(303C8) Social Research Methods on Psychology (Masters) Convenor: John Drury

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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

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