1. Distinctions from the APIM and the CFM Adam M. Galovan, Erin Kramer Holmes, and Christine M. Proulx

2. Theory Behind Similarity As stated in Segment 1: Individual functioning is related not only to the individuals themselves, but also to the complex system of behaviors between members of the system. We argue in this presentation that similarity is one important representation of the complex system of behaviors between members of the system. For example, family systems theory postulates that individuals within the same family system may share a common perception of the world, impacting interactions within family relationships (Reiss, 1981, as cited in Deal, Wampler, & Halverson, 1992). Assessments of similarity allow us to test this theoretical assumption.

3. Theory cont. Further, competing hypotheses about the role and nature of similarity between partners exist. Some suggest that initial similarity between partners is a key feature of how couples maintain a relationship over time. Others suggest that initial similarity between partners is less important than the process of each individual growing more similar over time—similarity may be a by-product of staying in a close relationship. Finally, aside from the association between similarity and the longevity of relationships, similarity may also be an important correlate of relationship quality. Based on family systems theory, Minuchin (1985) suggested that similarity between multiple individuals within the same family system may: foster closeness promote positive interactions between members of the system With questions about the nature of shared processes within families, the longevity of relationships, and the quality of such relationships, exploring approaches to measure similarity will help scholars better understand relationships and family systems processes.

4. Using Similarity in Research Understand how similar, or dissimilar, partners are to one another Use similarity scores (e.g., the ICC) to predict other variables, or use other variables to predict levels of similarity Can test hypotheses about constructs such as complementarity, assortative mating, or the influence of shared environment on couple similarity

5. Using Similarity Scores Can answer questions about the role of similarity in romantic relationships: Are partners similar on certain individual characteristics more likely to marry or stay married over time? Are spouses with more similar beliefs about their relationship more satisfied than those with divergent beliefs? Do spouses with more similar interests experience greater cohesion than those with more divergent interests?

6. Using Similarity Scores Determining the role of similarity in family relationships Some evidence to suggest we partner with people who are similar to us (Epstein & Guttman, 1984; Kalmijn, 1998) But in established relationships, what is the role of similarity? For example, although cohesive experiences and shared views may benefit relationships ( Luo & Klohnen, 2005 ), are we moving away from valuing that in favor of individuality in contemporary relationships?

7. Dyadic Indexes Kenny, Kashy, and Cook (2006) note several common dyadic indexes: Discrepancy: ∑ absolute difference / number of measures d 2 : ∑ squared difference between measures Distance: Square root of d 2 Correlation and Covariance of measures Intraclass correlation Stereotype-adjusted Intraclass correlation

8. Correlation ≠ Similarity Deal, Wampler, and Halverson (1992) note that using a simple correlation between members of a dyad does not measure similarity in individual scores In reality, a simple correlation measures how members of the dyad increases and decreases on a given measure This says nothing about the actual level of their responses As Deal et al note, on a given measure a wife could have scores of 1 and 2 and a husband could have scores of 4 and 5, yet their responses could be perfectly correlated Thus, the correlation—or shared variance—does not tell the researcher anything about how similar dyad members’ scores are

9. Conceptual Representation of Similarity Mother Father Mother Father Shared Mother Father Shared No Similarity Slight SimilarityHigh Similarity ICC = -1.0 ICC =.30 ICC =.75

10. Muddying the Picture: Stereotype Accuracy Members of a dyad are often similar to one another because they share views held by most people in the group or population Two randomly selected individuals might have a degree of similarity in their responses For example, most people would respond in a fairly similar fashion to questions about their acceptance of domestic violence In dyadic- and family-research, we are usually curious if members of a dyad are more (or less) similar than would be expected by chance Do spouses from a given dyad report more (or less) similarity than a randomly selected man and woman?

11. Mother Father Shared Fair Similarity All members of a group/population may have a fair amount of similarity After we remove the general similarity, how is having greater (or less) similarity than the group related to other variables? Muddying the Picture: Stereotype Accuracy

12. The Intraclass Correlation Coefficient (ICC) Kenny, Kashy, and Cook (2006) argue that the Intraclass correlation coefficient (ICC)is an effective measure of similarity This takes into account the similarity between two individuals in the value of their scores, rather than the correlation between scores. The ICC is a traditionally a measure of inter-rater reliability (Hallgren, 2012) The ICC can also be adjusted for stereotype accuracy

13. Computing the ICCs in SPSS Data needs to be in TALL rather than WIDE format. The following syntax will restructure the data in SPSS: VARSTOCASES /MAKE Dad_CP FROM HCP_AGR HCP_CLOS HCP_CON_r HCP_SUP HCP_UND_r HCP_END HCP_LBR /MAKE Mom_CP FROM WCP_AGR WCP_CLOS WCP_CON_r WCP_SUP WCP_UND_r WCP_END WCP_LBR /INDEX=item(7) /KEEP=ID /NULL=KEEP.

14. Data Restructuring The data restructuring obviously deletes other variables from the data set, so you should save a separate copy of the data set before computing the ICCs.

15. Computing the ICCs The following SPSS syntax computes the ICCs and creates a separate matrix file: sort cases by id. split file by id. RELIABILITY /VARIABLES=Dad_CP Mom_CP /SCALE('ALL VARIABLES') ALL /MODEL=PARALLEL /ICC=MODEL(MIXED) TYPE(CONSISTENCY) CIN=95 TESTVAL=0. split file off.

16. SPSS Warning As the ICC is traditionally a reliability measure, values are assumed to be from 0 to 1 Dyadic analysis allows for values from -1 to 1 Thus, SPSS often gives the following warning, which can be ignored: "The average covariance among items in this scale is negative. This violates reliability model assumptions. Statistics which are functions of this value may have estimates outside theoretically possible ranges."

17. Output

18. Import the “Single Measures” ICC Using the Pivot Trays in SPSS, reformat the Table for output Delete the footnote from the ICC data value Export your table to SPSS I usually copy and paste it into Microsoft Excel Format it to just have the ID variable and ICC value Open the Excel File in SPSS

19. Adjustment for Stereotype Accuracy Kenny and Acitelli (1994) outline a complex regression procedure for obtaining the stereotype-adjusted ICCs This considers both self and partner effects of stereotype accuracy by saving and correlating the residuals More recently, Kenny, Kashy, and Cook (2006) note that, in practice, the partner effects are substantively inconsequential They advocate for a much simpler approach

20. Adjustment for Stereotype Accuracy To obtain the Stereotype-adjusted ICCs, the mean for each item should be subtracted before computing the ICCs In effect, this makes the ICC a measure of how similar the dyad members’ deviation scores are Kenny et al suggest that in analyses with distinguishable dyads the group mean should be used For example, the mean for wives’ scores should be used separately from the husbands’ mean scores Analyses with indistinguishable dyads (e.g., siblings) should use the overall mean

21. Coparenting ICCs (r =.808) Non-adjusted ICC Mean =.03; SD =.38 Stereotype-adjusted ICC Mean =.02; SD =.40

22. Conflict ICCs (r =.885) Non-adjusted ICC Mean =.03; SD =.45 Stereotype-adjusted ICC Mean =.00; SD =.45

23. Marital Adjustment ICCs (r =.511) Non-adjusted ICC Mean =.94; SD =.06 Stereotype-adjusted ICC Mean = -.01; SD =.57

24. Using the ICCs in Research The ICC similarity index can be used as Independent variables How does similarity relate to other constructs? Dependent variables What is predictive of more similarity in perceptions? Moderator variables Do variables have a stronger or weaker association based on the level of similarity in the dyad? Similar to analysis with interaction terms, each partner’s report should also be included in analyses (Kenny, Kashy, & Cook, 2006)

25. Example with the Generated Data Mother ConflictFather Conflict Mother Mar AdjFather Mar Adj Couple Conflict Couple Marital Adjustment.54.52 –.16.55.57 Mother Coparenting Father Coparenting Coparenting.75.67.06.03 –.07 –.54.59 –.04 –.05.05 Coparenting ICC by Conflict Int. Coparenting SA ICC

26. SUBSTANTIVE EXAMPLE

27. S UBSTANTIVE E XAMPLE : SAMPLE 568 couples and a target child All participants in the Study of Early Child Care and Youth Development (SECCYD) 88% White, non-Hispanic 5% Hispanic 4% African American 3 % “Other” Ethnicity 290 male children, 278female children Data here represent 2 periods in time: 54 months and kindergarten

28. Hypotheses Mother-father similarity in sensitivity, couple emotional intimacy, and parent-child affective mutuality will be positively related to children’s school readiness Mother-father similarity in parental sensitivity will moderate the association between parent-child affective mutuality and children’s school readiness Mother-father similarity in sensitivity will moderate the association between couple emotional intimacy and children’s school readiness

29. M EASURES Similarity in Parent-Child sensitivity Well-trained observers rated parents’ supportive presence, respect for autonomy, and hostility (reversed) using 7-point rating scales with responses ranging from (1) very low to (7) very high (Egeland & Hiester, 1993) Stereotype-adjusted ICC was computed for sensitivity Parent-Child Affective Mutuality Mutuality of emotion between children and their parents was assessed during two 15-20 minute video recorded, structured interaction tasks when children were 54 months old Coders assessed the availability and mutuality of emotion between the child and parent, including: the child’s ability to inhibit inappropriate behavior and affect the reciprocal nature of warmth in the dyad emotional intimacy in the dyad the child’s sense that the parent has his/her own best interests in mind features of parent-child communication (both verbal and non-verbal)

30. M EASURES Couple Emotional Intimacy Personal Assessment of Intimacy in Relationships (PAIR) (Schaefer & Olson, 1981) 6 items assess emotional intimacy in areas such as support, sharing, and loneliness Higher scores represent more emotional intimacy School Readiness Kindergarten teachers assessed school readiness by completing the Social Skills Rating System (SSRS)–Teacher Form (Gresham & Elliott, 1990). 39 items (social skills and academic competence) Higher scores represent better social skills and academic competence

31. Results Child Gender (1=Female) Similarity in Parental Sensitivity Parental Education Parent-Child Affective Mutuality Couple Emotional Intimacy School Readiness Social Skills Academic Competence.22 + * ns.25 ns*.22.66.81 –.16.38.16.27.15 R 2 =.21 R 2 =.22 R 2 =.07 N=568. + p<.10. Bias-corrected p-value for all other path coefficients is p <.05. The asterisks (*) indicate a significant moderation effect in follow-up analyses. Model fit statistics: χ 2 (170)=227.840, p <.01; TLI=.979; CFI=.983; RMSEA=.024.

32. D ISCUSSION OF S UBSTANTIVE E XAMPLE Mothers and fathers who are similar in their interaction styles with children and who demonstrate emotional closeness with each other foster emotional environments that promote child well-being (Cummings & Davies, 1995, 2010). The effect of affective mutuality on school readiness appeared to be stronger when parents were similar in their demonstration of sensitive behavior. We found that greater similarity in parenting sensitivity allowed for a stronger influence of the couple relationship on the child’s school readiness. Thus, when the relationships most proximal to the child (both parent-child relationships) are consistent, the couple relationship may have a stronger influence on children’s development of social skills and academic competence.

33. Methodological Limitations The computation of the ICCs assumes that the factor structure of the measure is similar for both members of the dyad If dyad members interpret the items differently, this may affect their similarity score The ICCs cannot be computed when there is missing data Item-level data would need to be imputed, or missing ICC values would need to be addressed with multiple imputation or FIML estimation

34. Possible Extensions For interpretability, it may be helpful to rescale the ICC Adding 1 and dividing by 2 will give a range of 0 to 1 For example: A value of.50 would indicate 50% (or chance) similarity A value of.75 would indicate 75% similarity Stereotype Adj ICC for CP:.015 .508 Unadjusted Marital Adj ICC:.968 .984 Subtracting the Stereotype-adjusted ICC score from the unadjusted score may allow researchers to examine the effect of group/population stereotypes

35. Possible Extensions (con’t) The ICC can be computed with more than two reporters. Similarity at the family-level (rather than the couple-level) could be computed using the “Single Measures” ICC Researchers could evaluate predictors of this similarity, outcomes of family-level similarity, and how family-level similarity may moderate associations between other variables

36. Questions?

37. Acknowledgements We are grateful to the Eunice Kennedy Shriver National Institute of Child Health and Human Development Early Child Care Research Network for designing and carrying out the data collection for the example in this project. The NICHD Study of Early Child Care is a study directed by a Steering Committee and supported by NICHD through a cooperative agreement that calls for scientific collaboration between the grantees and the NICHD staff. The content of this project is solely the responsibility of the named authors and does not represent the official views of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Health, or individual members of the Network.