Assessment of Grade-Level Differences in Coping Behavior Among Adolescents Using Multidimensional Scaling Single-Ideal-Point Model

Abstract

The purpose of the study was to examine grade-level differences in coping behaviors among adolescents using a probabilistic multidimensional scaling (MDS) single-ideal-point model. Using data from students in middle school and at college, this article illustrated the MDS single-ideal-point model as an alternative to examine students’ typical coping behaviors.

Keywords

coping typical behaviors developmental differences MDS single-ideal-point analysis

Coping behavior is an important concept in human development. Ways of coping have been identified to be related with the psychosocial adjustment among adolescents and young adults, including gender role (Eaton & Bradley, 2008; Renk & Creasey, 2003), psychological functioning (Greenglass & Fiksenbaum, 2009), substance abuse (Hussong & Chassin, 2004; O’Connell, Hosein, Schwartz, & Leibowitz, 2007), or health issues (Hermann, Hohmeister, Zohsel, Ebinger, & Flor, 2007). Adequate coping has also been suggested to decrease levels of depression or anxiety (Ben-Zur, 2009; Galaif, Sussman, Chih-Ping, & Wills, 2003). Given the important role of coping responses, a great deal of effort has focused on the development of coping processes and the determinants of selecting different coping behaviors (e.g., Eisenberg, Fabes, & Guthrie, 1997). According to the model of coping by Lazarus and Folkman (1984), coping may take different forms at different ages and individuals may make conscious decisions on how best to cope. A coping response that is effective for older individuals may not be viable for younger ones, and vice versa. Researchers suggest that there are large developmental differences in the coping strategies used across age ranges (Amirkhan & Auyeung, 2007; Boekaerts, 1996; Compas, Conner-Smith, Saltzman, Thomsen, & Wadsworth, 2001; Donaldson, Prinstein, Danovsky, & Spirito, 2000; Hampel & Petermann, 2005).

From developmental theory and the developmental perspective on coping strategies (e.g., Compas, Malcarne, & Fondacaro, 1988; Frydenberg & Lewis, 1993), Compas, Melcarne, and Banez (1992) suggest that developmental differences in coping may be due to differences in biological, cognitive, and emotional maturity between the young and old. As a result, coping style may be more rigid in younger people and include more emotional- or withdrawal-focused coping styles, whereas older people may develop certain specific coping strategies such as active-focused coping because of their more mature cognitive skills. However, the findings from empirical studies regarding the developmental differences do not provide a clear picture. For example, Donaldson et al. (2000) found that elementary school children used more avoidance/withdrawal coping responses than did junior high school students, but others did not find a change in the use of avoidant coping responses between Grades 4 and 6 or from Grade 6 to college (Ayers, Sandler, West, & Roosa, 1996; Hampel & Petermann, 2005; Williams & McGillicuddy-De Lisi, 2000). In their exhaustive review of 24 factor analytic investigations of adult coping and 10 factor analytic studies of child–adolescent coping, Skinner, Edge, Altman, and Sherwood (2003) found that these studies did not converge on a single set of coping categories, and they suggested that the lack of convergence was due to methodological problems rather than true differences in coping between age groups. Similar findings were also reported by Amirkhan and Auyeung (2007), McCrae (1989), and Felton and Revenson (1987), indicating that age had a limited effect on coping behaviors.

The factor analytical approach is the most widely used multivariate analysis to test developmental differences in coping. Ways in which the individual chooses certain coping behaviors are examined via the variable-oriented approach rather than the person-oriented approach (Bergman & Magnusson, 1987). Although a variable-oriented approach can address important theoretical and practical questions, ambiguities in these findings may be due to this sine qua non tool for the study of complex behavioral phenomena. In spite of progress made in conceptualization, measurement, and predictability of coping responses in adolescents and young adults, there is a lack of models that are sensitive to development of cognitive processes (Compas et al., 2001). The implication of this difference in analytical approach is that in studying individuals’ behaviors using a factor model, we usually make use of an aggregated score (e.g., a mean score) of a set of items that assess a particular construct, which leads to assumptions about an individual’s behavior via summation of items on a scale. However, individual items on a measure may be more or less able to detect one’s typical behavior regarding how one usually behaves (e.g., Chernyshenko, Stark, Drasgow, & Roberts, 2007; Fava, Ruini, & Rafanelli, 2004; Meier, 1997, 2004; Mumma, 2004; Roberts, Donoghue, & Laughlin, 2000; Stark, Chernyshenko, Drasgow, & Williams, 2006; Vermeersch, Lambert, & Burlingame, 2000). For example, if some items that are intended to assess emotion-focused coping are not sensitive to an individual’s typical coping behaviors, then the ability of an aggregated score based on these items in detecting individual differences may decrease. In addition, statistical models that use the aggregated score as data input may be negatively affected since these models are designed to work best with the aggregated score rather than individual items; items that are not sensitive in assessing individual’s typical behaviors may contribute to measurement error, thus decreasing the utility of statistical models in studying one’s typical behaviors. Accordingly, we need to employ alternative statistical models that can take advantage of each item in the instrument that may better assess individuals’ typical behaviors (Fava et al., 2004; Meier, 2004; Mumma, 2004; Vermeersch et al., 2000; Weinstock & Meier, 2003).

Multidimensional scaling (MDS) ideal-point model is one alternative method of modeling behavior differences without using aggregated scores. This person-oriented modeling method is an internal unfolding model that can address questions such as whether individuals have preferred (i.e., typical) behaviors as indicated by specific items that assess particular aspects of a behavior. From the theoretical framework of social development theory (Vygotsky, 1978), we can study individuals’ cognitive development based on the zone of proximal development, which is a developmental level reached when he or she engages in social activities. From this perspective, we can consider individuals’ typical or preferred choice of certain coping behaviors as corresponding to their cognitive developmental level (Boekaerts, 1996). For instance, younger people may not attain fully mature levels of processing and metacognitive abilities, and thus their coping strategies may be less selective, whereas older people, with their increased cognitive skills, may be more selective in coping responses by evaluating their effectiveness (Compas et al., 1992; Ryan-Wenger, 1992).

The purpose of the current article is to study grade-level differences in typical coping behaviors of Chinese students in Grade 7 and at college using a metric probabilistic MDS single-ideal-point model.

The MDS single-ideal-point model is an explicitly cognitive approach in that the various endorsements that an individual makes of a coping behavior can be explained as a systemic function of an internal representation the individual has of that behavior. Operationally, such internal representations can be revealed by modeling the distance between the point of single items that represent the actual behaviors and the ideal point that represents the individuals. Endorsement of an item will be made when the item is close to the reflection of his/her behavior in the perceptual space, and nonendorsement will occur when the item does not invoke any perceptual similarity to the ideal behaviors. In the MDS single-ideal-point model, this discrepancy occurs when the individuals’ ideal point is located too far away from the particular coping items. Previous studies on coping behaviors among Chinese students in middle school indicated that these students were likely to use all coping behaviors (i.e., problem solving, help seeking, withdrawal, ventilation, fantasy, and tolerance), suggesting a less differentiated approach (e.g., Huang, Yu, Zheng, Yang, & Wang, 2000). Thus, we hypothesized that Chinese college students would exhibit a more focused coping approach in comparison with Chinese middle school students. Operationally, this expectation suggested that the ideal point of these Chinese middle school students was not close to the point of any particular coping items. In addition, we expected that the variance associated with coping items would be larger for the middle school students than that for the college students, reflecting more uncertainty in typical coping behaviors for younger individuals.

Method

Participants

The participants of the current study were 258 Chinese students in Grade 7 and 271 in university. These Chinese students were drawn from two large urban and surrounding areas in Northeast China. These students shared similar experiences of recent economic growth in China, and their socialization processes were shaped by similar economic and cultural conditions. The gender ratio across the two groups was fairly even (average of 47% male and average of 53% female). The average age of the participants in Grade 7 and in university was 13.6 (SD = 1.27) and 22.3 (SD = 1.08) years, respectively. The average education level of the parents among these participants was high school.

Measure

The instrument used was an 18-item coping behavior survey adapted from a questionnaire developed by Seiffge-Krenke and Shulman (1990). Students were asked how likely they were to use each of the 18 coping behaviors when they had a problem, for example, “I discuss the problem with my parents/other adults” or “I behave as if everything is alright.” Each item was rated on a 5-point Likert-type scale from 1 (most likely to use) to 5 (least likely to use). Cronbach’s α was .79 and .69 for middle school students and college students, respectively. Note that the 18-item coping survey assessed the global tendency of coping behaviors rather than situation-specific coping behaviors. We realized that individuals cope differently in different situations, and it was also possible that the degree of covariance between perceived specific responses was at least somewhat conditional on the coping context. However, given that there could be an infinite number of specific situations in which individuals use different coping techniques, it could also be argued that general tendency of coping behaviors was underlying those coping behaviors in specific situations. This global preference in a general situation could be susceptible to developmental change. This argument is supported by the findings that global coping showed significant age and gender effects but situation-specific coping strategies did not show consistent effects (Hampel & Petermann, 2005).

Since the 18 coping items were originally in English, they were translated into Chinese and were validated through a double-translation process according to the guidelines provided by Brislin (1986). After the translation–back-translation procedure, two bilingual translators compared the results item by item to assess the equivalence of the Chinese translation and to ensure that each of the items was clearly phrased. A panel of four developmental psychologists discussed the appropriateness of each of the translated items and approved the translated version of the final survey.

Procedures

Participants were asked by teachers to complete a battery of questionnaires that assessed adolescents’ psychosocial adjustments, including their coping behaviors in the regular classroom setting. Participants were advised that the questionnaire was related to their daily behaviors and feelings. The study was approved by the research ethics committee of the university, and informed consent was obtained from all students before assessments were carried out.

Analysis

The current study employed a metric probabilistic MDS single-ideal-point model in examining typical coping behaviors among middle school students and college students from a developmental perspective. Specifically, the probabilistic MDS single-ideal-point model requires a single-ideal solution to be estimated. A single-ideal solution represents both individuals and behaviors as a point in a Euclidean space. The distance relationship between individuals and behaviors as indicated by the items provides information about the preference structure of the individuals in such a way that individuals are closer to the behaviors they prefer. The basic idea can be traced to Thurstone (1928) and Coombs (1950). The model is initially used to represent a rectangular matrix of preferences by i individuals for v variables as distances between i ideal points and v actual objects or variables by estimating the coordinates of individuals and the objects in the same latent space. Thus, MDS single-ideal-point model is a spatial model in which i individuals and v items are represented as points in multidimensional space. The coordinates x_i of an individual or a group of individuals is generally referred to as his or her (or that group) ideal point and, hence, called the ideal-point model.

The preference of an individual or a group of individuals for an item or object is an inverse function of the distance between the point that represents the actual objects and the ideal point that represents the individuals. A large distance between an object and an ideal point indicates that the object has high disutility (i.e., less liked or preferred). In other words, an individual responds negatively to an actual object (a variable or item) when the attitude or behavior represented by the object or item does not closely reflect the attitude or behavior of the individual. In the MDS single-ideal-point model, such disagreement occurs when the individual is located too far away from the object. On the other hand, individuals respond positively to actual items or objects that have locations similar to their own.

In a probabilistic MDS single-ideal-point model, the ideal points and actual items are represented not by points but by distributions. In this article, we mainly focus on the probabilistic MDS single-ideal-point model of Hefner (1958) as proposed by Mackay and his associates (MacKay, 2007; MacKay & Zinnes, 1986; Zinnes & MacKay, 1983). The Hefner model assumes that the coordinates x_ik of an item or object have the Euclidean properties:

d_{i j} = \sqrt{\sum_{k = 1}^{r} {(x_{i k} - x_{j k})}^{2},}

where d_ij is the distance random variable between item i and j, and x_ik or x_jk are coordinates that are assumed to be normally and independently distributed with mean µ_ik and variance $σ_{i k}^{2}$ . The variance $σ_{i k}^{2}$ can be assumed equal (i.e., isotropic) or unequal (i.e., anisotropic) for each item i on each dimension k in a Euclidean space. The goal of the analysis is to estimate the mean location µ_ik of coordinates x_ik or x_jk and variance $σ_{i k}^{2}$ , including location and variance of the ideal point. To obtain the parameter estimates µ_ik and $σ_{i k}^{2}$ , one needs to specify the probability function of the distance random variable d_ij, which depends on the variance structure and sampling properties. The detailed discussion of how the probability MDS single-ideal-point model is derived may be found in MacKay and his associates (MacKay, 1989, 2007; MacKay & Zinnes, 1986; Zinnes & MacKay, 1983, 1992).

MacKay, Easley, and Zinnes (1995) indicated one primary reason why the probabilistic MDS models are of particular interest in modeling preferences characterized by a single-ideal point. Probabilistic MDS is able to estimate mean (i.e., centroid) location and variance of preferred behaviors. When variability in preferred behavior exists or when there are measurement errors inherent in single items of an instrument, it is desirable to take into consideration such variability or measurement errors. Technically, for each ideal point or actual item, i, there is a corresponding k-dimensional random vector X_j that has an x variate normal distribution with mean vector u_j and covariance matrix Σ_j. Individuals’ choices are assumed to be based on values sampled from the X_j distributions. If an individual has a consistently preferred behavior, be it actual or ideal, then we expect the diagonal elements of the covariance matrix Σ_j to be small. However, if the individual does not have a consistently preferred behavior or there are additional measurement errors, the diagonal elements of the Σ_j are expected to be large.

The model fit can be tested using information criterion statistics, such as Akaike’s information criterion (AIC; Bozdogan, 1987), Bayesian information criterion (Schwarz, 1978), or log-likelihood ratio test. Thus, we can test various models with respect to kinds of variances assumed, latent groups in the data, or the number of ideal points that may need to reflect individuals’ typical behavior. The ability to test hypotheses about the structure of the variance can also be just as interesting to researchers as the ability to test hypotheses about the location of actual objects and ideal points. For example, a psychologist might have an interest in knowing if the variability in the clients’ anxiety behaviors about a positive event and a negative event were the same as a result of interventions or a part of developmental processes.

A simulated example was presented to illustrate the MDS single-ideal-point model. Five hypothetical behaviors (singing, drinking alcohol, listening to music, watching sports, and reading by adults) were examined in two dimensions with mean locations (i.e., centroids) randomly sampled in the range (−0.6, 0.6) and a single-ideal point with centroid (0, 0) selected. The variances of all behaviors on all dimensions were assumed to be equal (i.e., isotropic) and arbitrarily set at 0.11, and the variance of the ideal point was arbitrarily set at 0.15. Figure 1 shows the parameter space. It can be seen that Behavior 1 (singing) and Behavior 5 (reading) were less preferred (i.e., high disutility, with a larger distance between actual items and the ideal point) than the other three behaviors, indicating a less typical behaviors engaged by adults. Another application of this analysis would be if a psychologist wanted to know if a treatment resulted in a particular behavior being more or less preferred over another behavior. Moreover, the results in Figure 1 can be further verified by the perceptual share analysis, in which the percentage or probability of a behavior as first choice by individuals is calculated. This analysis allowed us to make predicative statements about the probability of a particular behavior being the individual’s typical one or the probability of one behavior chosen over others. For our hypothetical example, the estimated first-choice probabilities are .16 for singing and .17 for reading, which are lower than that for drinking alcohol (.21), listen to music (.24), and watching sports (.21); we can predict that for adults, in general, their typical behaviors would be less likely singing and reading among this set of behaviors.

Figure 1.

Parameter space for five hypothetical behaviors and one ideal point.

For the current analysis, we began with the probabilistic MDS single-ideal-point analyses by comparing a simpler model and a more complex model in terms of goodness of fit using consistent Akaike’s information criterion (CAIC). Specifically, three models were compared: one-, two-, and three-dimensional solutions with equal (isotropic) and unequal (anisotropic) variance structure across 18 coping items. The analyses were performed separately for students in Grade 7 and students in college. Although all the participants took the same set of coping items, each age group was expected to have its own unique preference pattern. One analysis with both groups together may mask such differences in preference structure.

To compare the preference structure across the two age groups, however, we compared the solutions of two groups by testing the hypothesis of no difference in preferred or typical coping behaviors. This testing was done by using the solution from Grade 7 as the initial solution in the analysis of college data, and the correlation between the two solutions was examined. The analyses were performed using software PROSCAL (MacKay & Zinnes, 2005). PROSCAL provided maximum likelihood estimates of scale values and associated variances. It also plotted the centroids and standard deviations of the maximum likelihood configuration in a Euclidean space. Particularly, PROSCAL provided for the testing of hypotheses with respect to the variance structure of the items, locations of the item, and dimensionality of the space. All the tests involved comparing a complex model to a simple model. When a complex model did not fit the data better than a simpler model as assessed by fit indices such as the log likelihood or CAIC, the simpler model was used.

In addition, we also conducted a traditional one-way multivariate analysis of variance to compare whether this analysis could also detect developmental differences in typical coping behaviors based on the aggregated score of three coping constructs (i.e., AC, IC, and WC). In the analysis, three coping factors were used as dependent variables, and grade level (i.e., Grade 7 and college) was used as the independent variable. The analyses were performed using SAS (SAS Institute, 2010).

Results

The CAIC value for each of the three models is shown in Table 1 under the assumption of isotropic and anisotropic variance structure. The results of CAIC value indicated that a two-dimensional solution with anisotropic variance structure was supported for both age groups by the data. In addition, by means of likelihood ratio tests, the estimates obtained from a two-dimensional anisotropic variance model were significantly (p < .001) better than the other models. Thus, we selected the two-dimensional anisotropic variance model as our final solution.

Table 1.

CAIC Model Fit Index of Three Single-Ideal-Point Models.

Model	Age Level	Isotropic Model	Anisotropic Model
One dimensional	Grade 7	2234.8	2146.5
	College	4215.3	4101.5
Two dimensional	Grade 7	1354.8	1286.0
	College	1304.2	1257.3
Three dimensional	Grade 7	3681.8	3610.1
	College	2536.1	2314.7

Note: CAIC = consistent Akaike’s information criterion.

The single-ideal anisotropic two-dimensional solution is shown in Figure 2. It is important to note that, unlike factor analysis, the focus of the MDS preference analysis in this study was not on the interpretation of dimensions. That is, dimensions bear no relation to the configuration and were not interpreted as factors of homogeneous items assessing a construct (e.g., emotional-focused coping). Rather, we needed to identify the position of these items in relation to the ideal point for each grade group. It can be seen that the ideal point (shown as I-7) of Grade 7 students did not seem to be close to any of the coping items (shown as O with a number). This pattern indicated that there may not be particular coping behaviors preferred by Chinese middle school students.

Figure 2.

The configuration of the single-ideal anisotropic two-dimensional solution for students in Grade 7 and college.

On the other hand, the ideal point of college students seemed to be closer to Items 3 (get help from professionals), 6 (let out feelings with loud music), 7 (behave as if everything was alright), 11 (talk with friends or peers), 15 (forget problems using alcohol), and 18 (withdrawal), as can be seen in Figure 2. In addition, these aspects of behaviors were ranked as the top six preferred ones among the 18 coping behaviors (as indicated by the I-scale value) by these college students. The results suggested that these particular aspects of coping might be preferred over other aspects of coping.

To examine whether the configural pattern of coping behaviors for students in Grade 7 differed from that of college students, the configural pattern of coping behaviors for students in Grade 7 was used as the target configuration of coping behaviors for the college students. This target configuration was compared with that estimated from the data of the college students. The correlation coefficient (r) can be used to quantify the strength of configural similarity, with r > .90 being strong similarity. In the current case, the correlation coefficient (r) between configurations of the college students and Grade 7 students was only .45, indicating not a strong similarity of the configurations between the two groups in coping behaviors, as we expected. This finding was further supported by perceptual share analyses, which indicated that the model predicted first-choice probability (p) was quite evenly spread out across all coping items for students in Grade 7 (the range of probability was .04 to .07), while the first-choice probability for college students was higher for Items 3, 6, 7, 11, 15, and 18 (the probability was about .10).

Figure 3 shows the anisotropic variance structure of the coping items by each age group. As expected, for students in Grade 7 the variance for all coping items was large and the variance of the ideal point was small in comparison. In contrast, the variance of typical or preferred coping behaviors was smaller than that of the rest for college students, as shown in Figure 3, indicating that college students were more homogeneous in selecting typical coping behaviors, whereas they were more heterogeneous with respect to untypical coping behaviors.

Figure 3.

Anisotropic variance structure of coping items for each age group.

In comparison to MDS single-ideal-point analysis, we performed one-way multivariate analysis of variance (MANOVA). The correlation among three dependent variables was .24 between AC and IC, .40 between IC and WC, and .32 between AC and WC. The preliminary analysis showed that these three coping factors were normally distributed, as indicated by skew index (1.56) and kurtosis index (−1.05). The results of one-way MANOVA indicated that there was no significant multivariate effects of grade level on three coping factors: active, internal, and withdrawal, Wilks’s Λ(3, 562) = 2.31, p = .52, partial η² = .096. The follow-up discriminant analysis indicated that the linear discriminant function was not statistically significant (p > .05) and that the weights of three coping measures were low, suggesting that the three coping styles did not predict grade level. The difference in means for each of the three coping factors across the two grade levels was less than 0.3 points, as shown in Table 2.

Table 2.

Means and Standard Deviations of Three Coping Factors by Grade Level.

Coping Factor	Grade 7 (n = 258), M (SD)	College (n = 271), M (SD)
Internal	1.30 (0.66)	1.48 (0.54)
Active	2.19 (0.89)	2.23 (0.60)
Withdrawal	1.19 (0.70)	1.21 (0.61)

Discussion

Using the MDS single-ideal-point analysis, typical coping behaviors of Chinese students in middle school and in college were examined. The results revealed developmental differences from less differentiated coping behaviors among younger students to more focused coping behaviors among college students, a finding that was consistent with the developmental theory and the developmental perspective on coping strategies (e.g., Compas et al., 1988; Frydenberg & Lewis, 1993). The results suggested that younger adolescents had not formed a stable coping behavior of their choice. However, for Chinese college students, they tended to get help from professionals, let out feelings with loud music, behave as if everything was alright, talk with friends or peers, forget problems using alcohol, and withdraw. It seemed that these six coping items, which covered different aspects of active, emotional, and withdrawal coping, were more typical to young adults than other items on the instrument.

In contrast, the results of analysis of variance did not suggest such developmental differences between younger and older individuals when we used the aggregated scores (i.e., active, internal, and withdrawal). There may be several possible reasons: (a) items are inadequate, (b) samples are not representative, (c) response model is misspecified, or (d) the theory is wrong. Although all these reasons were probable, based on the current analyses, the results seemed to suggest that the college students may use different aspects of the three coping styles (active, internal, and withdrawal) as a typical way of coping at this time of their development rather than focusing on one particular way of coping such as either active or internal. As a result, it was possible that some of the coping items were not sensitive to developmental differences in coping behaviors so that mixing them together with items that can assess such differences may reduce the scale’s ability to detect nuances in coping response development.

Another interesting finding was that the MDS single-ideal-point model was able to single out the particular aspects of coping behaviors that were more preferred. That is, the analysis was able to address how people actually responded to coping items that closely reflected their own behaviors. For example, in this sample of college students, they sought professional help or talked with friends or peers as an active (or problem-solving) coping response rather than other kinds of active coping such as looking for information in magazine. Moreover, the typical coping behaviors of these college students included mixed coping responses. This finding of mixture in coping behaviors was also consistent with results from previous studies (Galaif et al., 2003; Hampel & Petermann, 2005; Seiffge-Krenke & Shulman, 1990) and may be more realistically reflective of how individuals coped.

The third interesting finding was that the variance structure of coping items differed by age level as well as by preferred coping behaviors. Younger students tended to manifest large variance in their rating of all coping items, whereas college students showed less variance for the preferred coping behaviors. This finding was also consistent with the finding that no particular coping items were preferred by the younger adolescents. This variance structure may also suggest that the college students might develop a more stable coping approach than younger adolescents. Thus, the result may suggest a cognitive difference in developing preference of coping behaviors. That is, the large variance from younger students indicated that they did not have consistently preferred coping behaviors as described by the items, revealing considerable ambiguity concerning the utility of these coping items for younger students. On the other hand, the variance of preferred coping behaviors by college students was smaller than that of the less preferred, indicating again that the college students may develop certain preferred coping behaviors over time.

Methodologically, the single-ideal-point model examines behavioral development from a different perspective from other methods. Many researchers and practitioners are familiar with using sum or aggregated scores, which are not appropriate for ideal-point contexts. Since the ideal-point methods more closely correspond to the way people respond to items, they can provide a better understanding of the psychology of behavioral assessment. As we demonstrated in this study, the probabilistic MDS single-ideal-point model helps us to explicate the response process that mirrors cognitive functions of people from a developmental perspective. Moreover, it explicitly modeled measurement errors inherent in single items. Of course, there is a need for vigorous experimental evaluation of the probabilistic MDS single-ideal-point model, testing its ability to provide more insights into cognitive processes involved in behavioral choices. In addition, it should be noted that the analysis was based on an ideal-point framework, and as such, we did not discuss validity issues with respect to content coverage, what construct was assessed, or inferences drawn based on single items. The focus was on models that can take into consideration the differentiability of various items in studying behaviors.

The approach used for measurement and assessment of typical or preferred behaviors in the current study has clear implications for counselors in assessment. First, although MDS preference modeling framework is not a new technique, its applications in direct modeling of individuals’ typical behaviors is needed to discover new phenomena as well as deepen existing knowledge about how individuals typically behave. Second, understanding of individuals’ typical behaviors, based on aggregated scores, may possess limitations that hinder counselors’ ability to apply knowledge and to think in nonlinear fashions. For example, counselors may need to focus on the different ingredients of coping constructs that clients tend to use as adaptive or maladaptive. Based on the preference assessment, the goal of treatment may be to expand clients’ reservoir of coping ingredients that are more adaptive for themselves. On a related point, counselors should know that avoidant behaviors or cognitions may not be entirely bad, just because they are labeled so. The key is to identify the typical avoidant coping behaviors that may be linked to negative psychological outcomes. Thus, it is important for counselors to help clients acquire individualized coping knowledge, perspectives, or skills that may be useful for solving specific problems.

Conclusion

The main purpose of the current study was to investigate grade-level differences in coping behaviors between middle school and college students by explicating the utility of the MDS single-ideal-point model in examining such developmental differences. This statistical model seemed to provide a viable alternative method for investigating students’ typical behaviors by tapping into its ability of working with items that better reflect what individuals typically do. The findings suggest that older individuals were more likely to use a mixture of coping behaviors covering areas of active coping, internal coping, or withdrawal coping rather than just one specific coping style (e.g., internal coping). In contrast, the results from MANOVA based on aggregated scores of three factors do not suggest grade-level differences in a particular coping style. Unlike a variable-oriented approach, MDS single-ideal-point analysis attempts to maximize the utility of coping items for studying developmental effects through a person-oriented approach.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Cody Ding is a faculty member in Educational Psychology at the University of Missouri-St. Louis. His research interests include growth modeling, MDS modeling, learning and cognition, and psychosocial development.

Dong Yang is a professor at the School of Psychology at Southwest University in China. His research interests include adult mental health, assessment of working environment, and alienation.

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