Abstract

1. Abstract The goal of this study was to identify patterns of development of romantic relationships among females in high school, and to determine if involvement with best female friendships was influenced by patterns of involvement with romantic partners. In this study, retrospective data were gathered from 102 female adolescents (M age = 18.2). Estimates of the amount of leisure time spent with best female friends and romantic partners were gathered after focusing participants on eight time periods during high school. Cluster analysis was used to classify participants based upon repeated measures of the amount of leisure time spent with romantic partners during high school. Five clusters of females with very different patterns of involvement with partners were identified. These groups of female adolescents also exhibited differing patterns of involvement with their best female friends during high school.

2. Introduction To understand social lives we need to study associations between levels of relationships (Hinde, 1992). As Bronfenbrenner (1988) proposed, the first task of ‘an ecology of human behavior’ is to understand linkages between levels of environmental influence. The research of Zani and colleagues (1993) and Hendry and colleagues (1993) has suggested that the transition to dating and steady romantic relationships does not uniformly change existing involvement with social networks. Despite this information, as well as studies of the average age of initiation and predictors of dating, very little information exists to describe the normative timing of the transition or individual variation in the transition to involvement in romantic relationships. In response, the goal of this study was to identify patterns of involvement in romantic relationships among females in high school, and to determine if patterns of time spent with best female friends was influenced by patterns of involvement with romantic partners.

3. Participants and Data Collection Procedures Participants were 102 white females who had recently graduated from high school. Seniors were recruited the last month of school at a large urban high school. The school population was socioeconomically and racially diverse. Because of evidence that individuals of different racial/ethnic backgrounds may have systematically different experiences of the transition to romantic relationships (for example, see Phinney, et al., 1990) this study was limited to white females. Overall, 37 females from this urban high school were interviewed in the summer after their senior year. The remaining 55 participants were recruited at a large urban university. Recruitment involved advertising on campus for females (19 years of age or less) that were interested in participating in a study of high school relationships. These interviews were completed at the university or at the interviewees' homes. The portion of the interview and survey described here took between 20 and 40 minutes to complete.

4. Measurement Retrospective data of level involvement with best female friends and romantic partners during high school were gathered by asking participants a series of questions. After focusing participants on eight periods during high school (in the fall and spring of grade 9, 10, 11, and 12), they were asked to estimate the amount of time that was spent outside of work and school (leisure time or “free-time”) with their best female friends and with their romantic partners. Participants were given a visual-analog scale ranging from 0 to 100 and asked to write numbers to indicate the time they selected. Normative and significant events (such as the start of each new school year, sports of the season, prom, activities, etc.) and general conversation about each time during high school were used to help focus participants prior to making estimates.

5. ResultsCluster Analysis: Patterns of Involvement with Romantic Partners A k-means clustering algorithm (Hartigan, 1975) was used to classify participants based upon repeated measures of the amount of leisure time spent with romantic partners during high school. K-means clustering was used to minimize within cluster variance on criterion variables and between cluster differences, while allowing all profiles to be considered. This algorithm makes no attempt to obtain groups of similar size. The criterion variables used to identify clusters were average time spent with romantic partners in 9th, 10th, 11th, and 12th grades. The mean square ratio statistic (R; Hartigan, 1975) was used to guide the selection of the appropriate number of clusters. To justify selecting a larger number of clusters, R was required to be at least 10. A 5-cluster solution was chosen over a 2-, 3-, and 4-cluster solution (R = 58.3, R = 23.7, and R = 26.6, respectively). A 6-cluster solution was also considered, and, although the value of R justified moving to 6 clusters (R = 12.0), this solution was not chosen due to small cluster sizes (3 clusters had fewer than 10 members) and no real gain in conceptual clarity.

6. THREE clusters had varying levels of somewhat stable involvement with romantic partners during high school (Figure 1). CLUSTER 1, LOW INVOLVEMENT: Contained 59 participants who, on average, reported spending a very little time with partners throughout high school. CLUSTER 2, MODERATE INVOLVEMENT: Contained 12 participants who spent a moderate amount of time with partners in grade 9 that remained steady throughout most of high school . CLUSTER 3, HIGH INVOLVEMENT: Contained 10 females who spent a high proportion of their leisure time with partners throughout high school. The other TWO clusters increased involvement with romantic partners between grade 9 and grade 12 (Figure 2). CLUSTER 4, EARLY INCREASING: Contained 10 participants who exhibited a sharp increase in time with romantic partners between grade 9 and grade 10 with only a slight decline thereafter. CLUSTER 5, LATE INCREASING: Contained 11 females who reported a sharp increase in time spent with romantic partners later in high school (between grade 10 and 12).

7. ResultsInvolvement with Best Female Friends The next step was to determine whether these clusters also exhibited different patterns of involvement with best female friends during high school. A random effects mixed model (Bryk & Raudenbush, 1992; Littell, Milliken, Stroup, & Wolfinger, 1996; Singer, 1997) was used to examine between-cluster differences in trajectories of time spent with female friends during high school. In this model, time spent with best female friends was the criterion variable. Predictor variables included the time of measurement, a variable indicating cluster membership, and the interaction between time of measurement and cluster. Clusters were coded from one to five and time of measurement was specified as repeated within subjects. Coding guaranteed that all clusters would be contrasted with the largest cluster (low involvement). In this model, the main effect of cluster tested the difference in the average amount of time spent with best female friends in fall of grade 9 between each cluster and the low involvement cluster. The interaction effect tested the difference in the average linear rate of change in time spent with friends between each cluster and the low involvement cluster.

8. Females in all but one cluster spent different amounts of their leisure time with best female friends early in high school when compared to the low involvement group(Figure 3 and Figure 4). Involvement with Best Females Friends in Grade 9 In grade 9, the moderate involvement cluster spent about the same amount of time with best female friends as the low involvement cluster. In grade 9, females in the high involvement, early increasing, and late increasing clusters spent significantly more time with friends than females in the low involvement cluster. Change in Involvement with Best Female Friends from Grade 9 to Grade 12 In the low involvement cluster, time with friends slightly increased from fall of grade 9 to spring of grade 12. In contrast, the late increasing cluster greatly reduced their time with best female friends during the same period of time. Time spent with female friends within the other three clusters (moderate involvement, high involvement, and early increasing) also declined during high school, but the linear change in involvement with best female friends of these three groups did not significantly differ from the low involvement cluster.

9. Conclusions Among white females, five patterns of development of romantic relationships during high school exist. These include three clusters that spend almost unvarying amounts of time with romantic partners during high school (low, moderate, or high) and two clusters that increase the amount of time they spend with romantic partners during high school (increasing sharply between grade 9 and 10 or between grade 10 and 12). Females with these different patterns report that their time spent with best female friends also varied. All clusters, except the moderate involvement cluster, spend significantly more time with best female friends early in high school when compared to the low involvement cluster (about 60% to 65% compared to 48%). Excepting the low involvement cluster, all clusters reduce time with friends while increasing time with partners. Time spent with friends is reduced between 2% and 8% per year depending on the cluster. The late increasing cluster seems to trade the most time with friends for time with partners.

10. References Bronfenbrenner, U. (1988). Interacting systems in human development. Research paradigms: Present and future. In N. Bolger, A. Caspi, G. Downey, & M. Moorehouse (Eds.), Persons in context: Developmental processes. New York: Cambridge University Press. Bryk, A.S., & Raudenbush, S.W. (1992). Hierarchical linear modeling. Newbury Park. CA: Sage. Hartigan, J. (1975). Clustering algorithms. Wiley: New York. Hendry, L.B., Shucksmith, J., Love, J.G., & Glendinning, A. (1993). Young people's leisure and lifestyles. New York: Routledge. Hinde, R.A. (1992). Developmental psychology in the context of other behavioral sciences. Developmental Psychology, 28,. 1018-1029. Littell, R.C., Milliken, G.A., Stroup, W.W., & Wolfinger, R.D. (1996). SAS system for mixed models. Cary, NC: SAS Institute, Inc. Phinney, V.G., Jensen, L.C., Olsen, J.A., & Cundick, B. (1990). The relationship between early development and psychosexual behaviors in adolescent females. Adolescence, 25, 321-332. Singer, J.D. Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Manuscript under review by The Journal of Education and Behavioral Statistics. Zani, B. (1993). Dating and interpersonal relationships in adolescence. In S. Jackson & H. Rodriguez‑Tomé (Eds.), Adolescence and its social worlds. Hillsdale, NJ: Erlbaum.

11. Figure 1. Trajectories of Time Spent with Romantic Partners during High School among Clusters 1, 2, and 3 100 80 Clusters 1, 2, and 3 High Involvement (n=10) 60 Spent with Romantic Partners Mean Amount of Leisure Time Moderate Involvement (n=12) 40 20 Low Involvement (n=59) 0 9th Grade 10th Grade 11th Grade 12th Grade

12. Figure 2. Trajectories of Time Spent with Romantic Partners during High School among Clusters 4 and 5 Clusters 4 and 5 100 Late increasing (n=11) 80 60 with Romantic Partners Mean Amount of Leisure Time Spent Early increasing (n=10) 40 20 0 9th Grade 10th Grade 11th Grade 12th Grade

13. 100 80 60 40 20 0 9th 9th 10th 10th 11th 11th 12th 12th Fall Spring Fall Spring Fall Spring Fall Spring Figure 3. Trajectories of Time Spent with Best Female Friends during High School among Clusters 1, 2, and 3 Clusters 1, 2, and 3 High Involvement (n=10); slope = -0.79 Low Involvement (n=59); slope = 0.36 Mean Amount of Leisure Time Spent with Best Female Friends Moderate Involvement (n=12); slope = -1.49

14. Figure 4. Trajectories of Time Spent with Best Female Friends during High School among Clusters 4 and 5 100 80 Clusters 4 and 5 Early increasing (n=10); slope = -2.16 60 Mean Amount of Leisure Time Spent with Best Female Friends Late increasing (n=11); slope = -3.97 40 20 0 9th Fall 9th 10th 10th 11th 11th 12th 12th Spring Fall Spring Fall Spring Fall Spring