Psychometric Properties of the Short Self-Regulation ...

Journal of Psychology in Africa 2009 19(3); 321-328 Printed in USA - All rights reserved

Copyright Ó2009

Journal of Psychology in Africa ISSN 1433-0237

Psychometric Properties of the Short Self-Regulation Questionnaire (SSRQ) in a South African Context Johan C. Potgieter Karel F.H. Botha North-West University, Potchefstroom Campus. Address correspondence to Dr. J.C. Pogieter, School for Psychosocial Behavioural Sciences, Department of Psychology, Private Bag X6001, Potchefstroom, South Africa, 2520. E-mail: [email protected] Properties of the SSRQ

Self-regulation (SR), an important construct within the psychological well-being context, involves the ability to monitor behaviour, to contrast it with reference values and to introduce adjustments where necessary. A lack of validated measures of SR in the South African context has contributed to the current caveat in our knowledge of the potential importance of this construct. This investigation aimed to establish the utility of the Short Self-Regulation Questionnaire (SSRQ; Carey, Neal & Collins, 2004) in a South African context. This short version of the original Self-Regulation Questionnaire (SRQ; Brown, Miller & Lawendowski, 1999) was completed by a group of 385 undergraduate psychology students. Factor analysis produced 7 factors, all of which showed significant positive correlations with other measures of psychological well-being. This first step toward establishing the importance of SR in non-Western cultures reflected positively on the potential use of the SSRQ in large epidemiological studies. 321-328

Keywords: self-regulation, Short Self-Regulation Questionnaire (SSRQ), psychological well-being, scale validation, South African context

Introduction Research spanning more than three decades (Kanfer, 1970) has seen an evolution in the conceptualisation of self-regulation (SR), from an initially rigid stimulus-response conceptualisation, where it was defined as “the capacity to behave, in the relative absence of supportive external structures, according to a directive or command that was originally given externally” (Diaz & Fruhauf, 1991, p. 88) to a conceptualisation that acknowledges the complexity of the SR process as encompassing developmental, personality and social determinants of individual decisions and behaviours (Brown, Miller & Lawendowski, 1999). Beckman and Kellmann’s (2004) definition of SR captures the complexity of the SR process as involving the control of thinking, emotion, attention, and concentration. These control processes help an individual maintain and effectively perform an action, even if the performance conditions are adverse. Importantly, SR is seen as a systematic process (Jackson, Mackenzie & Hobfoll, 2000) involving the individual’s ability to constantly monitor his/her behaviour, to contrast it with his/her reference values, goals and behavioural standards, and to constantly introduce the necessary adjustments to maintain the agreement between behaviour and reference values, between what he/she is doing and what he/she intends and would like to do (Bermudez, 2006). Theories on self-regulation. A number of theories have been developed in an attempt to explain the SR process. The cybernetic view of SR developed by Carver and Scheier (1982; 1998;) was one of the first self-identified SR theories. From this perspective, SR is viewed in terms of a feedback loop. In the initial “test” phase, a person determines his or her current state and compares it to a desired or preferred state. If a discrepancy is detected, the “operate” phase is initiated, which involves actions intended to move the self toward the desired end state. Progress toward the goal is monitored by further “test” phases.

When the desired end state has been achieved, the “test” phase will reveal no discrepancy between current and desired states, so the process is terminated, constituting the “exit” phase of the feedback loop. Miller and Brown (1991), influenced by the research of Kanfer in the early 1970’s, expanded the number of processes involved in SR to seven. These are receiving relevant information; evaluating the information and comparing it to norms; triggering change, searching for options; formulating a plan; implementing the plan; and finally, assessing the plan’s effectiveness. Brown, Miller and Lawendowski (1999) developed the 63-item Self-Regulation Questionnaire (SRQ) as a first attempt to assess these seven self-regulatory processes through self-report. The SRQ was then adapted into the Short Self-Regulation Questionnaire (SSRQ; Carey, Neal & Collins, 2004), a 31-item version of the SRQ. Research on SR. A generic finding throughout the history of SR research has been the existence of a strong association between SR and various positive outcomes in a wide variety of life domains, including relationships, work, religion (Brown & Ryan, 2004), as well as personal health and well-being (Siegel, 2007; Sokol & Müller, 2007). One possible explanation for this is that the ability to effectively regulate one’s progress toward goal fulfilment does not only serve as a buffer against the deleterious effects of stress, but that it also minimises the likelihood of an individual experiencing negative or stressful life events (Aspinwall & Taylor, 1997). In contrast, Brown, Miller and Lawendowski (1999) have found that individuals who are low in self-regulatory capacity are unable to flexibly adapt their behaviour in challenging circumstances, which exposes these individuals to the development of a psychological disorder. The South African Context. In the current South African context, the maintenance of psychological well-being at individual level is very challenging. In addition to historical socio-economic disparities (Møller, 2007) and the pervasive effects of apartheid

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Properties of the SSRQ

and racism (Murray, 2002), various factors that characterise the current South African context pose significant challenges to the well-being of individuals. These factors include, but are not limited to, the rapid rate of urbanisation (Vorster et al., 2000), extreme poverty (Barbarin, 2003), the enormous challenges of coping with the HIV/AIDS pandemic (Ferreira, 2008) as well as fragmented, under-resourced public mental health services (Lund & Flisher, 2006). In light of the positive contribution that SR could make in this regard, it is an unfortunate state of affairs that no reliable measure of SR exists that is short enough for inclusion in large epidemiological surveys, as well as health-related studies with physically ill patients where testing periods are typically very limited (Ibaòez, Ruipérez, Marqués, & Ortet, 2005). The resultant ignorance of the possible impact that effective SR might have on the well-being of individuals could prove to be costly. The Goals of the Study. This investigation aimed to establish the psychometric properties of the SSRQ in a South African context. More specifically, the objectives are to establish the i) factor structure; ii) reliability; and iii) concurrent validity of the SSRQ in a South African student sample.

Method Participants and setting. An availability sample of undergraduate students (N=385) who took psychology as a subject at a South African University took part in the study. They were predominantly white (94.85%), female (73.96%), third year students (41.53%), aged between 18 and 21 years (82.55%). Instruments. A self-compiled biographical questionnaire provided information regarding participant’s age, gender, language and ethnic group. The main focus of this study, the SSRQ (Carey, Neal & Collins, 2004), is a 31-item self-report questionnaire which, in contrast to the original longer version (Brown, Miller & Lawendowski, 1999) produced a one factor solution in the measure of an individual’s capacity for SR. The authors reported an overall Cronbach alpha of 0.91 for the 31 items. To determine the concurrent validity of the SSRQ, the following scales were included because of the strong association between SR and psychological well-being. The 20-item Affectometer 2 (AFM2) (Kammann & Flett, 1983) was developed to measure a sense of general well-being and happiness. Psychological well–being is measured on an affective level by determining the balance between positive and negative affect (Kammann & Flett, 1983). The ratio between the positive and negative affect scores constitutes a person’s positive-negative affect balance (PNB). The extent of well-being experienced is presented by the predomination of Positive affect over Negative affect (Kammann & Flett, 1983). Cronbach alpha reliability indices of 0.88 to 0.93 were indicated by Kammann and Flett (1983). Wissing et al. (1999) attest to the applicability of this scale in a South African context. The 5-item Satisfaction with Life Scale (SWLS) (Diener, Emmonds, Larsen & Griffin, 1985) was developed to give an indication of an individual’s general satisfaction with life. An evaluation of quality of life, as indicated by personal criteria, is measured on a cognitive-judgmental level. Diener et al. (1985) report a two month test–retest reliability index of 0.82, and a Cronbach alpha–reliability index of 0.87. The SWLS was found to be valid and reliable to be used in a South African context (Wissing et al., 1999), and manifested a Cronbach alpha reli-

ability index of 0.69 in a recent South African study (Keyes et al., 2008). The 28-item General Health Questionnaire (GHQ) (Goldberg & Hillier, 1979) was developed to detect common symptoms that are indicative of the various syndromes of mental disorder, and differentiates between individuals with psychopathology as a general class and those who are considered to be subclinical. Sub-scales are: Somatic Symptoms (SS), Anxiety and Insomnia (AI), Social Dysfunction (SD) and Severe Depression (DS). Goldberg et al. (1997) reported extensively on the reliability and validity of the GHQ. Cronbach alpha reliabilities reported vary from 0.82 to 0.86 (Goldberg et al., 1997) and 0.77 to 0.84 for sub–scales and 0.91 for the Total Scale Score in a South African sample (Wissing & Van Eeden, 2002). In a recent South African study, cronbach alpha reliability indices of 0.74 (SS), 0.74 (AS), 0.55 (SD), 0.75 (DS), and 0.89 (total scale) was found (Keyes et al., 2008). The Mental Health Continuum – Short Form (MHC-SF) (Keyes, 2005) consists of 14 items. It measures the degree of emotional well–being (EWB) (items 1–3), social well–being (SWB) (items 4–8) and personal / psychological well–being (PWB) (items 9–14). In a recent validation study of this instrument for the South African context produced acceptable results regarding its internal consistency, with cronbach alpha’s ranging between 0.59 and 0.74. The instrument also revealed sound criterium related validity (Keyes et al., 2008), and was found to give a good indication of the mental health of individuals from the South African context. In determining the concurrent validity of the SSRQ, it was also decided to include two constructs that are theoretically related to SR, namely mindfulness and self-efficacy. The Mindful Attention Awareness Scale (MAAS) (Brown & Ryan, 2003), is a 15-item scale that measures mindfulness, defined as the receptive attention to and awareness of present events and experience. The authors report internal reliability with an alpha of 0.82. which was confirmed by Carlson and Brown (2005), with an alpha of 0.87. The Generalized Self-Efficacy Scale (GSE) (Schwarzer & Jerusalem, 1993), a 10-item scale, was developed to provide a measurement of the strength of an individual’s conviction in his/her ability to react successfully to minimum pressures as well as to difficult situations, and to cope with any associated setbacks. Schwarzer and Jerusalem (1993) report Cronbach alpha reliability indices of between 0.82 and 0.93 and test–retest reliability indexes of 0.47 for males and 0.63 for females over a 2 year period. They also contest to the good construct validity of the GSE. This was confirmed by Van Straten, Temane, Wissing and Potgieter (2008), who recently attested to its applicability for use in a South African context. Procedure. Participants consented to take part in the study. They were informed about the study in one of their classes, after which they completed consent forms and were provided a test booklet. The completion of the battery of questionnaires took approximately 45 minutes. Detailed information of the participant group is provided in Table 1. Data Analysis. According to Paunonen and Ashton (1998) five psychometric properties need to be considered when determining the cross-cultural applicability of scales, namely scale means, variances, reliability, criterion-related validity and factor structure. Descriptive statistics and reliability indices were used to determine reliability. To determine the factor structure of the SSRQ, a principal component factor analysis with oblique rotation was applied to extract factors. Kaiser-Meyer-Olkin’s Mea-

Journal of Psychology in Africa 2009 19(3); 321-328

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Table 1 Sociodemographic Variables of Participant Group Variable Year group

Age

Gender

Marital status

n

Cumulative %

1st

99

27.97

27.97

2nd

104

29.38

57.34

3rd

147

41.53

98.87

18

16

4.17

4.17

19

86

22.40

26.56

20

100

26.04

52.60

21

115

29.95

82.55

22

38

9.90

92.45

Male

100

26.04

26.04

Female

284

73.96

100.00

Not Married

359

99.36

99.36

6

1.64

100.00

Married Ethnicity

%

350

94.85

94.85

Coloured

White

3

0.81

95.66

Black

15

4.07

99.73

Indian

1

0.27

100.00

The Kaiser-Meyer-Olkin Measure of Sampling Adequacy sure of Sampling Adequacy and Bartlett’s Test of Sphericity to support the factorability of the data, were also applied. Finally, was 0.911, which is “superb” according to Field (2005). The Pearson correlations between the different scales was done to Bartlett’s Test of Sphericity supported the factorability of the data with an approximate Chi-square of 4416.107(n = 380, establish concurrent validity. df = 378 and p < .0001), the lowest value yielded by all available extraction methods. This gives an indication that the seven-facResults Descriptive statistics. Descriptive statistics and reliability in- tor model is the best fit for the data, and that there is a minimal dices are provided in Table 2. Reliability indices of the SSRQ likelihood of the existence of additional factors that should be were satisfactory, yielding a Cronbach alpha of 0.895 for the to- explored. Communalities before rotation were 0.40 or higher tal scale, merely indicating that the SSRQ as a measure of SR across the board, which can be regarded as sufficient. Accordhas some degree of homogeneity. Nunnally and Bernstein ing to Costello and Osborne (2005), low to moderate (1994) reported that a modest reliability of 0.70 could be re- communalities (0.40 to 0.70) can be expected in the social scigarded as sufficient during the early stages of scale validation. ences. All the indicated factors have more than the minimum suggested number of three items per factor and therefore can The total scale scores (M=101.34, SD=14.73, be seen as strong factors (Costello & Osborne, 2005). More range=58-137) were slightly lower than the scores originally obitems with similar content might, however, be included in factors tained by Carey et al. (2004). When the total scale scores were 6 and 7 for future research in order to create stronger factors, compared across various demographic variables, including greater cohesion between individual items and improved age, gender, year group, ethnicity and marital status, no significommunalities (Van der Walt, Potgieter, Wissing, & Temane, cant differences were found. The majority of item means varied 2008). between 3.0 and 4.0, and standard deviations ranged between After rotation, the seven-factor model indeed proved to be the 0.65 and 1.19. Item-total correlations ranged between 0.17 and best fit for the data, as it resulted in the “cleanest” factor structure 0.65. Almost all the items yielded item-total correlations that fall within the desirable range as described by Clark and Watson with the least cross-loadings, as most items loaded above 0.30 (1995), namely values between 0.15 and 0.55. This clearly illus- and no factors had fewer than three items (Costello & Osborne, trates a large degree of homogeneity within the SSRQ, which 2005). This solution was invariant across gender, academic year and age. Variability across ethnicity could not be determined, as confirms the Cronbach alpha previously reported. groups other than whites were underrepresented in this sample. Factor analysis. A principal component analysis with After eliminating items with significant cross-loadings, as oblique rotation revealed seven factors that explained 61.79% of the total variance. Although it produced the same number of well as items that did not load significantly on any of the factors, factors, the content of the items were different from the ratio- the remaining items provided a clear distinction between the difnally-derived subscales of the original scale formulated by Miller ferent factors, and allowed for relatively easy interpretation of and Brown (1991), as well as the single factor solution subse- the meaning of factors. Specifically, items 18, 29 and 31 not quently found by Carey et al. (2004). The results of the factor only loaded significantly on more than one factor, but also showed low communalities, and low item-total correlations (Taanalysis are provided in Table 3.

324


Table 2 Descriptive statistics and reliability indices for the SSRQ (N=381*) V SSRQ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

M

SD

R Min.

3.54 3.68 3.65 3.87 3.35 3.68 3.36 3.55 4.33 2.61 3.56 3.97 3.85 3.60 3.31 3.77 3.86 4.00 3.52 3.62 3.89 2.86 3.91 3.46 3.40 3.00 4.07 3.51 4.11 3.69 3.72

1.04 1.03 0.97 1.02 0.95 0.75 1.04 1.04 0.73 0.99 1.01 0.87 0.81 0.87 1.04 0.92 1.00 0.82 0.86 0.95 0.77 0.98 0.88 0.97 0.99 1.13 0.92 0.97 0.65 0.79 1.19

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

CT Max. 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

0.64 0.54 0.24 0.42 0.57 0.49 0.54 0.38 0.31 0.58 0.29 0.54 0.50 0.58 0.52 0.59 0.48 0.38 0.43 0.17 0.50 0.32 0.50 0.26 0.47 0.65 0.31 0.44 0.33 0.49 0.41

Note: V = Variable; M = Mean; SD = Standard deviation; R = Range and CT = Correlation with Total; * = casewise deletion of missing data. ble 3). The low internal consistency of these items could be ascribed to a variety of factors. When the content of these three items was reviewed, it was apparent that unclear formulations could have significantly contributed to their poor performance. For the purpose of this study it was decided to withdraw these items from subsequent analyses. Items 2, 8, 16, and 25 also loaded significantly on more than 1 factor, but item content was used as criterium to establish in which factor they belong. Subsequently, item 2 (I have a hard time setting goals for myself) was included in factor 2, decision making, while item 8 (I tend to keep doing the same thing, even when it doesn’t work) was included in factor 3, learning from mistakes. Items 16 (Most of the time I don’t pay attention to what I’m doing) and 25 (Often I don’t notice what I am doing until someone calls it to my attention) were both included in factor 7, which constitutes mindful awareness. Future studies could, however, consider reformulating the items so that the specific aspect of SR that it measures is more clearly expressed in the item content. Based on an analysis of the content of remaining items, the extracted factors were labelled as follow, each with one item as an example:

1. Monitoring: “When I am trying to change something, I pay attention to how I am doing.” 2. Decision making: “When it comes to deciding about a change, I feel overwhelmed by the choices.” 3. Learning from mistakes: “I usually only have to make a mistake once in order to learn from it.” 4. Perseverance: “I have trouble following through with things once I’ve made up my mind to do something.” 5. Self-evaluation: “I set personal standards, and try to live up to them.” 6. Creativity: “As soon as I see a problem or challenge, I start looking for possible solutions.” 7. Mindful awareness: “I don’t notice the effects of my actions until it’s too late.” The component correlation matrix and reliability indices of the different subscales are shown in Table 4. According to Field (2005) “ . . . if your questionnaire has subscales, Chronbach’s alpha should be applied separately to

Journal of Psychology in Africa 2009 19(3); 321-328

325

Table 3 Exploratory Factor Analysis using the Principal Componentsmethod of Factor Extraction, and Promax with Kaiser Normalisation used as Rotation Method Variable

Factor loadings 1

SSRQ 19 SSRQ 6 SSRQ 5 SSRQ 14 SSRQ 2 SSRQ 22 SSRQ 25 SSRQ 26 SSRQ 15 SSRQ 1 SSRQ 31 SSRQ 16 SSRQ 8 SSRQ 23 SSRQ 28 SSRQ 17 SSRQ 29 SSRQ 18 SSRQ 11 SSRQ 4 SSRQ 12 SSRQ 10 SSRQ 13 SSRQ 27 SSRQ 24 SSRQ 9 SSRQ 3 SSRQ 21 SSRQ 30 SSRQ 7 SSRQ 20

.903 .889 .767 .737 .435

2

3

.331 .746 .621 .608 .531 .501 .455 .446 .430

4

Comm. 5

6

7

.327

.412 .371 .327

.337 .840 .817 .707 .362 .327

.349 .307 .968 .808 .681 .631 .400 .706 .617 .531 .721 .624 .624 .671 .659

.728 .626 .638 .619 .620 .491 .564 .482 .505 .696 .406 .615 .584 .764 .643 .681 .509 .450 .674 .606 .719 .607 .613 .536 .469 .415 .546 .606 .549 .696 .604

Note: Values less than 0.3 are not displayed.

Table 4 Cronbach Alpha Reliabilities (in brackets) and Component Correlation Matrix of the SSRQ SSRQ

1

2

3

4

5

6

7

1. Mon 2. Dec 3. Lea 4. Min 5. Per 6. Cre 7. Sel Total

(0.83) .384 .514 .624 .439 .436 .124 .770

(0.76) .309 .455 .105 .171 .179 .780

(0.82) .512 .428 .317 .134 .770

(0.70) .425 .327 .141 .730

(0.81) .290 .212 .820

(0.60) .211 .600

(0.50) .620

Note: Mon = Monitoring; Dec = Decision making; Lea = Learning from mistakes; Min = Mindful awareness; Per = Perseverance; Cre = Creativity; Sel = Self-evaluation

326


Table 5 Concurrent-Related Validity: Pearson Correlation Coefficients Of The Ssrq Components With Other Measures Of psychological Well-Being. (Prob. > /r/ under H0: Rho = 0)

Total AFM SWLS MHC-SF MAAS GSE GHQ

Mon

Dec

Lea

Min

Per

Cre

Sel

SSRQ

.474** .460** .440** .361** .420** -.297**

.588** .417** .400** .488** .429** -.378**

.556** .429** .460** .473** .431** -.422**

.481** .393** .380** .618** .350** -.353**

.558** .475** .430** .402** .491** -.311**

.413** .321** .410** .175** .540** -.219**

.472** .477** .400** .286** .412** -.320**

.697** .576** .560** .567** .588** -.458**

Note: Mon = Monitoring; Dec = Decision making; Lea = Learning from mistakes; Min = Mindful awareness; Per = Perseverance;

Cre = Creativity; Sel = Self-evaluation; AFM2 – PNB = Affectometer 2 – Positive-negative affect balance; SWLS = Satisfaction with life scale; MHC-SF = Mental Health Continuum – Short Form; MAAS = Mindful Attention Awareness Scale; GSE = General Self-Efficacy Scale; GHQ = General Health Questionnaire, Total; ** Correlation significant at 0.01 level. these subscales” (p 668). The reliability of the total scale was supported by high internal consistency of extracted factors for factors 1 to 5, yielding alphas of 0.83, 0.76, 0.82, 0.70, and 0.81, while factors 6 and 7 yielded lower alphas of 0.60, and 0.50 respectively. Criterion-related validity. Both the total scale and extracted factors showed a high degree of criterion-related validity through strong correlations with various measures of psychological well-being. Correlations between the SSRQ subscales and various measures of psychological well-being are summarised in Table 5. All individual factors as well as the SSRQ total score showed significant positive correlations with all the indices of psychological well-being used in this study, and correlated negatively with symptoms of psychopathology, as measured with the GHQ. In order to determine the contribution that the components of SR as measured with the SSRQ made to participants’ mental health, a hierarchical regression analysis was done with participants’ SSRQ component scores as predictors, and their MHC scores as dependent variables. The results of the final model fit are provided in Table 6. It is interesting to note that the Perseverance factor did not feature prominently in the results of this analysis, and that Mindful awareness made only a small non-significant impact on participants’ MHC total score. These results are somewhat difficult

to interpret, as both these factors can conceptually be regarded as strong components of the SR process.

Discussion and Conclusion In contrast to the findings of Carey et al. (2004), the SSRQ in this study produced seven factors, as originally proposed by Miller and Brown (1991). These factors, however, consisted of different item-combinations than those proposed for the original SRQ, and were therefore not identical in content to the ‘logically derived’ dimensions of effective SR as proposed by Miller and Brown (1991). The relationship between SR and various forms of behaviour is proposed as an avenue for further investigation. This should include various contexts, like sport, health, education etc. More important, though, is the validation of this scale in different cultural groups in the South African context. Research investigating this topic is currently underway. It is felt that this shorter form of the SRQ will be ideally suited to large epidemiological studies as it decreases respondent burden, without sacrificing any of its original power of assessing the different components of the SR process. The SSRQ in this study proved to be internally consistent, and correlated strongly with a number of measures of psychological well-being, as expected. The potential utility of the SSRQ among a young, white student population in the South African

Table 6 Summary of Stepwise Regression with MHC – Total scores as Dependent Variable Variable

Step

Lea Cre Mon Sel Dec Min

1 2 3 4 5 6

Multiple R .46 .52 .55 .57 .58 .58

Multiple R-square .22 .28 .31 .32 .33 .33

R-Square change .22 .06 .03 .02 .01 .00

F – to entr/rem 104.08 32.95 16.13 8.64 5.23 1.15

P-level 0.000 0.000 0.000 0.003 0.022 0.284

Variables included 1 2 3 4 5

Note: Mon = Monitoring; Dec = Decision making; Lea = Learning from mistakes; Min = Mindful awareness; Per = Perseverance; Cre = Creativity; Sel = Self-evaluation

Journal of Psychology in Africa 2009 19(3); 321-328 context thus appears to be strong. Factor analysis also gave strong empirical support for the existence of a seven-factor solution representing separate dimensions for the SR-process, which are different from the components originally proposed by Miller and Brown (1991). Further psychometric evaluation is necessary however, especially to confirm the validity and utility of this scale and the conceptual model in other cultural groupings in the South African context. Almost four decades ago, Kanfer (1970) concluded that “... the self control of many behaviours is tantamount to a prerequisite for participation in the social community” (p. 178). This statement predicts that the ability to self-regulate behaviour might be even more essential in collectivistic cultural contexts than in the individualistic contexts in which it has been studied until now.

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