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Actual environments do affect motivation and psychological adjustment: A test of self-determination theory in a natural setting

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Abstract

This study examined the impact of the actual environment on changes in psychological adjustment over time. According to Self-Determination Theory (SDT; Deci and Ryan, Intrinsic motivation and self-determination in human behavior, 1985a, Plenum, New York; J Res Pers 19:109–134, 1985b; Psychol Inq 11:227–268, 2000), environments that are objectively supportive of autonomy should facilitate psychological adjustment through their impact on people’s subjective perceptions of autonomy and self-determined motivation. The present study tested this hypothesis using a prospective design with nursing homes residents. Results from structural equation modeling showed that actual autonomy-supportive nursing home environments were positively associated with residents’ perceptions of autonomy that in turn predicted self-determined motivation in major life domains. Self-determined motivation, in turn, predicted increases in psychological adjustment over a one-year period. Theoretical implications of the present findings are discussed in line with SDT.

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  1. Another approach would have been to measure residents’ self-determined motivation twice––at Times 1 and 2. However, the length of the scale was a clear obstacle with such a type of population, especially knowing that the attrition rate is high and that the sample size is usually small. We wanted the maximum number of participants to take part in the second phase of the study. Therefore, we decided to use a short questionnaire at Time 1 and opted for the method of a one-year time interval between residents’ perceptions of autonomy and self-determined motivation assessments.

  2. Because we wanted to assess the influence of nursing homes’ autonomy support on the motivational sequence at the between level of the nursing homes, we were interested in the between covariance of the model variables. We expected that the variance in nursing homes autonomy support between the nursing homes would lead to perceptions of autonomy at the mean average of the residents living in each nursing homes that would in turn lead to residents’ mean average of self-determined motivation and residents’ mean average of increases in psychological adjustment. Variation at the within level, that is variation among residents of a same nursing home, was also plausible because of individual differences—not all participants were equally self-determined or equally psychologically adjusted when entering their respective nursing home. Thus, these variations were expected and they were predicted to vary in the same direction of the hypothesized between level model (it was not the present research goal to examine differences between the nursing homes level [between level] and the residents level [within level]). Results from two separate path analyses using between and within (pooled) covariance matrices showed that model fit indices were excellent for both between and within models (p s > .35; RMSEA = 0.00, CFI > .95, SRMR < .05), thus suggesting that there was variance to be modeled at both levels (between and within). However, the between level model had to be computed from 11 nursing homes (groups = n = 11). Therefore, not all parameter estimates were significant at p < .05 (they were at p one-tailed < .10) (see Hox and Maas 2001 for an estimation of the bias in parameter estimates with a small group sample size in a multilevel structural equation analysis). Because both covariance matrices fit adequately the hypothesized model, it was hypothesized that they should vary in the same direction, but independently from each other. If such was the case, they could thus be combined on this basis. To test this model of invariance, we examined concurrently the entire model composed of the between and within covariances using a multilevel structural equation modeling technique (see Hox 2002; Stapleton 2006). In this analysis, both matrices (between and within) are modeled together according to the hypothesized model and are expected to vary independently and to be equal—that is, each parameter estimate and measurement error at the within level is constrained to be equal to its corresponding one at the between level. Fit indices for this model were excellent, χ2(10) = 11.69, p = .31, CFI = .96, GFI = .97, RMSEA = .042, thus suggesting that the same hypothesized model held at both the between and within levels. Because both matrices were independent and adequately and equally fit the model, we thus computed a path analysis with the whole variance (i.e., without separating between and within variances) in order to evaluate the significance of the parameter estimates and provide mediation analyses with a sufficient power to detect them. The details of this path analysis are presented in the Results section.

  3. The hypothesized path between psychological adjustment and perceptions of autonomy is equivalent to a covariance (see Hershberger 2006; Kline 2005 on equivalent models). However, because psychological adjustment at Time 1 is used as a control variable to remove shared variance with psychological adjustment at Time 2, it needs to be modeled as an exogenous variable in the model (psychological adjustment at Time 1 is not predicted by any variable). Hence, it is not well advised to have a covariance between an exogenous and endogenous variable in a structural equation model (because the measurement error of an exogenous variable is fixed to 1.00 for identification purposes). Thus, it has been replaced with a direct path. It should be understood that psychological adjustment at Time 1 is not meant to be seen as predicting perceptions of autonomy, but is only correlated with it (a covariation). However, such a covariation is in line with past research that has shown that psychological adjustment is associated with greater perceptions of autonomy (Deci et al. 2001).

  4. Controlling for residents’ daily required number of care hours or residents’ health level as rated by head nurses did not change the results. An alternative model was also tested. This alternative model tested the sequence “Actual Autonomy-Supportive Environment → Perceptions of Autonomy → Changes in Psychological Adjustment → Self-Determined Motivation”. Results revealed poor fit indices for this model, χ 2 (df = 4, n = 83) = 8.53, p = .07, RMSEA = .119, thus suggesting that the original hypothesized model should be preferred. Other potential alternative models were not theoretically or methodologically possible, thus they were not tested.

  5. Baron and Kenny’s classical step 1—that is, to assess the correlation between the predictor and the outcome—was dropped as simulation studies have shown that this step is not a necessary condition (Mackinnon et al. 2002), particularly with distal effect (such as in a prospective study) and with small sample sizes (Shrout and Bolger 2002).

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  1. Laboratoire de Recherche sur le Comportement Social, Département de Psychologie, Université du Québec à Montréal, Station “Centre-ville”, P.O. Box 888, Montreal, QC, Canada, H3C 3P8

    Frederick L. Philippe & Robert J. Vallerand

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Philippe, F.L., Vallerand, R.J. Actual environments do affect motivation and psychological adjustment: A test of self-determination theory in a natural setting. Motiv Emot 32, 81–89 (2008). https://doi.org/10.1007/s11031-008-9087-z

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