Assessing Reinvestment Tendencies in Athletes: Psychometric Validation of Decision- and Movement-Specific Reinvestment Scales

Abstract

Reinvestment Theory proposes that excessive conscious control over movement execution or decision making can disrupt automaticity and contribute to performance breakdown under pressure. Despite its clinical relevance in sport psychology, culturally validated measures of reinvestment remain limited. This study examined the psychometric properties of adapted versions of the Decision-Specific Reinvestment Scale (DSRS) and Movement-Specific Reinvestment Scale (MSRS) in a sample of athletes and physically active individuals. Participants were 259 undergraduate sport science students (M_age = 20.34 years). Data were analyzed using item discrimination indices, confirmatory factor analyses, convergent validity tests, and internal consistency estimates. Item-level analyses supported the removal of one item from each scale. The resulting DSRS comprised 12 items across two factors (decision reinvestment and decision rumination), and the MSRS comprised nine items across two factors (conscious motor processing and movement self-consciousness). Both scales demonstrated acceptable to good reliability and evidence of factorial and convergent validity. These findings support the use of the adapted DSRS and MSRS as clinically informative tools for assessing vulnerability to performance disruptions under pressure in sport and performance settings.

Keywords

reinvestment theory decision making motor control psychometric validation sport psychology assessment

Introduction

High levels of motivation and commitment are fundamental to athletic success; however, optimal performance is not guaranteed in demanding competitive environments. Under conditions of heightened pressure, athletes frequently experience performance decrements despite strong intentions to succeed. Such breakdowns have been linked to evaluative audiences (Wang et al., 2004), reward contingencies (Jackson & Beilock, 2007; Lee & Grafton, 2015), and the cognitive load imposed by competitive expectations (Kinrade et al., 2015). These conditions often prompt athletes to increase conscious monitoring of their actions in an attempt to maintain control, a response that may paradoxically undermine performance effectiveness.

Empirical evidence indicates that directing attention toward the mechanics of movement execution can disrupt performance, particularly in skilled performers whose actions are typically governed by automatic control processes (Christensen et al., 2016; Englert & Oudejans, 2014; Gorgulu & Gokcek, 2021). This phenomenon has been examined through several theoretical frameworks. Early models, such as Fitts and Posner (1967) stage theory, suggested that skill acquisition involves a progression from effortful, conscious control to automatic execution, with pressure potentially triggering regression to earlier, less efficient control modes. Building on Deikman’s (1969) notion of attentional reinvestment, Masters (1992) formally conceptualized this process as reinvestment, describing how individuals under pressure may revert to consciously controlling actions that would otherwise run automatically.

Reinvestment Theory posits that conscious control of movement or decision can interfere with the fluid execution of well-learned skills (Masters, 1992; Masters & Maxwell, 2008). According to the theory, performance pressure increases the likelihood that athletes will attend to step-by-step processes, retrieving explicit knowledge about how to perform the task. This inward attentional shift distrupts automaticity, increasing susceptibility to errors and performance instability (Bartura et al., 2024; Beilock & Carr, 2001; Gorgulu et al., 2019; Gray et al., 2017; Masters & Maxwell, 2008). From a clinical sport psychology perspective, reinvestment is particularly relevant because it represents a dispositional vulnerability that may predispose athletes to choking under pressure, heightened performance anxiety, and maladaptive coping responses accordingly.

Importantly, Reinvestment Theory emphasizes individual differences in the tendency to consciously control performance. Masters et al. (1993) initially proposed reinvestment as a personality characteristic, leading to the development of the original Reinvestment Scale. Although widely, used, this measure was later criticized for limited construct specificity and indirect assessment of the reinvestment process (Jackson et al., 2006). In response, more refined instruments were developed to capture domain-specific manifestations of reinvesment: the Movement-Specific Reinvestment Scale (MSRS; Masters et al., 2005) and the Decision-Specific Reinvestment Scale (DSRS; Kinrade, Jackson, Ashford, & Bishop, 2010).

The MSRS assesses individuals’ propensity to consciously monitor and control their movements and comprises two dimensions: conscious motor processing, reflecting deliberate attention to movement mechanics, and movement self-consciousness, reflecting concern about how movement appear to others (Masters & Maxwell, 2008). The DSRS, in turn, focuses on cognitive processes underlying decision-making under pressure and includes decision reinvestment, referring to conscious monitoring of decisions, and decision rumination, capturing repetitive negative evaluation of prior decisions (Kinrade, Jackson, & Ashford, 2010). Both scales have demonstrated robust psychometric properties and have been widely applied in performance, clinical, and developmental contexts.

Cross-cultural validation studies support the structural validty of the MSRS and DSRS across diverse populations, including adaptations of the DSRS in French, German, and Iranian samples (Laborde et al., 2014, 2015; Soleimanirad et al., 2017), and the MSRS in Dutch, German, French, Chinese, Japanese, Singaporean, and Iranian contexts (Kawabata & Imanaka, 2021; Kleynen et al., 2013; Laborde et al., 2014, 2015; Wong et al., 2008). Extensions of this work to child and adolescent samples further highlight the developmental relevance of reinvestment tendencies (Ling et al., 2016, 2019). Despite this extensive international literature, research grounded in Reinvestment Theory remains absent within the Turkish sport psychology context, largely due to the lack of validated assessment tools. The absence of culturally validated measures represents a significant barrier to both research and applied practice. Reinvestment tendencies have been linked to clinically relevant outcomes in athletes, including stress responses, coping effectiveness, performance anxiety, and well-being (Kinrade, Jackson, & Ashford, 2010, 2015). Without valid and reliable instruments, clinicians and researchers are limited in their ability to identify athletes who may be vulnerable to maladaptive attentional control strategies under pressure.

Accordingly, the aim of the present study was to adapt the DSRS and the MSRS for use in a new cultural context and to evaluate their psychometric properties in a sample of university athletes. By establishing the validity and reliability of these instruments, this study aims to extend Reinvestment Theory cross-culturally and provide clinicians and researchers with psychometrically sound tools for assessing reinvestment tendencies in performance settings. Such tools are essential for advancing evidence-based assessment, informing targeted interventions, and facilitating cross-cultural comparisons in clinical sport psychology research.

Methods

Participants and Sample Size Determination

The present study examined the psychometric properties of two self-report instruments assessing reinvestment tendencies: the Decision-Specific Reinvestment Scale (DSRS; 13 items) and the Movement-Specific Reinvestment Scale (MSRS; 10 items), yielding a total of 23 items. Sample size adequacy was determined using complementary psychometric and statistical criteria. Consistent with recommendations for scale validation research, a minimum ratio of 10 participants per item was adopted, indicating a target sample size of at least 230 participants to support reliable factor analytic procedures (Boateng et al., 2018). In addition, an a priori power analysis was conducted using G*Power (Faul et al., 2007) to ensure sufficient power for examining associations between decision- and movement-specific reinvestment. Assuming a medium effect size (.30), an alpha level of .05, and statistical power of .95 (1-β), the required sample size was 134.

Sample Characteristics

Participants were 265 student-athletes enrolled in Faculty of Sport Sciences at a large public university in Türkiye. Six participants were excluded for being under 18 years of age, resulting in a final sample of 259 participants (167 men, 92 women). The mean age was 20.34 years (SD = 3.51). Participants represented 15 sport disciplines, including team and individual sports, and reported an average of 7.69 years of sport participation (SD = 4.74). This heterogeneous athletic sample enhances the clinical and applied relevance of the findings across diverse performance contexts.

Measures

Decision-Specific Reinvestment Scale (DSRS)

This scale measures the reinvestment that occurs during individuals’ decision-making processes. Developed by Kinrade, Jackson, Ashford, & Bishop (2010), it consists of 13 items and two subdimensions: ‘decision reinvestment’ (DRE) and ‘decision rumination’ (DRU). It uses a five-point Likert-type scale, scored from 0 (strongly disagree) to 4 (strongly agree). In addition to calculating separate scores for each subdimension, a total score for the overall scale can also be computed, with higher scores indicating greater decision-specific reinvestment. Cronbach’s alpha internal consistency coefficients were reported as 0.89 and 0.91 for the decision reinvestment and decision rumination subdimensions, respectively (Kinrade, Jackson, Ashford, & Bishop, 2010).

Movement Specific Reinvestment Scale (MSRS)

MSRS assesses the extent to which individuals consciously monitor and control their own movements and actions. Developed by Masters et al. (2005), it consists of ten items and two subdimensions: ‘movement self-consciousness’ (MSC) and ‘conscious motor processing’ (CMP). It is a 6-point Likert-type instrument, scored from 1 (strongly disagree) to 6 (strongly agree). Subdimension scores can be calculated separately, and an overall score representing overall movement-specific reinvestment can also be computed, with higher scores indicating greater reinvestment. Internal consistency coefficients were reported to range from 0.70 to 0.78 for the movement self-consciousness sub-dimension, and from 0.65 to 0.71 for the conscious motor processing subdimension. Test–retest reliability coefficients were 0.67 and 0.76, respectively (Masters et al., 2005).

Procedure

Approval to adapt the DSRS and MSRS was obtained from the original scale developers prior to data collection. Scale adaptation followed established back-translation procedures (Behling & Law, 2000). Three bilingual experts with backgrounds in sports sciences, sport psychology and psychology, independently translated the original English items into Turkish. The research team synthesized these translations into a preliminary version, which was subsequently back-translated into English by two independent bilingual experts. The original and back-translated versions were compared to evaluate semantic and conceptual equivalence, and feedback from the original scale developers was incorporated before finalizing the Turkish versions.

Data were collected using an online survey platform. Eligible participants were athletes aged 18 years or older residing in Istanbul. Recruitment occurred via direct contact with athletes, dissemination through sports clubs and coaches, and snowball sampling within athletic networks. Prior to participation, all individuals reviewed an informed consent statement and provided voluntary consent electronically. Participation was anonymous, and no incentives were offered.

Data Analysis

Psychometric evaluation proceeded in several stages. Factorial validity was assessed using confirmatory factor analysis (CFA). Model fit was evaluated using the Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), and ratio of the chi-square value to the degrees of freedom (χ²/df). For acceptable model fit, CFI and TLI values are expected to be close to .95, RMSEA and SRMR values should be below .08 (Hu & Bentler, 1999; Kline, 2016). A χ²/df ratio of less than 2 is generally considered to be indicative of a good model fit. However, some researchers suggest that values between 2 and 5 may also be regarded as acceptable, depending on the context (Tabachnick & Fidell, 2013; Wheaton et al., 1977). Because multivariate normality assumptions were violated, CFA models were estimated using diagonally weighted least squares (DWLS) with robust fit indices (Gana & Broc, 2019).

Item-level analyses examined inter-item correlations, corrected item–total correlations, and changes in internal consistency following item deletion. Items were expected to demonstrate positive within-factor correlations and corrected item–total correlations of at least .30 to be considered adequately discriminative (Coaley, 2010).

Convergent validity was examined by testing associations between DSRS and MSRS scores, consistent with theoretical and empirical links between decision- and movement-specific reinvestment (Laborde et al., 2014). Reliability was evaluated using Cronbach’s alpha and composite reliability (CR), with values ≥.70 indicating acceptable internal consistency (Hair et al., 2014). The CR coefficient was used because it provides evidence of construct reliability based on factor loadings obtained after CFA. Cronbach’s alpha was also examined, as it has been widely used in the adaptation of the DSRS and MSRS to different languages and cultures, providing evidence of the scales’ internal consistency reliability. Analyses were conducted using IBM SPSS Statistics 27 (IBM Corp, 2020) and the lavaan package (Rosseel, 2012) in R (R Core Team, 2024).

Results

Validity and Reliability Results of the DSRS

Factorial Validity

To examine the factorial validity of the DSRS, CFA was conducted, and model fit indices (Table 1) along with item factor loadings (Table 2) were evaluated. The findings indicated that the original 13-item, two-factor structure of the DSRS did not demonstrate acceptable model fit (Model 1: RMSEA = .083, SRMR = .087). To improve model fit, modification indices were examined. The results showed that Item D2, which was originally loaded on the DRE subdimension, had a high cross-loading on the DRU subdimension and also showed substantial modification indices with other items in the DRU subdimension. Thus, item D2 appeared to belong to a different subdimension, which was inconsistent with the DSRS’s two-factor theoretical structure. Based on these findings, Item D2 was removed from the model, and an alternative model was tested without compromising the theoretical two-factor structure of the DSRS. The revised 12-item, two-factor structure was then re-tested using CFA, and the model demonstrated acceptable fit indices (Model 2). Hierarchical CFA model (Model 3) also demonstrated satisfactory fit, providing evidence that two subdimensions coherently reflect a higher-order reinvestment construct.

Table 1.

Fit Indices for the Confirmatory Factor Analysis Models of the DSRS and the MSRS

	χ2	df	χ2/df	CFI	TLI	SRMR	RMSEA
DSRS
Model 1	176.919	64	2.76	.948	.937	.087	.083
Model 2	105.536	53	1.99	.974	.967	.073	.062
Model 3	105.536	52	2.03	.973	.966	.073	.063
MSRS
Model 4	130.978	34	3.85	.925	.900	.096	.105
Model 5	72.315	26	2.78	.956	.940	.077	.083
Model 6	72.315	25	2.89	.955	.936	.077	.082

Table 2.

Factor Loadings of the DSRS Confirmatory Factor Analysis Model

		Model 1	Model 2	Model 3
Decision reinvestment (DRE)	D1. I’m always trying to figure out how I make decisions	.47	.53	.53
	D2. I’m concerned about my style of decision-making	.44	-	-
	D4. I’m constantly examining the reasons for my decisions	.59	.64	.64
	D6. I Sometimes have the feeling that I’m observing my decisionmaking process	.69	.74	.74
	D9. I am alert to changes in how much thought I give to my decisions	.67	.72	.72
	D10. I’m aware of the way my mind works when I make a decision	.35	.43	.42
Decision rumination (DRU)	D3. I remember poor decisions I make for a long time afterwards	.62	.62	.62
	D5. I get ‘‘worked up’’ just thinking about poor decisions I have made in the past	.72	.71	.71
	D7. I often find myself thinking over and over about poor decisions that I have made in the past	.80	.80	.80
	D8. I think about better decisions I could have made long after the event has happened	.70	.70	.70
	D11. I rarely forget the times when I have made a bad decision, even about the minor things	.76	.76	.76
	D12. When I am reminded about poor decisions I have made in the past, I feel as if they are happening all over again	.70	.70	.70
	D13. I’m concerned about what other people think of the decisions I make	.53	.51	.51
General factor	DRE	-	-	.72
General factor	DRU	-	-	.76

Model 1: 13 items, two-factor correlated CFA, Model 2: 12 items, two-factor correlated CFA, Model 3: 12 items, 2 × 1 hierarchical CFA.

Table 2 shows the factor loadings for the three models examined in the CFA. In Model 1, the factor loadings ranged from .35 to .80; in Model 2, they ranged from .43 to .80. In Model 3, the factor loadings ranged from .42 to .80.

Item Analysis

For the item analysis of the DSRS, the inter-item correlation coefficients were examined first (see Table 3). Item D2 in the DRE subdimension was not significantly correlated with Items D1, D9, and D10 within the same subdimension, whereas it showed significant correlations with all items in the DRU subdimension. In other words, in line with the CFA findings, item D2 loaded on the other subdimension instead of its intended subdimension, contradicting the scale’s theoretical two-factor structure. In contrast, all items within the DRE subdimension, except for Item D2, were positively and significantly correlated with one another (p < .01). In the DRU subdimension, all items were significantly and positively correlated with one another (p < .01).

Table 3.

Inter-Item Correlations of the Decision-Specific Reinvestment Scale

	D1	D2	D4	D6	D9	D10	D3	D5	D7	D8	D11	D12
D1	1
D2	.05	1
D4	.50^**	.19^**	1
D6	.45^**	.21^**	.41^**	1
D9	.29^**	.12	.40^**	.41^**	1
D10	.39^**	−.06	.38^**	.34^**	.38^**	1
D3	.31^**	.21^**	.39^**	.36^**	.34^**	.16^*	1
D5	.22^**	.42^**	.25^**	.37^**	.26^**	.03	.47^**	1
D7	.13^*	.31^**	.21^**	.29^**	.32^**	.07	.48^**	.61^**	1
D8	.23^**	.24^**	.35^**	.38^**	.45^**	.23^**	.41^**	.44^**	.54^**	1
D11	.17^**	.26^**	.24^**	.34^**	.38^**	.09	.39^**	.51^**	.62^**	.46^**	1
D12	.06	.27^**	.13^*	.20^**	.30^**	.00	.35^**	.50^**	.61^**	.41^**	.64^**	1
D13	.06	.44^**	.10	.20^**	.20^**	.10	.25^**	.35^**	.45^**	.28^**	.42^**	.49^**

Decision reinvestment: D1, D2, D4, D6, D9, D10.

Decision rumination: D3, D5, D7, D8, D11, D12, D13. * p < .05 ** p < .01

Table 4 summarizes descriptive statistics and item-level psychometric indices for the DSRS. Item means ranged from 1.73 (D2) to 2.98 (D1), indicating adequate response variability across items. Corrected item-total correlation analyses revealed that Item D2 demonstrated poor discrimination (r = .15), falling below the recommended threshold of .30. In contrast, all remaining items exhibited satisfactory item-total correlations (rs ≥ .41).

Table 4.

Item Analysis of the Decision-Specific Reinvestment Scale

	M	SD	Corrected item total correlation	Cronbach’s alpha if item deleted	Cronbach’s alpha
Decision reinvestment
D1	2.98	1.05	.50	.64	.69
D2	1.73	1.27	.15	.76
D4	2.77	1.02	.58	.62
D6	2.68	1.09	.56	.62
D9	2.61	1.19	.48	.65
D10	2.76	1.05	.41	.67
Decision rumination
D3	2.83	1.19	.51	.85	.86
D5	2.41	1.28	.65	.83
D7	2.23	1.34	.77	.82
D8	2.66	1.18	.58	.85
D11	2.14	1.36	.70	.83
D12	1.96	1.40	.69	.83
D13	1.75	1.33	.50	.86

Consistent with these findings, removal of Item D2 resulted in a meaningful increase in internal consistency for the DRE subscale, with Cronbach’s alpha from .69 to .76. Deleting any other item did not yield further improvements in reliability. Accordingly, Item D2 was excluded from subsequent analyses, resulting in a more psychometrically robust scale structure.

Reliability

In the 13-item version of the scale, the Cronbach’s alpha internal consistency coefficient was .69 for the DRE subdimension and .86 for the DRU subdimension. When one item (D2) was removed from the decision reinvestment subdimension, its Cronbach’s alpha increased to .76. In the 12-item version of the scale, the composite reliability (CR) coefficients were .75 for the DRE subdimension and .86 for the DRU subdimension.

Validity and Reliability Results of the MSRS

Factorial Validity

CFA was conducted to assess the factorial validity of the MSRS, with a focus on model fit indices (Table 3) and item factor loadings (Table 5). Initial results indicated that the 10-item, two-factor structure of the MSRS failed to meet the criteria for acceptable fit (Model 4: RMSEA = .105, SRMR = .096). Modification indices were examined to investigate the theoretical and statistical sources of poor model fit. The results indicated high covariance between item M5 (MSC subdimension) and items within the CMP subdimension. The fact that item M5 showed higher modification indices with items from the other subdimension rather than with items from its own subdimension was inconsistent with the theoretical structure of the MSRS. In other words, M5 behaved as if it belonged to the other subdimension. To preserve the two-factor theoretical structure of the MSRS and improve model fit, item M5 was removed, after which a revised alternative model was examined. The revised 9-item, two-factor structure was subsequently tested via CFA, yielding acceptable fit indices (Model 5). The hierarchical model (Model 6) also demonstrated satisfactory fit, providing evidence that two subdimensions coherently reflect a higher-order movement-specific reinvestment construct. Although RMSEA values for Model 5 and 6 (.083 and .082, respectively) were slightly above the conventional cutoff, all other fit indices met established criteria, supporting the adequacy of the revised nine-item structure and its hierarchical representation. Table 5 presents the factor loadings for the three models examined within the CFA. In Model 4, item factor loadings ranged from .44 to .74. In Models 5 and 6, the factor loadings ranged from .44 to .77.

Table 5.

Factor Loadings of the MSRS Confirmatory Factor Analysis Model

		Model 4	Model 5	Model 6
Conscious motor processing (CMP)	M1. I remember the times when my movements have failed me.	.56	.59	.59
	M3. I reflect about my movement a lot.	.74	.77	.77
	M4. I try to think about my movements when I carry them out.	.72	.72	.72
	M7. I am aware of the way my body works when I am carrying out a movement.	.55	.49	.49
	M9. I try to figure out why my actions failed.	.44	.44	.44
Movement self-consciousness (MSC)	M2. If I see my reflection in a shop window, I will examine my movements.	.65	.69	.69
	M5. I am self-conscious about the way I look when I am moving.	.52	-	-
	M6. I Sometimes have the feeling that I am watching myself move.	.65	.63	.64
	M8. I am concerned about my style of moving.	.46	.54	.54
	M10. I am concerned about what people think about me when I am moving.	.47	.53	.53
General factor	CMP	-	-	.96
General factor	MSC	-	-	.97

Model 4: Ten items, two-factor correlated CFA, Model 5: Nine items, two-factor correlated CFA, Model 6: Nine items, 2 × 1 hierarchical CFA.

Item Analysis

For the item analysis of the MSRS, inter-item correlation coefficients were first examined (see Table 6). All items within the CMP subdimension showed significant positive correlations with one another (p < .01). Within the MSC subdimension, Item M5 did not exhibit a significant correlation with Items M8 and M10. All other items in this subdimension demonstrated significant positive correlations (p < .01). Item M5 in the MSC subdimension does not have a significant relationship with items M8 and M10 in the same subdimension (p > .05). However, it has a significant relationship with all items in the CMP subdimension (rs ranged from .19 to .64). Furthermore, all items in the MSC subdimension, except for item M5, have positive and significant relationships with each other (p < .01). That is, in line with the CFA findings, item M5 loaded on the CMP subdimension instead of the MSC subdimension, contradicting the theoretical structure of the MSRS.

Table 6.

Inter-Item Correlations of the Movement-Specific Reinvestment Scale

	M1	M3	M4	M7	M9	M2	M5	M6	M8	M10
M1	1
M3	.39^**	1
M4	.29^**	.60^**	1
M7	.25^**	.35^**	.46^**	1
M9	.26^**	.28^**	.36^**	.33^**	1
M2	.47^**	.56^**	.44^**	.25^**	.20^**	1
M5	.19^**	.34^**	.44^**	.64^**	.28^**	.28^**	1
M6	.27^**	.47^**	.50^**	.48^**	.20^**	.37^**	.47^**	1
M8	.40^**	.30^**	.27^**	.05	.22^**	.35^**	−.03	.33^**	1
M10	.43^**	.35^**	.26^**	.08	.25^**	.34^**	.08	.24^**	.55^**	1

Conscious motor processing: M1, M3, M4, M7, M9.

Movement self-consciousness: M2, M5, M6, M8, M10. * p < .05 ** p < .01

Table 7 presents the means, standard deviations, corrected item–total correlations and Cronbach’s alpha coefficients obtained when each MSRS item was deleted. Item means ranged from 1.87 (M8) to 3.34 (M5). In terms of corrected item–total correlations, Item M5 had a coefficient of .28, whereas all the other items had coefficients of .41 or higher. Furthermore, removing Item M5 increased the Cronbach’s alpha coefficient of the movement self-consciousness subdimension from .69 to .70. However, removing the other items did not improve reliability.

Table 7.

Item Analysis of the Movement-Specific Reinvestment Scale

	M	SD	Corrected item total correlation	Cronbach’s alpha if item deleted	Cronbach’s alpha
Conscious motor processing
M1	2.83	1.52	.41	.73	.74
M3	2.75	1.55	.58	.66
M4	3.02	1.52	.62	.65
M7	3.32	1.32	.49	.70
M9	3.23	1.53	.43	.72
Movement self-consciousness
M2	2.57	1.57	.50	.61	.69
M5	3.34	1.29	.28	.70
M6	2.85	1.59	.52	.60
M8	1.87	1.56	.46	.63
M10	2.12	1.61	.45	.64

Reliability

In the 10-item version of the scale, the Cronbach’s alpha internal consistency coefficient was .74 for the CMP subdimension and .69 for the MSC subdimension. Removing one item (M5) from the MSC subdimension increased its Cronbach’s alpha to .70. In the nine-item version of the scale, the CR coefficients were .74 and .69, respectively.

Convergent Validity of the DSRS and MSRS

Table 8 presents the correlation coefficients between the subdimensions and total scores of the DSRS and the MSRS. The analysis revealed significant positive correlations between the subdimensions and total scores of both scales (p < .01).

Table 8.

Relationships Between Decision-Specific and Movement-Specific Reinvestment

	1.1	1.2	1.3	2.1	2.2	2.3
1. DSRS
1.1. DRE	1
1.2. DRU	.43^**	1
1.3. Composite score	.74^**	.92^**	1
2. MSRS
2.1. CMP	.53^**	.55^**	.64^**	1
2.2. MSC	.22^**	.62^**	.56^**	.65^**	1
2.3. Composite score	.43^**	.65^**	.66^**	.92^**	.89^**	1

**p < .01.

DSRS: decision-specific reinvestment scale; DRE: decision reinvestment; DRU: decision rumination; MSRS: movement-specific reinvestment scale; CMP: conscious motor processing; MSC: movement self-consciousness.

Discussion

The purpose of the present study was to adapt the DSRS and the MSRS for use in a Turkish sport context and to evaluate their psychometric properties within a sample of university-level athletes. Grounded in Reinvestment Theory (Masters, 1992; Masters & Maxwell, 2008), these instruments are widely used to assess dispositional tendencies toward conscious control and rumination under performance pressure. Overall, the findings provide support for the factorial validity, internal consistency, and convergent validity of revised Turkish versions of both scales, with minor item reductions necessary to achieve acceptable psychometric performance.

Decision-Specific Reinvestment Scale

Psychometric evaluation of the DSRS indicated that one item (D2) did not function adequately within the Turkish context. This item demonstrated weak item–total correlations, reduced internal consistency, and contributed to poor model fit in CFA. The primary reason for this issue was that item D2 showed stronger associations with items from the other subdimension (DRU) rather than with those in its intended subdimension (DRE). Accordingly, in the CFA, D2 demonstrated high modification indices with items from the DRU subdimension. Similarly, in the item-level and reliability analyses, D2 was more strongly related to items from the DRU subdimension than to those within its own. In other words, item D2 appeared to load on a different factor, which both undermined the theoretical structure of the DSRS and contributed to poorer model fit. When the item contents are examined, this pattern is not entirely unexpected. In the inter-item correlations, item D2 (I’m concerned about my style of decision-making) was found to be most strongly associated with items D5 (I get ‘‘worked up’’ just thinking about poor decisions I have made in the past) and D13 (I’m concerned about what other people think of the decisions I make). A review of these three items indicates that they all focus on emotional aspects of reinvestment, such as feelings of concern and anger. Therefore, it is not unexpected that D2 would show strong associations with D5 and D13, even though they belong to a different subdimension. However, in order to preserve the discrimination, factorial validity, and reliability of the subdimensions, it was deemed more appropriate to remove item D2, given its high association with a different subdimension. Removal of this item resulted in a 12-item, two-factor structure—decision reinvestment and decision rumination—with acceptable model fit and reliability indices. Importantly, this revised structure aligns with the theoretical distinction proposed by Kinrade, Jackson, Ashford, & Bishop, (2010), separating conscious monitoring of decision processes from maladaptive rumination about past decisions.

The need to modify the original scale structure is consistent with prior cross-cultural adaptation studies. For example, French and German validations of the DSRS required correlated error terms to achieve acceptable model fit (Laborde et al., 2014, 2015), whereas the Iranian adaptation reported acceptable fit without item removal (Soleimanirad et al., 2017). The present findings extend this literature by demonstrating that, within the Turkish cultural and linguistic context, item removal—rather than post hoc error correlations—was the most parsimonious solution. From a clinical assessment perspective, this approach enhances interpretability and reduces measurement noise, thereby strengthening the scale’s applicability in applied sport psychology settings.

The revised DSRS demonstrated good internal consistency across both subscales, comparable to those reported in international samples. These findings suggest that decision-related reinvestment and rumination can be reliably assessed in Turkish athletes, supporting the use of the DSRS for identifying athletes who may be vulnerable to performance decrements under pressure due to maladaptive decision-related cognitive processes.

Movement-Specific Reinvestment Scale

Similar to the DSRS, psychometric analyses of the MSRS indicated that one item (M5) performed poorly in the Turkish sample. This item showed weak associations with other items in the MSC subdimension and detracted from both internal consistency and model fit. A primary concern regarding reliability and validity was that item M5 showed weak and non-significant associations with items in its intended subdimension (MSC), while demonstrating relatively strong associations with several items from the other subdimension (CMP). In particular, M5 (I am self-conscious about the way I look when I am moving) was found to be highly correlated with M4 (I try to think about my movements when I carry them out) and M7 (I am aware of the way my body works when I am carrying out a movement), both of which belong to the other subdimension (CMP). An examination of the item content suggests a shared conceptual emphasis on movement awareness and conscious attention to one’s own movements. Therefore, it may be considered unsurprising that item M5 was more strongly related to these items than to those in its original subdimension. However, to ensure a clear distinction between subdimensions, the removal of item M5 from the scale was deemed necessary. This decision was expected to ensure that items were more strongly associated with their respective subdimensions, thereby improving factorial validity and reliability. Removal of this item yielded a nine-item, two-factor structure—conscious motor processing and movement self-consciousness—with satisfactory fit indices and reliability estimates. Thus, removing item M5 from the MSRS resulted in a more consistent scale for the Turkish sample, both theoretically and statistically.

Cross-cultural validation studies of the MSRS have frequently reported the need for model modifications, particularly through correlated error terms among conceptually overlapping items (Laborde et al., 2014, 2015; Ling et al., 2016, 2019). Notably, the present study achieved acceptable model fit without introducing correlated residuals, instead opting for item removal based on clear psychometric criteria. This decision enhances clinical usability by preserving a cleaner factor structure and reducing potential ambiguity in score interpretation.

The internal consistency coefficients observed in the present study were comparable to those reported in adult samples across Japanese, Singaporean, Dutch, French, and German contexts (Kawabata & Imanaka, 2021; Kleynen et al., 2013; Laborde et al., 2014, 2015). Consistent with previous findings, reliability estimates for child samples tend to be lower (Ling et al., 2016, 2019), underscoring the importance of age-specific validation. Accordingly, further psychometric evaluation of the Turkish MSRS is recommended before its use with youth or adolescent athletes.

Convergent Validity and Theoretical Implications

Consistent with Reinvestment Theory, significant positive associations were observed between decision-specific and movement-specific reinvestment at both subscale and total-score levels. This pattern mirrors findings reported in previous validation studies (Laborde et al., 2014, 2015) and provides further support for the theoretical proposition that individuals who are prone to consciously monitoring and controlling their movements under pressure are also more likely to engage in decision-related reflection, self-evaluation, and rumination. These findings reinforce the conceptualization of reinvestment as a multidimensional construct that encompasses both motor execution and higher-order cognitive processes. In high-pressure environments, such tendencies may disrupt the automaticity of well-learned skills, thereby increasing the likelihood of performance breakdown. Empirical evidence from experimental sport settings supports this mechanism. For instance, research examining ironic and overcompensation effects in a pressured tennis serving task demonstrated that heightened self-focus and conscious control can alter motor behaviour in maladaptive ways, leading to performance errors under stress (Gorgulu, 2019). Similarly, experimental work in baseball has shown that directing performers’ attention toward movement processes under pressure can impair skilled performance by interfering with automatic control mechanisms (Gray et al., 2017). Taken together these findings align with the present correlational pattern, suggesting that reinvestment-related tendencies may constitute a shared cognitive vulnerability factor underlying performance disruptions, particularly in contexts characterized by evaluative pressure and heightened self-awareness.

From a clinical and applied sport psychology perspective, these interrelations underscore the importance of addressing both movement-specific and decision-related reinvestment tendencies in interventions. Targeting only technical overcontrol without considering athletes’ decision-related rumination or self-regulatory cognitions may be insufficient. Instead, integrative approaches that promote automaticity, attentional flexibility, and adaptive coping under pressure may be more effective in reducing performance vulnerability in high-stakes environments.

Clinical and Applied Implications

The availability of psychometrically sound Turkish versions of the DSRS and MSRS represents an important advancement for clinical sport psychology research and practice. These instruments can be used by practitioners to identify athletes who may be at heightened risk of performance breakdowns under pressure, particularly in evaluative or high-stakes competitive contexts. In applied settings, reinvestment profiles may inform individualized interventions targeting attentional control, decision-making under stress, and the reduction of maladaptive self-focus—such as implicit motor learning strategies, pre-performance routines, and acceptance-based approaches.

Moreover, validated reinvestment measures enable clinicians and researchers to monitor cognitive tendencies that may interact with anxiety, perfectionism, burnout, and well-being—constructs of central interest in clinical sport psychology. The present adaptations therefore facilitate both assessment-driven intervention planning and cross-cultural comparative research.

Limitations and Future Directions

Several limitations warrant consideration. First, reliability evidence was limited to internal consistency; future studies should examine test–retest reliability to establish temporal stability. Second, the cross-sectional design precludes causal inference regarding the role of reinvestment in performance outcomes. Longitudinal and experimental designs are needed to clarify how reinvestment tendencies interact with competitive stress over time. Third, although prior research has linked reinvestment to objective performance decrements under pressure (e.g., Kinrade, Jackson, & Ashford, 2010), the present study did not include behavioral or coach-rated performance measures. Incorporating such criteria would strengthen the clinical validity of the scales.

Conclusion

In summary, the present study provides evidence that revised Turkish versions of the DSRS (12 items) and MSRS (9 items) are valid and reliable tools for assessing decision- and movement-specific reinvestment in adult athletes. These adaptations extend the cross-cultural generalizability of Reinvestment Theory and offer clinically useful instruments for research and applied practice within sport psychology. Their use may enhance the assessment of pressure-related performance vulnerabilities and support more targeted psychological interventions in competitive sport settings.

Footnotes

ORCID iDs

Osman Urfa

Duygu Karadağ

Recep Görgülü

Ethical Considerations

Ethical approval was granted by the Institutional Ethics Committee of Bursa Uludag University (25.04.2025, Decision no. 10).

Consent to Participate

Prior to participation, all individuals reviewed an informed consent statement and provided voluntary consent electronically.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Data Availability Statement

Data will be available upon request.

Author Biographies

Dr. Osman Urfa is a school counsellor in the Counselling Psychology Department at the Ministry of National Education of the Republic of Turkey. He received his MSc and PhD degrees in Sport and Exercise Psychology from Marmara University. He also completed master's degrees in Measurement and Data Analytics at Anadolu University and Counseling Psychology at Mehmet Akif Ersoy University. His scholarly interests include sports psychology, counselling psychology, and psychometrics. He has a particular focus on the mental health and performance of athletes, fans and students, and on cultural and cross-cultural applications in psychometric research. Dr Urfa is also certified in Rational Emotive Behavior Therapy by the Albert Ellis Institute and in Motivational Interviewing by the Motivational Interviewing Network of Trainers. He incorporates these approaches into his scientific research and professional practice.

Dr. Duygu Karadağ works at the Faculty of Sport Sciences at Haliç University. She completed her master’s and PhD degrees in the Department of Physical Education and Sports at Marmara University. Her doctoral research focused on the determinants and outcomes of dual career competency in adolescent athletes. Her research interests lie in Sport and Exercise Psychology, including dual careers, athletic career development, career transitions, and career termination in sport.

Dr. Recep Görgülü is a full professor of sport and performance psychology with extensive academic and applied experience in both Türkiye, Romania, Germany and the United Kingdom. He holds MSc and PhD degrees in Applied Sport and Exercise Psychology from Bangor University, UK. During his MSc studies he was supervised by Senior Lecturer Dr. Calum Arthur, and during his PhD he was supervised by Prof. Tim Woodman and Senior Lecturer Dr. Andrew Cooke. Dr. Görgülü is currently a faculty member at Bursa Uludağ University and the founder of the Psychology of Elite Performance Laboratory (PePLaB). His research program examines the attentional processes underlying “choking” under pressure, with a particular focus on ironic processes of mental control in performance settings. His work investigates the causes of ironic errors, how prior expectations influence skill failure and susceptibility to such errors, and the effectiveness of perceptual training approaches designed to help individuals thrive under pressure. Through his academic work, he aims to advance scientific understanding of elite performance and dedicates his achievements to Pırıl Görgülü.

Dr. Noel Kinrade is a senior lecturer in Sport and Exercise Psychology at Nottingham Trent University. He is module leader for the Level 6 module Performance, Skill and Expertise, as well as contributing to other modules across the curriculum. He is also the course leader for the BSc Coaching and Sport Science degree. In addition, Dr Kinrade actively researches a range of issues related to skill failure under pressure (e.g. choking, falls in elderly/clinical populations) and factors influencing decision making (e.g. individual differences, bias, training).

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