis and exploratory factor analysis Definitions, differences, and choices James Dean Brown University
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 informationPRINCIPAL COMPONENT ANALYSIS
1 Chapter 1 PRINCIPAL COMPONENT ANALYSIS Introduction: The Basics of Principal Component Analysis........................... 2 A Variable Reduction Procedure.......................................... 2
More informationAstitva International Journal of Commerce Management and Social Sciences
AN EMPIRICAL EVALUATION OF CRITICAL SUCCESS FACTORS OF KNOWLEDGE MANAGEMENT FOR ORGANIZATIONAL SUSTAINABILITY ABSTRACT DR. ANLI SURESH Assistant Professor of Commerce Madras Christian College Chennai anli.sgain@gmail.com
More informationEntrepreneurs of Small Scale Sector: A Factor Analytical Study of Business Obstacles
Entrepreneurs of Small Scale Sector: A Factor Analytical Study of Business Obstacles Anil Kumar Associate professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar
More informationFactorial Invariance in Student Ratings of Instruction
Factorial Invariance in Student Ratings of Instruction Isaac I. Bejar Educational Testing Service Kenneth O. Doyle University of Minnesota The factorial invariance of student ratings of instruction across
More information1/27/2013. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Introduce moderated multiple regression Continuous predictor continuous predictor Continuous predictor categorical predictor Understand
More informationTHE INTERNATIONAL JOURNAL OF BUSINESS & MANAGEMENT
THE INTERNATIONAL JOURNAL OF BUSINESS & MANAGEMENT Customer Preference and Satisfaction towards Housing Finance with Special Reference to Vijayawada, Andhra Pradesh P. Krishna Priya Assistant Professor,
More informationMultivariate Analysis of Variance (MANOVA): I. Theory
Gregory Carey, 1998 MANOVA: I - 1 Multivariate Analysis of Variance (MANOVA): I. Theory Introduction The purpose of a t test is to assess the likelihood that the means for two groups are sampled from the
More informationMeasuring Evaluation Results with Microsoft Excel
LAURA COLOSI Measuring Evaluation Results with Microsoft Excel The purpose of this tutorial is to provide instruction on performing basic functions using Microsoft Excel. Although Excel has the ability
More informationCard sort analysis spreadsheet
Interaction Design Instructions for use: Card sort analysis spreadsheet June 2007 Page Card sort analysis spreadsheet About the spreadsheet I have an Excel spreadsheet that I use to analyze data from physical
More informationA Survey Instrument for Identification of the Critical Failure Factors in the Failure of ERP Implementation at Indian SMEs
A Survey Instrument for Identification of the Critical Failure Factors in the Failure of ERP Implementation at Indian SMEs ABSTRACT Dr. Ganesh L 1, Arpita Mehta 2 Many quantitative and qualitative studies
More informationReliability Overview
Calculating Reliability of Quantitative Measures Reliability Overview Reliability is defined as the consistency of results from a test. Theoretically, each test contains some error the portion of the score
More informationDrawing a histogram using Excel
Drawing a histogram using Excel STEP 1: Examine the data to decide how many class intervals you need and what the class boundaries should be. (In an assignment you may be told what class boundaries to
More informationIntroduction to. Hypothesis Testing CHAPTER LEARNING OBJECTIVES. 1 Identify the four steps of hypothesis testing.
Introduction to Hypothesis Testing CHAPTER 8 LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 Identify the four steps of hypothesis testing. 2 Define null hypothesis, alternative
More informationThis chapter discusses some of the basic concepts in inferential statistics.
Research Skills for Psychology Majors: Everything You Need to Know to Get Started Inferential Statistics: Basic Concepts This chapter discusses some of the basic concepts in inferential statistics. Details
More informationThe Campbell Collaboration www.campbellcollaboration.org. Study content coding Effect size coding (next session)
Systema(c Review Methods Workshop Study Coding Sandra Jo Wilson Vanderbilt University Editor, Campbell Collabora9on Educa9on Coordina9ng Group Workshop Overview Levels of Study Coding Study eligibility
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 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 informationAn Empirical Examination of the Relationship between Financial Trader s Decision-making and Financial Software Applications
Empirical Examination of Financial decision-making & software apps. An Empirical Examination of the Relationship between Financial Trader s Decision-making and Financial Software Applications Research-in-Progress
More informationIntroduction to Data Tables. Data Table Exercises
Tools for Excel Modeling Introduction to Data Tables and Data Table Exercises EXCEL REVIEW 2000-2001 Data Tables are among the most useful of Excel s tools for analyzing data in spreadsheet models. Some
More informationQ&As: Microsoft Excel 2013: Chapter 2
Q&As: Microsoft Excel 2013: Chapter 2 In Step 5, why did the date that was entered change from 4/5/10 to 4/5/2010? When Excel recognizes that you entered a date in mm/dd/yy format, it automatically formats
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 informationFinancial Innovation and Advancing Information Technology Values in Indian Banks
Research Journal of Management Sciences ISSN 2319 1171 Financial Innovation and Advancing Information Technology Values in Indian Banks Abstract Deepika Upadhyaya and Manish Badlani Faculty of Management
More informationSegmentingIndianConsumersAPsychographicApproach
Global Journal of Management and Business Research: E Marketing Volume 14 Issue 3 Version 1.0 Year 2014 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA)
More informationLevels of measurement in psychological research:
Research Skills: Levels of Measurement. Graham Hole, February 2011 Page 1 Levels of measurement in psychological research: Psychology is a science. As such it generally involves objective measurement of
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 informationProcedures for Estimating Internal Consistency Reliability. Prepared by the Iowa Technical Adequacy Project (ITAP)
Procedures for Estimating Internal Consistency Reliability Prepared by the Iowa Technical Adequacy Project (ITAP) July, 003 Table of Contents: Part Page Introduction...1 Description of Coefficient Alpha...3
More informationHMRC Tax Credits Error and Fraud Additional Capacity Trial. Customer Experience Survey Report on Findings. HM Revenue and Customs Research Report 306
HMRC Tax Credits Error and Fraud Additional Capacity Trial Customer Experience Survey Report on Findings HM Revenue and Customs Research Report 306 TNS BMRB February2014 Crown Copyright 2014 JN119315 Disclaimer
More informationSoftware Solutions - 375 - Appendix B. B.1 The R Software
Appendix B Software Solutions This appendix provides a brief discussion of the software solutions available to researchers for computing inter-rater reliability coefficients. The list of software packages
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 informationPreparing a budget template and presenting financial graphs using Excel
Preparing a budget template and presenting financial graphs using Excel KLA: STAGE: UNIT: Topic: HSIE Stage 5 Commerce Personal Finance Budgeting, loans and insurance Outcomes: 5.1 Applies consumer, financial,
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 informationChi Square Tests. Chapter 10. 10.1 Introduction
Contents 10 Chi Square Tests 703 10.1 Introduction............................ 703 10.2 The Chi Square Distribution.................. 704 10.3 Goodness of Fit Test....................... 709 10.4 Chi Square
More informationUSC Marshall School of Business Academic Information Services. Excel 2007 Qualtrics Survey Analysis
USC Marshall School of Business Academic Information Services Excel 2007 Qualtrics Survey Analysis DESCRIPTION OF EXCEL ANALYSIS TOOLS AVAILABLE... 3 Summary of Tools Available and their Properties...
More informationSimple Linear Regression, Scatterplots, and Bivariate Correlation
1 Simple Linear Regression, Scatterplots, and Bivariate Correlation This section covers procedures for testing the association between two continuous variables using the SPSS Regression and Correlate analyses.
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 informationWorking with SPSS. A Step-by-Step Guide For Prof PJ s ComS 171 students
Working with SPSS A Step-by-Step Guide For Prof PJ s ComS 171 students Contents Prep the Excel file for SPSS... 2 Prep the Excel file for the online survey:... 2 Make a master file... 2 Clean the data
More informationUncertain Supply Chain Management
Uncertain Supply Chain Management 4 (2016) ** ** Contents lists available at GrowingScience Uncertain Supply Chain Management homepage: www.growingscience.com/uscm An investigation into the determinants
More informationPRELIMINARY ITEM STATISTICS USING POINT-BISERIAL CORRELATION AND P-VALUES
PRELIMINARY ITEM STATISTICS USING POINT-BISERIAL CORRELATION AND P-VALUES BY SEEMA VARMA, PH.D. EDUCATIONAL DATA SYSTEMS, INC. 15850 CONCORD CIRCLE, SUITE A MORGAN HILL CA 95037 WWW.EDDATA.COM Overview
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 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 informationIssues in Information Systems Volume 15, Issue II, pp. 270-275, 2014
EMPIRICAL VALIDATION OF AN E-LEARNING COURSEWARE USABILITY MODEL Alex Koohang, Middle Georgia State College, USA, alex.koohang@mga.edu Joanna Paliszkiewicz, Warsaw University of Life Sciences, Poland,
More informationAnalysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk
Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk Structure As a starting point it is useful to consider a basic questionnaire as containing three main sections:
More informationOne-Way ANOVA using SPSS 11.0. SPSS ANOVA procedures found in the Compare Means analyses. Specifically, we demonstrate
1 One-Way ANOVA using SPSS 11.0 This section covers steps for testing the difference between three or more group means using the SPSS ANOVA procedures found in the Compare Means analyses. Specifically,
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 informationPERCEIVED ROLE OF BUSINESS SCHOOL IN DEVELOPING LEADERSHIP IN STUDENTS
IMPACT: International Journal of Research in Business Management (IMPACT: IJRBM) ISSN(E): 2321-886X; ISSN (P): 2347-4572 Vol. 3, Issue 8, Aug 2015, 33-46 Impact Journals PERCEIVED ROLE OF BUSINESS SCHOOL
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 informationAN ILLUSTRATION OF COMPARATIVE QUANTITATIVE RESULTS USING ALTERNATIVE ANALYTICAL TECHNIQUES
CHAPTER 8. AN ILLUSTRATION OF COMPARATIVE QUANTITATIVE RESULTS USING ALTERNATIVE ANALYTICAL TECHNIQUES Based on TCRP B-11 Field Test Results CTA CHICAGO, ILLINOIS RED LINE SERVICE: 8A. CTA Red Line - Computation
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 informationASIAN JOURNAL OF MANAGEMENT RESEARCH Online Open Access publishing platform for Management Research
Online Open Access publishing platform for Management Research Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research Article ISSN 2229 3795 Unique selling proposition in
More informationCaring for depression
Caring for depression Aetna Health Connections SM Disease Management Program Get information. Get help. Get better. 21.05.300.1 B (6/08) Get back to being you How this guide can help you Having an ongoing
More informationSimulating Chi-Square Test Using Excel
Simulating Chi-Square Test Using Excel Leslie Chandrakantha John Jay College of Criminal Justice of CUNY Mathematics and Computer Science Department 524 West 59 th Street, New York, NY 10019 lchandra@jjay.cuny.edu
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 informationInvestorsInvestmentDecisionsinCapitalMarketKeyFactors
Global Journal of Management and Business Research: C Finance Volume 15 Issue 4 Version 1.0 Year 2015 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA)
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 informationThe Staffing Climate in Nursing: Concept and Measurement
The Staffing Climate in Nursing: Concept and Measurement Holly A. De Groot, PhD, RN, FAAN Chief Executive Officer Laura O. McIntosh, MS, RN Senior Research Consultant Catalyst Systems, LLC Keeping Patients
More informationAwareness and Willingness to Pay for Health Insurance: An Empirical Study with Reference to Punjab India
Awareness and Willingness to Pay for Health Insurance: An Empirical Study with Reference to Punjab India Dr. Sumninder Kaur Bawa Sr. Lecturer Department of Commerce and Business Management Guru Nanak Dev
More informationEXCEL Tutorial: How to use EXCEL for Graphs and Calculations.
EXCEL Tutorial: How to use EXCEL for Graphs and Calculations. Excel is powerful tool and can make your life easier if you are proficient in using it. You will need to use Excel to complete most of your
More informationTest Reliability Indicates More than Just Consistency
Assessment Brief 015.03 Test Indicates More than Just Consistency by Dr. Timothy Vansickle April 015 Introduction is the extent to which an experiment, test, or measuring procedure yields the same results
More informationSimple Random Sampling
Source: Frerichs, R.R. Rapid Surveys (unpublished), 2008. NOT FOR COMMERCIAL DISTRIBUTION 3 Simple Random Sampling 3.1 INTRODUCTION Everyone mentions simple random sampling, but few use this method for
More informationAn empirical study of factor analysis on M & A performance of listed companies of Chinese pharmaceutical industry
Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(4):963-968 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 An empirical study of factor analysis on M & A performance
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 information