The Humour and bullying project: Modelling cross-lagged and dyadic data.

1. The Humour and bullying project: Modelling cross-lagged and dyadic data. Dr Simon C. Hunter School of Psychological Sciences and Health, University of Strathclyde email: simon.hunter@strath.ac.uk This research was funded by the Economic and Social Research Council, award reference RES-062-23-2647.

2. Project team and info P.I.: Dr Claire Fox, Keele University Research Fellow: Dr SiânJones Project team: SirandouSaidy Khan, Hayley Gilman, Katie Walker, Katie Wright-Bevans, Lucy James, Rebecca Hale, Rebecca Serella, Toni Karic, Mary-Louise Corr, Claire Wilson, and Victoria Caines. Thanks and acknowledgements: Teachers, parents and children in the participating schools. The ESRC. More info: Project blog: http://esrcbullyingandhumourproject.wordpress.com/ Twitter: @Humour_Bullying

3. Overview • Background and rationale to the Humour & Bullying project • Methods used in the project • AMOS – what is it, why use it (and pertinently, why not)? • Measurement models – achieving fit. • Cross-lagged analyses • Dyadic data – issues and analyses • Summary

4. What functions does humour serve? Humour Social: Strengthening relationships, but also excluding, humiliating, or manipulating others (Martin, 2007). Personal: To cope with dis/stress, esp. in reappraisal and in ‘replacing’ negative feelings (Martin, 2007). Both of these functions are directly relevant to bullying and peer-victimisation contexts.

5. Multi-dimensional (Fox et al., in press; Martin, 2007): • Self-enhancing: Not detrimental toward others (e.g. ‘I find that laughing and joking are good ways to cope with problems’). • Aggressive: Enhancing the self at the expense of others (e.g. ‘If someone makes a mistake I often tease them about it’). • Affiliative: Enhances relationships and can reduce interpersonal tensions (e.g. ‘I often make people laugh by telling jokes or funny stories’). • Self-defeating: Enhances relationships, but at the expense of personal integrity or one’s own emotional needs(e.g. ‘I often put myself down when making jokes or trying to be funny’). Humour

6. Repeated attacks on an individual. • Conceptualised as a continuum rather than a category. Peer-victimisation Also multi-dimensional in nature: • Verbal: Being teased or called names. • Physical: Hitting, kicking etc. Also includes property damage. • Social: Exclusion, rumour spreading.

7. Peer-victimisation • Clearly a stressful experience for many young people, associated with depressive symptomatology (Hunter et al., 2007, 2010), anxiety (Visconti et al., 2010), self-harm (Viljoen et al., 2005) and suicidal ideation (Dempsey et al., 2011; Kaltiala-Heino et al., 2009), PTSD (Idsoe et al., 2012; Tehrani et al., 2004), loneliness (Catterson & Hunter, 2010; Woodhouse et al., 2012), psychosomatic problems (Gini & Pozzoli, 2009), ...etc • Also a very social experience – peer roles can be broader than just victim or aggressor (Salmivalli et al., 1996).

8. Klein and Kuiper (2008): • Children who are bullied have much less opportunity to interact with their peers and so are at a disadvantage with respect to the development of humour competence. • Cross-sectional data support: Peer-victimisation is negatively correlated with both affiliative and self-enhancing humour (Fox & Lyford, 2009). • May be particularly true for victims of social aggression. Victims gravitate toward more use of self-defeating humour? • May be particularly true for victims of verbal aggression as peers directly supply the victim with negative self-relevant cognitions such as “You’re a loser”, “You’re stupid” etc which are internalised (see also Rose & Abramson, 1992, re. depressive cognitions). Peer-victimisation

9. Research aims Primarily, to evaluate the relationship between humour use, involvement in bullying (as victim or aggressor), and adjustment • Testing causal hypotheses, e.g., verbal victimisation will cause an increase in levels of self-defeating humour (Rose & Abramson, 1992) • Evaluating proposed explanatory causal pathways, e.g. the effect of victimisation on mental health is mediated via negative humour styles • Evaluating humour as a risk / resilience factor, e.g. high levels of positive humour may buffer young people against the negative effects of peer-victimisation • Assessing whether friendships serve as a contextual risk factor for peer-victimisation

10. Research aims Primarily, to evaluate the relationship between humour use, involvement in bullying (as victim or aggressor), and adjustment • Testing causal hypotheses, e.g., verbal victimisation will cause an increase in levels of self-defeating humour (Rose & Abramson, 1992) • Evaluating proposed explanatory causal pathways, e.g. the effect of victimisation on mental health is mediated via negative humour styles • Evaluating humour as a risk / resilience factor, e.g. high levels of positive humour may buffer young people against the negative effects of peer-victimisation • Assessing whether friendships serve as a contextual risk factor for peer-victimisation

11. N=1241 (612 male), 11-13 years old, from six Secondary schools in England. • Data collected at two points in time: At the start and at the end of the 2011-2012 school session. • Data collection spread over two sessions at each time point due to number of tasks. • N=807 present at all four data collection sessions. Methods

12. Self-Report: • 24-item Child Humour Styles Questionnaire (Fox et al., in press). Measures • 10-item Children’s Depression Inventory – Short Form (Kovacs, 1985). • 36-item Victimisation and Aggression (Owens et al., 2005). • 10-item Self-esteem (Rosenberg, 1965). • 4-item Loneliness(Asher et al., 1984; Rotenberg et al., 2005).

13. Peer-nomination: • Rated liking of all peers (Asher & Dodge, 1986). • Nominated friends and very best friend (Parker & Asher, 1993). • Peer-victimisation and use of aggression (Björkqvist et al., 1992), participants nominated up to three peers for Verbal, Physical, and Indirect. This was an 8-item measure. • Verbal: “Gets called nasty names by other children.” • Physical: “Gets kicked, hit and pushed around by other children.” • Indirect – “Gets left out of the group by other children” and “Has nasty rumours spread about them by other children.” • Humour (adapted from Fox et al., in press). This was a 4-item measure, young people nominated up to three peers for each item. Measures

14. AMOS • Easy to use graphical interface, comes as an SPSS bolt-on. • Can use for path analysis, assessment of measurement models, and structural equation modeling. • Includes features such as bootstrapping, modification indices, assessment of multivariate normality etc. • But… these can’t be used if you have missing data, on relevant items, in your SPSS data file!

15. Measurement models Depression Measurement models describe the relationships between indicators and latent variables. Latent variable  Manifest indicators  Error  Item 1 Item 2 Item 3 error1 error2 error3

16. Verbal Victimisation Physical Victimisation Item 1 Item 1 Item 2 Item 2 Item 3 Item 3 error1 error4 error2 error5 error3 error6 Measurement models Measurement models may have more complex structures.

17. Item 1 Item 2 Item 3 error1 error2 error3 General Victimisation Measurement models Resid2 Resid1 Physical Victimisation Verbal Victimisation Item 1 Item 2 Item 3 error4 error5 error6

18. Measurement models Measurement models may fit well when you model them in a straightforward manner in AMOS (like those just shown). If they don’t fit well, there are different ways to try and improve fit.

19. Verbal Victimisation Physical Victimisation Item 1 Item 1 Item 2 Item 2 Item 3 Item 3 error1 error4 error2 error5 error3 error6 Measurement models Correlate error terms:

20. Depression Measurement models Model the method variance: Item 1 [R] Item 2 Item 4 Item 3 [R] Item 5 [R] Item 6 error4 error5 error1 error2 error3 error6 Method

21. Depression Measurement models Create ‘parcels’ (Bandalos, 2002; Little et al., 2002) so that this… Item 1 Item 2 Item 4 Item 3 Item 5 Item 6 error4 error5 error1 error2 error3 error6

22. Measurement models Depression …becomes this Parcel 1 Parcel 2 Parcel 3 error5 error1 error3

23. Parcelling • Most likely to do this if you have a factor which has lots of items. • Need unidimensional construct: Parcelling is problematic if you are unsure of the factor structure. Little et al. (2002) suggest this is the biggest threat in terms of model misspecification. • Clearly, cannot be used when the goal of your analysis is to understand fully the relations among items • Some measures actually come with instructions to parcel (e.g. the Control, Agency, and Means–Ends Interview: Little et al., 1995)

24. Parcelling • May improve fit because fewer parameters are being estimated (so better sample size to variable ratio). This can therefore also be a way to deal with smaller sample sizes when you have measures with lots of indicators. • But, likely to improve fit across all models regardless of whether they are correctly specified – increasing the chances that we will fail to reject a model which should be rejected (Type II error)

25. Cross-lagged analyses • Omitted variable accounts for correlation Cross-sectional data are restricted in terms of unpacking causation relating to two correlated variables. Three explanations: • Both variables influence the other • One variable influences the other Verbal Victimisation Self-Defeating Humour Symptoms of Depression

26. Cross-lagged analyses Cross-lagged data, sometimes called panel data, allow us to move forward in our understanding of how the variables influence each other. • ‘A happened, followed by B’ design • More complex to analyse than cross-sectional data Critique • Omitted variable bias still a problem. • Only looks at group level change, not individual level (where latent growth curve models might be more appropriate)

27. Cross-lagged analyses T1 Victimisation T2 Victimisation The cross-lagged model (also referred to as a simplex model, an autoregressive model, a conditional model, or a transition model). Can the history of victimisation predict depression, taking into consideration the history of depression (and vice-versa)? e e T1 Depression T2 Depression

28. Cross-lagged analyses T1 Victimisation T2 Victimisation Can also include time-invariant predictors in the model. e e T1 Depression T2 Depression Gender

29. Cross-lagged analyses e e NB – the model can be extended to incorporate further time points. T1 Victimisation T2 Victimisation T3 Victimisation T1 Depression T2 Depression T3 Depression e e

30. Cross-lagged analyses Analysis 1: Victimisation and Internalising Internalising = withdrawal, anxiety, fearfulness, and depression (Rapport et al., 2001). Operationalised here as symptoms of depression and loneliness.

32. Competing models: A stability-only model • Stability paths T1 Social Victimisation T2 Social Victimisation T1 Verbal Victimisation T2 Verbal Victimisation T1 Physical Victimisation T2 Physical Victimisation T1 Internalising T2 Internalising

33. Competing models: A stability and a restricted cross-lagged model • Stability paths • PLUS cross-lagged within related concept only (victimisation) T1 Social Victimisation T2 Social Victimisation T1 Verbal Victimisation T2 Verbal Victimisation T1 Physical Victimisation T2 Physical Victimisation T1 Internalising T2 Internalising

34. Competing models: A fully cross-lagged model • Stability paths, cross-lagged within related concept • Cross-lagged across all variables T1 Social Victimisation T2 Social Victimisation T1 Verbal Victimisation T2 Verbal Victimisation T1 Physical Victimisation T2 Physical Victimisation T1 Internalising T2 Internalising

35. Cross-lagged analyses Fit indices • Chi-square: ideally, non-significant. • CMIN/DF: under 2 or 3. • CFI: > .95 = good, >.90 = adequate. • RMSEA: < .050 = good, < .080 = adequate. Results: • Model comparisons: All models significantly different from each other (ΔX2, p < .001).

36. Cross-lagged analyses Results:

37. Cross-lagged analyses Cross-sectional results: • Different forms of peer-victimisation all positively associated with each other (T1 = .69 to .78; T2 = .57 to .69). • Different forms of peer-victimisation all positively associated with internalising. Verbal = .54 (T2 = .39), Physical = .46 (T2 = .23), Social = .59 (T2 = .43). • Social and verbal seem most problematic

38. Cross-lagged analyses T1 Verbal Victimisation T2 Verbal Victimisation .11 .36 Cross-Lagged Results (only significant paths shown) -.14 T1 Social Victimisation T2 Social Victimisation .37 .12 .10 T1 Physical Victimisation T2 Physical Victimisation .47 .15 .17 .13 T1 Internalising T2 Internalising .57

39. Cross-lagged analyses Analysis 2: Victimisation, Humour, and Internalising

41. Cross-lagged analyses Results: • Model comparisons: All models were again significantly different from each other (ΔX2, p < .001).

42. Cross-lagged analyses Cross-sectional associations: T1 (T2)

43. Time 2 Self-Defeating Humour Self-Defeating Humour .11 .13 .07 Aggressive Humour Aggressive Humour .07 Self-Enhancing Humour Self-Enhancing Humour Time 1 Affiliative Humour .08 Affiliative Humour .20 -.17 Verbal Victimisation -.14 Verbal Victimisation -.14 .09 Social Victimisation .12 Social Victimisation -.13 .13 .15 Physical Victimisation Physical Victimisation .12 T1 Internalising T2 Internalising

44. Method summary • Useful for beginning to disentangle cause and effect • Not a panacea – still has limitations • Results summary • Humour may be self-reinforcing (virtuous cycle…of sorts) • (Mal)adjustment appears to be an important driver of both humour use and peer-victimisation • Implications for intervention re. adolescent mental health and adolescent attitudes toward those with mental health issues Cross-lagged analyses

45. Children in friendships are likely to produce data which are related in some way, violating the normal assumption that all data are independent. Usually, we just ignore this. • Shared experiences may shape friends’ similarity or their similarity may be what the attraction was to begin with. • The Actor-Partner Independence Model (APIM: Kenny, 1996) makes a virtue of this data structure. Dyadic analyses - APIM • Can conduct APIM analyses in SEM, or using MLM (where individual scores are seen as nested within groups with an n of 2)

46. Dyadic analyses • Looks a lot like the cross-lagged analysis • However, the data structure in SPSS is verydifferent. T1 Victimisation T2 Victimisation e e Friend’s T1 Victimisation Friend’s T2 Victimisation

47. Standard data entry in SPSS: Dyadic analyses

48. For APIM, data is entered by dyad, not person: Dyadic analyses

49. We can evaluate “actor effects” Dyadic analyses T1 Victimisation T2 Victimisation e e Friend’s T1 Victimisation Friend’s T2 Victimisation

50. We can evaluate “partner effects” Dyadic analyses T1 Victimisation T2 Victimisation e e Friend’s T1 Victimisation Friend’s T2 Victimisation