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STATISTICAL CORNER |
From the Department of Psychology (E.B., G.E.M.), University of British Columbia, Vancouver, British Columbia, Canada; and the Rush Institute for Aging (C.F.M.d.L.), Rush-Presbyterian–St. Luke's Medical Center, Chicago, Illinois.
Address correspondence and reprint requests to Ekin Blackwell, MA, UBC Department of Psychology, 2136 West Mall, Vancouver, BC V6T 1Z4, Canada. E-mail: ekinb{at}interchange.ubc.ca
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONCONCLUSIONNOTESREFERENCES |
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Methods and Results: We begin with a summary of the advantages that mixed regression models have over conventional statistical techniques in the context of repeated-measures designs. Next, we outline the conceptual and mathematical underpinnings of mixed regression models for a nonstatistical audience. The article ends with two examples of how these models can be applied in psychosomatic research; one deals with a prospective investigation of depressive symptoms and change in body mass index in older adults and the other with a diary study of social interactions and cortisol secretion.
Conclusions: Mixed regression models offer a flexible and powerful approach to analyzing repeated-measures data. They possess important advantages over more traditional strategies, and more widespread application of these models is likely to enhance the overall quality of psychosomatic research.
Key Words: mixed regression models • analysis of change • repeated measures • nested designs • random effects
Abbreviations: HLM = hierarchical linear model; OLS = ordinary least squares; ANOVA = analysis of variance; BP = blood pressure; CES-D = Center for Epidemiologic Studies–Depression; BMI = body mass index; D = dominance; EPAQ = Extended Version of the Personality Attributes Questionnaire.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONCONCLUSIONNOTESREFERENCES |
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Repeated Measurement Data Structures
Mixed regression models, a class of statistical models developed for the analysis of data structures with nested sources of variability, include hierarchical linear models (HLMs), growth curve models, and random coefficient models. They are rooted in time series analysis (1), mixed and variance components models (2), random effects models (3), and empiric Bayes models (4). Nesting occurs when units of observations at one level are clustered within higher-level observations. Examples of two-level nested structures include students within classrooms, children within families, families within neighborhoods, and spouses within couples. Nesting normally produces dependencies in the lower-level units. Sometimes this dependency is a nuisance that has to be controlled for, but it is often at the heart of the research question, specifically when the goal is to understand the source of the observed dependency. For example, in a data set in which families are nested within neighborhoods, the question may be how neighborhood-level characteristics (the higher-level units) such as social capital or median income relate to phenomena at the family level (the lower-level units) like children's educational achievement or delinquent behavior. Repeated-measures data represent a special kind of nesting in which units of measurement are nested within individuals. Dependency is the norm in repeated-measures data because observations obtained from the same individual tend to be correlated.
An important consideration in repeated-measures designs is whether to model time as a fixed or random factor. When the observed levels of a factor are the only ones of interest to the researcher, their effects on the outcome are fixed; they pertain to these levels only and cannot be generalized to levels that were not included in the study. This would occur, for example, in a study of immune functions at specific moments before, during, and after a discrete stressor; or in a study of well-being and health at specific intervals before, in the midst of, and after a woman goes through menopause. In other cases, the researcher is not interested in quantifying a measured outcome for any particular instance, but wants to infer its general pattern of change over time. The appropriate strategy in this situation is to model time as a random factor such that the moments of observation for each person represent a random sample of all possible observations during the study period. A longitudinal study tracking blood pressure (BP) levels throughout adulthood, or a diary study examining changes in BP during 10-minute intervals, are examples of this kind.
Traditional techniques like repeated-measures ANOVA can model time as either fixed or random, but violation of the sphericity assumption is a central concern. Sphericity dictates that all pairwise difference scores between observations have equal variance, but this is rarely found in unbalanced data sets. Failure to meet the sphericity assumption results in a serious liberal bias, or type I error (5). Although techniques such as the Greenhouse-Geisser correction can compensate for sphericity violations, mixed regression models allow this assumption to be relaxed altogether. Researchers can specify a variety of covariance structures to account for specific patterns of correlations. For instance, a first-order autoregressive structure is appropriate for many diary studies, in which correlations between observations are assumed to be an exponentially decreasing function of time. The choice of structure depends on the design and assumptions of the study as well as the nature and timing of the measurements. For a more detailed discussion, see Petkova and Teresi, Nezlek, Nezlek, and Schwartz and Stone (6–9).
It is also possible to analyze repeated-measures data in a multivariate framework that does not impose restrictions on the covariance matrix. However, this approach can lead to model overfitting and convergence problems, especially when the number of subjects is small relative to the number of repeated observations per subject. Unlike ANOVA techniques, mixed regression models do not require a "complete" data set, that is, the same number of assessments on all participants. Multiple imputation techniques provide another viable solution to the problem of incomplete data (10). Both strategies are superior alternatives to discarding data, at least when the data are assumed to be missing at random.
In the next sections, we consider another critical aspect of mixed regression models that sets them apart from ordinary least squares (OLS) techniques of estimation—their ability to simultaneously examine within- and between-person phenomena that contribute to change. The analysis of change can be thought of as being composed of two stages (11). In the first stage, we ask: What is the general shape or form of the process of change over time? Does it follow a linear or nonlinear pattern of increase or decrease or perhaps a cyclical pattern? We then use statistical modeling to derive a general form of change in the outcome variable from the individual-specific (within-person) patterns of change. In the second stage, we examine the heterogeneity in patterns of change between individuals and test the contribution of hypothesized factors to this heterogeneity. In the next section, we explain how these two steps fit into the general framework of the mixed regression model.
Basic Statistical Features of Mixed Regression Models for Repeated Measures
The mathematical underpinnings of the mixed regression model for repeated measures are presented in several excellent textbooks (12–15). Briefly, the most basic form of the two-level mixed regression model represents an outcome variable Y as a function of an intercept (ß0), a predictor variable A, and a random error term:
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The lower level (micro)units are indexed by i and the higher level (macro) units are indexed by j. The lower level units are measurement occasions and the higher-level units are individual subjects. In other words, Yij represents the outcome variable at time i for subject j. We can visualize the analysis as a set of j person-specific regression lines, one for each individual. Associated with each regression line are the coefficients ß0j and ß1j. These represent, respectively, the person-specific intercept and slope for individual j. These are presumed to vary among individuals.
Equation 1 is a general model for the first, or within-person, stage of analysis. In this phase, the analysis can take one of two forms. For example, in a longitudinal study examining changes in BP, the predictor variable Aij represents time elapsed since the first measurement occasion. Replacing Aij with the notation tij may be helpful in making this distinction. Thus, equation 1 is simply the regression of the outcome variable Y (BP) on time. This is commonly referred to as a growth curve model and is the conventional model for the analysis of change. The intercept ß0j is usually modeled such that it represents the person-specific value of Y at time "0," or baseline. Similarly, the slope ß1j represents the person-specific rate of change of Y over time.
This modeling approach differs from the diary example, in which the main interest was in how BP fluctuates with mood state. In such an analysis, BP at time i for subject j could be designated as the outcome variable Yij and mood state at the same time in the same subject as the predictor variable Aij. In this case, the predictor Aij is not time but a time-varying covariate. Analogous to the first example, intercept ß0j would represent the predicted BP value (Y) for subject j when mood (A) was rated as 0, and slope ß1j would represent the relationship between mood state and BP across sampling times for subject j.
In the second, or between-person, stage of analysis, the goal is to determine which person-level characteristics may explain differences in the within-person ß coefficients. Each coefficient is further broken down into a group mean and a deviation from that mean as follows:
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These equations are referred to as unconditional models, because they do not yet contain any level 2 predictors. 
00 and 10 signify the mean intercept and slope across all individuals. These are considered to be fixed, or constant. U0j and U1j are random variables designating the specific amounts by which individual j deviates from these means. These quantities will vary randomly from study to study depending on which subjects have been selected from the population. The term random reflects the fact that the parameter estimates (intercepts and slopes) are free to vary across individuals; hence, they have a variance. Although ß0j is referred to as the random intercept and ß1j as the random slope, the term mixed regression model implies that these coefficients have both a fixed and a random component. Just as the Rij residuals are assumed normally distributed with variance 
2, the U0j and U1j residuals are assumed normally distributed with variances 
02 and 12. Because the random intercepts and random slopes are often correlated, U0j and U1j also have covariance 01.
So far we have only presented models with a single predictor at level 1 representing either time itself or a time-varying covariate. A level 1 predictor serves to reduce the variance of the Rij residuals, signified by 2. If we want to explain even more of this unexplained variance, we can include additional level 1 predictors. Likewise, we can add any number of level 2 predictors to reduce the variances 02 and 12 associated with U0j and U1j. These predictors represent person-level characteristics such as sociodemographic information, personality features, and so on.
To illustrate, assume that Z denotes a single level 2 predictor called hostility. Equations 2 and 3 can be modified to reflect the contribution of hostility to the variability in the random intercept and random slope:
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The equation for ß0j is known as an intercepts as outcomes model and the equation for ß1j as a slopes as outcomes model owing to the fact that the intercepts and slopes at level 1 (ß0j and ß1j) are treated as outcome variables at level 2, each having their own intercepts (00 and 10) and slopes (01 and 11). Like in the unconditional model (equations 2 and 3), 00 and 10 signify the mean intercept and slope, respectively, across all individuals for Z = 0. The parameter 01 represents the slope of the regression equation predicting ß0j from Zj, and the parameter 11 represents the slope of the regression equation predicting ß1j from Zj. In the longitudinal study tracking change in BP, ß0j would represent the association between hostility and BP at time 0 or baseline, and ß1j would represent the association between hostility and rate of change in BP over time (see Table 1 for a glossary of terms used in these models).
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Before the development of mixed regression modeling techniques, these equations were often estimated in a two-step OLS estimation procedure. In the first step, ß coefficients would be generated for each individual; these would then be treated as outcomes in a second estimation procedure. Mixed regression models provide more accurate standard errors compared with this approach because they differentiate between the sampling error associated with the estimated coefficients and the observation error associated with the coefficients themselves, and use shrunken estimates to correct for the effects of sampling variation (12).
When thinking about level 2 predictors, it is important to distinguish between time- varying (state-like) and time-invariant (trait-like) characteristics. In the example presented previously, hostility was conceptualized as a trait that remains stable across time but can vary across individuals. It is thus appropriately modeled as a level 2 predictor. One could also measure hostility in a way that captures within-person fluctuations over time, in which case it should be treated as a level 1 predictor. This is analogous to the way mood was modeled as a level 1 covariate of BP in the diary study example.
Partitioning the within- and between-person sources of variability in outcome data produces more accurate estimates of associations between predictor and outcome variables relative to conventional regression and ANOVA approaches.
Centering
Centering involves shifting the zero point of a predictor's measurement scale to assign it a more meaningful reference. Because the intercept is defined with respect to this zero point, its interpretation will necessarily change. However, centering will not alter the interpretation of the slope in a linear model.
When a variable is uncentered, the intercept is the expected value of the outcome variable for a person whose score on the predictor variable equals 0. For example, in studies of infant development, it may be appropriate to structure the intercept so that it reflects values of the outcome variables at the time of birth (t = 0). However, imagine that a particular outcome variable such as number of spoken words was not assessed until 10 months of age (t = 10). The appropriate strategy in this case would be to shift the time scale so that the intercept reflects the value of this outcome variable at 10 months.
When the level 1 predictor is not time but rather a time-varying covariate of the outcome, centering can take the form of person-centering or grand-mean centering. When a level 1 predictor is person-centered, the intercept is the expected value of the outcome for a person whose score on the predictor variable equals the mean of his or her own observations. In a diary study, for instance, person-centering can be used to determine how each person's BP varies as a function of departures away from his or her average mood across the day. When a level 1 predictor is grand-mean centered, the intercept is the expected value of the outcome for a person whose score on the predictor variable equals the sample mean. So in a diary study predicting BP from mood, grand-mean centering can be used to relate each individual's BP readings to deviations away from the average mood of the group.
For level 2 (person-level) predictors, the relevant centering option is grand-mean centering. When centered in this manner, the intercept is the expected value of the outcome for a person whose score on the level 2 predictor variable equals the sample mean. This can be used to determine, for instance, how the relationship between BP and mood varies as a function of deviations away from the group mean on a trait-level characteristic such as hostility.
Note that we have limited our discussion of centering options to two-level nested structures, in which the highest level of nesting represents the person. When working with multiple groups represented by three-level data structures, group-mean centering is another option. Centering is discussed in greater detail elsewhere (6,7,9,13,15).
Applying Mixed Regression Models to Psychosomatic Research
Example 1: Depression and Change in Relative Weight in Older Adults
Obesity increases risk for a variety of adverse health outcomes, including mortality, cardiovascular disease, and diabetes (16–19). In older age, however, relative weight tends to decrease as a result of the loss of lean muscle mass that may result from declining physical health and activity levels (20–22). Because depression is associated with reduced activity levels and increased risk for chronic disease, we hypothesized that in older adults, higher levels of depressive symptoms would be associated with greater declines in relative weight over time.
The data are from a longitudinal study of 2812 older adults (23). Participants reported on their weight and height at baseline and during eight follow-up interviews, conducted at yearly intervals, for a total of nine waves of outcome data. The Center for Epidemiologic Studies–Depression (CES-D) scale was used to assess depressive symptoms (24). The within-person variable is time since baseline (in years). In addition to baseline CES-D, we included the between-person variables of age at baseline (in years) and sex as covariates. For the purpose of this analysis, we centered age at 75 and CES-D scores at the median value of 5.
Analytic Strategy
We used mixed regression models to test the association between baseline CES-D scores and change in relative weight during the 8-year follow-up period in the 2209 subjects who had nonmissing CES-D data at baseline and 
2 waves of nonmissing body mass index (BMI) outcome data. Their mean age at baseline was 73.8 and their mean BMI was 25.7 kg/m2. The level 1 model was specified as follows:
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where BMIij represents the BMI value for person i at time j; ß0j represents the person-specific intercept, or baseline BMI value; ß1j represents the person-specific slope of change in BMI over time; and Rij the residual error or deviation of the observed BMI values for each person i at each interview j. We then specified the level 2 model as follows:
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where the person-specific ß0j intercept is modeled as a function of the overall intercept 00 across all individuals, plus the systematic deviations from 00 that are accounted for by fixed variables Zj, plus the residual, unexplained deviation from ß0j, being U0j with variance 02. Similarly, the person-specific ß1j slope is modeled as a function of the overall slope of change 10 across all individuals, plus the systematic deviations from 10 that are accounted for by fixed variables Zj, plus the residual, unexplained deviations from ß1j, being U1j with variance 12. The fixed Zj predictor variables include age, sex, and CES-D scores, each of which may be associated with the intercept ß0j being the baseline value of BMI and with the slope ß1j being the rate of change in BMI over time. The main hypothesis is therefore tested by the term 11 for the Zj for CES-D, which represents the relationship between CES-D scores and the rate of change in BMI over time. This term represents the crosslevel interaction between the level 1 (within-person) variable, time, and the level 2 (between-person) variable, CES-D. We expected this term to be negative, because we hypothesized that higher CES-D scores would be associated with greater declines in relative weight over time (i.e., a more negative slope compared with the average slope). The analysis proceeded in two steps. At the first step (model A), we fit an unconditional level 2 model, that is, one without any level 2 predictor variables. At the next step (model B), we added the level 2 variables representing age, sex, and CES-D.
Depressive Symptoms and Rate of Change in Body Mass Index
The results of model A (see Table 2) indicate that the average BMI at baseline was 25.8. The unconditional estimate for ß1j, represented by the 10 for time (–0.206), indicates a decline in BMI averaging 0.206 kg/m2 units per year. The variance components indicate significant random coefficients for the intercept (U0j = 18.557, p < .001) and slope (U1j = 0.118, p < .001). Thus, there is evidence for significant variability in the person-specific intercepts and the person-specific rates of change in BMI over time. This justifies our efforts to identify the determinants of the between-person variation in BMI and change in BMI. We can use these coefficients as a base with which to compare the results of subsequent models. The objective of entering additional predictors of BMI is to explain the variation in BMI initial status and rate of change that exists between persons. If we select relevant predictors of this process, we should expect a reduction in the size of these coefficients. The within-person residual variance (Rij) reflects the difference between observed and predicted outcome scores resulting from the "random noise" in the instruments we use to measure outcomes. More reliable measurement instruments will normally reduce the magnitude of this variance component.
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The results of model B indicate that CES-D scores were unrelated to baseline levels of BMI (01 = –0.002, p > .50). In contrast, CES-D scores were significantly associated with decline in BMI levels (11 = –0.003, p = .03). The coefficient may be interpreted as follows: the average decline in BMI per year, according to model B, is –0.222. It should be noted that this estimate only applies to subjects who have a 0 value on all the covariates in the model; in other words, a 75-year-old (centered value of age) woman with a CES-D score of 5 (centered value on the CES-D). Each one-point increase on the CES-D above a score of 5 is associated with a 0.003 greater rate of yearly decline in BMI, and each one-point decrease below a score of 5 is associated with a 0.003 smaller rate of yearly decline. Older age is associated with lower BMI at baseline (01 = –0.121, p < .001) and with faster decline in BMI over time (11 = –0.012, p < .001). Male sex was not significantly associated with BMI levels at baseline or with decline over time.
One way to illustrate the nature of the association between CES-D scores and change in BMI is to use the results of the last regression model (model B) to compute the predicted values of BMI over time as a function of different levels of CES-D (see Fig. 1). To that end, we selected CES-D scores in this cohort corresponding to the 10th percentile (CES-D = 0), the median or 50th percentile (CES-D = 5), and the 90th percentile (CES-D = 19). We then plotted the predicted values of BMI until the end of follow up after selecting specific values for age (75, the centered value for this variable) and sex (female), the other two variables in the model (see Fig. 1). Although there were only minimal differences in predicted BMI levels at baseline between the three different levels of CES-D, each higher CES-D score was associated with a greater decline in BMI over time.
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Inspection of the variance components reveals that addition of the fixed effects in the level 2 model accounts for relatively small portions of the between-person variability in initial status in BMI (intercept) and rate of change in BMI (slope). We can compare the change in each of the random coefficients from model A to model B to determine the proportion of explained variance. For the intercept, the proportion of additional variance may be computed as ([18.557 – 17.927]/18.557) x 100%, or approximately 3.4%. Similarly, the reduction in the random slope is ([0.118 – 0.113]/0.118) x 100% or 3.1%. In other words, the fixed effects account for approximately 3.4% of the variation in baseline BMI values that exists between persons in this sample and 3.1% of the variation in the linear rate of change in BMI.
Example 2: Communal Orientation Moderates the Association Between Diurnal Cortisol Rhythm and Abrasive Social Interactions
Dominance has been implicated as a risk factor for cardiovascular disease in prospective studies (e.g., (25,26)). Although reactivity to social stress is thought to be a mechanism for this link, laboratory studies of acute social strain have yielded mixed findings (27–30). We reasoned that this inconsistency might be resolved by considering person–environment fit. People are more prone to physiological distress when their preferred behavioral style clashes with situational demands (e.g., (31)). Because social conflict should allow dominant individuals to display their preferred style, which is to exert control, they should evidence few signs of physiological stress. In contrast, individuals who are low in dominance may exhibit greater signs of physiological stress during discordant interactions, because these situations may challenge their normally passive stance.
We recently investigated whether dominance moderates the relationship between abrasive social interactions and diurnal cortisol secretion in daily life. Cortisol secretion usually peaks in the early morning and then declines throughout the day (32). Exposure to chronic stressors may result in a flattened diurnal rhythm (33,34). Disruptions in the regular pattern of cortisol release could have ramifications for a variety of biologic processes in the immune, circulatory, metabolic, and nervous systems (33,35) and may predict physical health problems (33,36,37).
Data were obtained from 87 healthy volunteers (82% female, mean age 29 years), 52 of whom were instructed to collect data across 4 nonconsecutive days and 35 of whom were instructed to collect data across 3 nonconsecutive days. A handheld computer sounded a daily alarm at 1, 4, 9, and 11 hours after the participant's planned wakeup time. Thus, ambulatory data collection occurred four times a day over a period of either 3 or 4 days. Our outcome measure was diurnal cortisol rhythm, which was computed by log-transforming the raw values and then regressing the transformed values on sampling times for each day separately. Note that we did not use individual cortisol assessments as our outcome measure, but rather the estimated cortisol rhythm we computed for each day per participant. Thus, we have a two-level model with cortisol rhythm across days nested within persons. Although this data set in fact represents three levels of data collection (cortisol output at each time-point nested with days in turn nested within persons), we reduced it to a two-level structure for the purpose of this example. In our own work, we have analyzed these data with both three- and two-level models; the results were similar.
Abrasive interactions were assessed using three questions from the Diary of Ambulatory Behavioral States (38). Each item used a 5-point scale ranging from 0 (none at all) to 4 (extremely). We collapsed the responses to these three questions to create an average measure of abrasive interactions at each time point. Because our outcome measure (cortisol rhythm) represents a day-level variable, we further collapsed these averages across the four time points on each day to obtain a day-level measure for abrasive interactions. However, the day-level responses were highly skewed with few ratings in the 1 to 4 range. We therefore recoded these scores to a dichotomous variable, with 0 indicating no abrasive interactions on that day and 1 indicating one or more abrasive interactions. This indicator created two groups of roughly equal frequencies representing low and high levels of abrasive interactions. This variable was used as the level 1 predictor in the ensuing analyses. Dominance (D) was measured using the Unmitigated Agency subscale of the Extended Version of the Personality Attributes Questionnaire (EPAQ) (39).
Analytic Strategy
The nested design of our study (either 3 or 4 days of data collection nested within each participant) allowed us to use mixed regression modeling to test whether the within-person relationship between abrasive social interactions and diurnal patterns of cortisol secretion varies as a function of the between-person characteristic D. A two-level model was constructed with random slopes and intercepts at the first level modeled as outcomes at the next level up. At level 1 (day-level), the between-day variability in cortisol rhythm for each individual was modeled as a function of abrasive social interactions (AI):
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In this equation, the abrasive interactions covariate has been person-centered. In general, level 1 centering will reduce problems in estimation because it reduces the covariance between the intercept and the slope (7). Centering also enables us to ask how each individual's cortisol rhythm changes in response to fluctuations from his or her own usual pattern of interactions. That is, the questions becomes: How does a person's cortisol rhythm differ on days when he or she has high compared with low levels of abrasive interactions? As evident from this question, the analysis is completely within-person; centering eliminates the influence of between-person differences in abrasive interactions on the outcome.
The regression coefficients ß0j and ß1j can be understood as follows: For each individual j, ß0j represents the expected value of diurnal cortisol rhythm on average interaction days (i.e., when abrasive exchanges are at person j's mean daily level), and ß1j represents how diurnal cortisol rhythm varies in response to deviations from person j's average levels of abrasive interactions. The residual error associated with each observation is denoted by Rij.
At level 2 (person-level), the between-person variability in ß0j and ß1j were modeled as a function of dominance:
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Note that the dominance variable was grand-mean centered so that Dj reflects the amount by which each person deviates from the sample mean. In other words, Dj = (Dj – Dmean). This centering strategy allows for a more meaningful interpretation of the intercept, because a score of 0 falls outside of the range of possible dominance scores. With the dominance score centered in this manner, the intercept parameters 00 and 10 denote the expected values of ß0j (diurnal cortisol rhythm on typical interaction days) and ß1j (the relationship between diurnal cortisol rhythm and abrasive interactions) for a person whose dominance score equals the sample mean. The slope parameter 01 estimates the relationship between ß0j and D, or the main effect of D. It signifies whether diurnal cortisol rhythm on typical interaction days varies according to between-person differences in dominance. However, our main interest is in the slope parameter 11, which estimates the relationship between ß1j and D, or the D x AI interaction effect. Importantly, this parameter enables us to test our hypothesis that the relationship between diurnal cortisol rhythm and abrasive interactions is moderated by this trait. The terms U0j and U1j capture unexplained variability in ß0j and ß1j after including this predictor.
A standard assumption of mixed regression models is that within-person residuals are independent. However, as mentioned earlier, this is not always the case in diary studies in which observations are so closely spaced together within the same person. Models with different variance–covariance structures can be compared using a likelihood ratio test, which compares the deviances of models with different restrictions. Deviance is a measure of lack of fit between the model and the data. Using this option, we compared the fit of a standard homogenous level 1 variance model (which also assumes uncorrelated level 1 residuals) to the fit of an autoregressive model, which assumes that level 1 residuals are correlated in a time-decreasing fashion. The 
2 statistic indicated that the two models did not differ significantly for our data set (2 = 10.92, p = .141). We therefore assumed an uncorrelated residual structure in the ensuing analysis. This lack of autocorrelation in our data set may be the result of the wide time intervals between measurements. In studies in which measurement intervals are shorter, an autoregressive structure often provides a better fit.
The analysis followed the same sequence as in the previous example. At the first step (model A), we specified an unconditional model without the level 2 predictor D. At the next step (model B), we added the level 2 predictor D. The coefficient for the D x AI interaction represents the test of the hypothesis. This two-step procedure allows us to compare the random coefficients of model A and model B and calculate the proportion of variance that is explained by including the level 2 predictor.
Abrasive Interactions, Cortisol Rhythm, and Dominance
The data were analyzed using the HLM software package (40). However, the same model may also be fitted in other software packages such as SAS (procedure MIXED) (41) and SPSS (command VARCOMP) (42). Results of the analysis are presented in Table 3. For the unconditional level 2 model (model A), the expected (mean) cortisol rhythm for the overall sample on average interaction days is 00 = –0.053, which is significantly different from 0. The estimated value of 10 is 0.003, which is nonsignificant. This suggests a very weak association between cortisol rhythm and abrasive interactions in the sample as a whole.
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We now turn to the results of the level 2 model (model B). The values for 00 and 10 now have slightly different interpretations. These parameters estimate, respectively, the expected values of cortisol rhythm on average interaction days and the relationship between cortisol rhythm and abrasive interactions for a person whose D score equals the sample mean (Dmean = 2.1). The estimates were not appreciably different from those obtained in the unconditional level 2 model (model A).
Importantly, and confirming our hypothesis, the level 2 model reveals a significant D x AI interaction effect (11 = –0.012, p = .02). This indicates that the strength of the within-person association between abrasive interactions and cortisol rhythm is moderated by trait dominance; the relationship between cortisol rhythm and abrasive interactions becomes increasingly negative for each D score above the sample mean and increasingly positive for each D score below the sample mean. So for individuals with high trait dominance, cortisol rhythms tend to be steeper on days when abrasive interactions exceed usual levels but flatter on days when abrasive interactions fall below usual levels. The finding of flatter (i.e., more dysregulated) rhythms on low conflict days was surprising, because we had expected cortisol rhythms to be similar for dominant persons regardless of frequency of conflict. It may be that for persons with a strong need for interpersonal control, neutral social interactions are perceived as potential threats rather than opportunities for affiliation. The reverse pattern is observed for individuals with low trait dominance whose cortisol rhythms tend to be flatter on days when abrasive interactions exceed usual levels and steeper on days when abrasive interactions fall below usual levels. Thus, the biologic consequences of conflict appear to be higher for persons who are low in dominance, possibly because they view conflict as more challenging or anxiety-provoking. However, in the absence of conflict, they may have a biologic advantage compared with their high-dominance counterparts. This interaction effect is illustrated in Figure 2 for D scores corresponding to the 25th, 50th, and 75th percentiles of the distribution.
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Before leaving this example, we draw attention to the variance components statistics in Table 3, which tell us how much of the variability in the outcome measure remains unaccounted for at each level of the model. By comparing the random coefficients from model A and model B, we can determine the proportion of between-person variance that is explained by inclusion of the level 2 predictor variable D. For the random intercept, there was no detectable change in variance between the two models. That is, the fixed effects did not contribute in any appreciable way to the between-person variability in cortisol rhythms on average interaction days. The proportion of change in the slope variance may be computed as ([0.00016 – 0.00015]/0.00016) x 100%, which is approximately 6%. This indicates that the fixed effects account for approximately 6% of the between-person variability in the relationship between cortisol rhythm and daily abrasive interactions.
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CONCLUSION |
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This research was supported by grants from the National Institute of Environmental Health Sciences (ES 10902) and the National Institute on Aging (AG 11101), the Canadian Institutes of Health Research, the Michael Smith Foundation for Health Research, the National Alliance for Research on Schizophrenia and Depression, the National Heart, Lung, and Blood Institute, National Institute of Environmental Health Sciences, and the Social Sciences and Humanities Research Council of Canada.
DOI:10.1097/01.psy.0000239144.91689.ca
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 A. M. Smith, T. J. Loving, E. E. Crockett, and L. Campbell What's Closeness Got to Do with It? Men's and Women's Cortisol Responses When Providing and Receiving Support Psychosom Med, October 1, 2009; 71(8): 843 - 851. [Abstract] [Full Text] [PDF]
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L. L. Barnes, C. F. Mendes de Leon, J. L. Bienias, R. S. Wilson, S. A. Everson-Rose, and D. A. Evans Hostility and Change in Cognitive Function Over Time in Older Blacks and Whites Psychosom Med, July 1, 2009; 71(6): 652 - 658. [Abstract] [Full Text] [PDF]
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I. Korstjens, A. M. May, E. van Weert, I. Mesters, F. Tan, W. J. G. Ros, J. E. H. M. Hoekstra-Weebers, C. P. van der Schans, and B. van den Borne Quality of Life After Self-Management Cancer Rehabilitation: A Randomized Controlled Trial Comparing Physical and Cognitive-Behavioral Training Versus Physical Training Psychosom Med, May 1, 2008; 70(4): 422 - 429. [Abstract] [Full Text] [PDF]
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