Movement imagery ability: development and assessment of a revised version of the vividness of movement imagery questionnaire.
01 Apr 2008-Journal of Sport & Exercise Psychology (J Sport Exerc Psychol)-Vol. 30, Iss: 2, pp 200-221
TL;DR: The results of the 3 studies provide preliminary support for the revised VMIQ-2 as a psychometrically valid questionnaire.
Abstract: The purpose of this research was to amend the Vividness of Movement Imagery Questionnaire (VMIQ; Isaac, Marks, & Russell, 1986) in line with contemporary imagery modality and perspective conceptualizations, and to test the validity of the amended questionnaire (i.e., the VMIQ-2). Study 1 had 351 athletes complete the 3-factor (internal visual imagery, external visual imagery, and kinesthetic imagery) 24-item VMIQ-2. Following single-factor confirmatory factor analyses and item deletion, a 12-item version was subject to correlated traits / correlated uniqueness (CTCU) analysis. An acceptable fit was revealed. Study 2 used a different sample of 355 athletes. The CTCU analysis confirmed the factorial validity of the 12-item VMIQ-2. In Study 3, the concurrent and construct validity of the VMIQ-2 was supported. Taken together, the results of the 3 studies provide preliminary support for the revised VMIQ-2 as a psychometrically valid questionnaire.
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TL;DR: The authors propose observation-based approaches to offer more valid and effective techniques in sport psychology and motor control by offering effective routes to access and reinforce neural networks for skilled performance.
Abstract: Imagery and observation are multicomponential, involving individual difference characteristics that modify the processes. The authors propose that both imagery and observation function by offering effective routes to access and reinforce neural networks for skilled performance. The neural isomorphism with overt behaviors offers a tempting mechanism to explain the beneficial outcomes of the 2 processes. However, several limitations related to imagery indicate the possibility that imagery may not be as efficacious as the literature would indicate. The authors propose observation-based approaches to offer more valid and effective techniques in sport psychology and motor control.
219 citations
TL;DR: This research validated and extended the Movement Imagery Questionnaire- Revised (MIQ-R) and demonstrated the MIQ-3's predictive validity revealing the relationships between imagery ability and observational learning use.
Abstract: This research validated and extended the Movement Imagery Questionnaire- Revised (MIQ-R; Hall & Martin, 1997). Study 1 (N = 400) examined the MIQ-R's factor structure via multitrait-multimethod confirmatory factor analysis. The questionnaire was then modified in Study 2 (N = 370) to separately assess the ease of imaging external visual imagery and internal visual imagery, as well as kinesthetic imagery (termed the Movement Imagery Questionnaire-3; MIQ-3). Both Studies 1 and 2 found that a correlated-traits correlated-uniqueness model provided the best fit to the data, while displaying gender invariance and no significant differences in latent mean scores across gender. Study 3 (N = 97) demonstrated the MIQ-3's predictive validity revealing the relationships between imagery ability and observational learning use. Findings highlight the method effects that occur by assessing each type of imagery ability using the same four movements and demonstrate that better imagers report greater use of observational learning.
173 citations
Cites background or methods or result from "Movement imagery ability: developme..."
...Because external visual imagery, internal visual imagery, and kinesthetic imagery are considered separate but related constructs (e.g., Roberts et al., 2008), a second hypothesis was that a model comprising three imagery traits as separate factors would provide a better fit to the data than…...
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...Based on the validation of the VMIQ-2 (Roberts et al., 2008), which also Movement Imagery Questionnaire 625 assesses multiple dimensions of imagery ability using the same items, and previous research that demonstrates significant correlations between visual and kinesthetic imagery (e.g., Abma et…...
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...Based on the validation of the VMIQ-2 (Roberts et al., 2008), which also...
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...Validation of the VMIQ-2 also found a CTCU model to be a good fit to the data (Roberts et al., 2008)....
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...Although the VMIQ-2 assesses vividness and the MIQ-3 ease of imaging, it has been suggested that both dimensions reflect the processes of image formation, transformation, and maintenance (Roberts et al., 2008)....
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TL;DR: Current research on motor imagery is hampered by a variety of semantic, conceptual, and methodological issues that prevent cross-fertilization of ideas between cognitive neuroscience and sport psychology, and some potentially fruitful new directions are sketched.
Abstract: One of the most remarkable capacities of the mind is its ability to simulate sensations, actions, and other types of experience. A mental simulation process that has attracted recent attention from cognitive neuroscientists and sport psychologists is motor imagery or the mental rehearsal of actions without engaging in the actual physical movements involved. Research on motor imagery is important in psychology because it provides an empirical window on consciousness and movement planning, rectifies a relative neglect of non-visual types of mental imagery, and has practical implications for skill learning and skilled performance in special populations (e.g., athletes, surgeons). Unfortunately, contemporary research on motor imagery is hampered by a variety of semantic, conceptual, and methodological issues that prevent cross-fertilization of ideas between cognitive neuroscience and sport psychology. In this paper, we review these issues, suggest how they can be resolved, and sketch some potentially fruitful new directions for inter-disciplinary research in motor imagery.
163 citations
TL;DR: This review examines the measurement of motor imagery processes and explains how physiological indices of the autonomic nervous system can measure MI and how these indices may be combined to produce a measure of MI quality called the Motor Imagery Index.
Abstract: This review examines themeasurement of motor imagery (MI) processes. First, self-report measures of MI are evaluated. Next, mental chronometry measures areconsidered. Then, we explain how physiological indices of the autonomic nervous system can measure MI. Finally, we show how theseindices may be combined to produce a measure of MI quality called the Motor Imagery Index. Key Words: motor imagery, mentalimagery, psychometric measures, mental chronometry, autonomic nervous system, electrodermal and cardiac activities
154 citations
Cites background or methods from "Movement imagery ability: developme..."
...In short, the VMIQ measures external other imagery rather than external self-imagery V a problem that, fortunately, has been addressed in the revised version of this scale (31)....
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...As it is difficult to create test items that objectively assess the ability to imagine physical movements, subjective measures of imagery vividness commonly are used in the measurement of MI ability (see (13) and (31) for the Motor Imagery QuestionnaireYRevised version and the Vividness of Movement ImageryYRevised version, respectively)....
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TL;DR: This is the first study to show that the strength of corticospinal activation during imagery, which may be a determinant of the effectiveness of imagery training, is related to imagery ability in the general population, and has implications for clinical programs.
124 citations
Cites background from "Movement imagery ability: developme..."
...However, when the activation patterns of the groups were of which play crucial roles in motor imagery [16, 17]....
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References
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TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Abstract: This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and G...
76,383 citations
"Movement imagery ability: developme..." refers background or methods in this paper
...Further, CFIs and NNFIs of greater than .95, and SRMRs of less than .08 were all taken to indicate a good fit (cf. Hu & Bentler, 1999)....
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...Study 1 had a fairly low chi-square/degrees-of-freedom ratio, and for both studies the fit indices met or exceeded proposed criteria (Hu & Bentler, 1999)....
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...Although the chi-square was significant, and the chi-square/degrees-of-freedom ratio was rather high, the rest of the fit statistics were within recommended limits (cf. Hu & Bentler, 1999)....
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...Significant correlations between the MIQ and MIQ-R have been obtained, for both the visual and kinesthetic subscales, suggesting the MIQ-R to be a suitable revision of the MIQ....
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Book•
01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs
53,113 citations
TL;DR: A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models and two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes.
Abstract: Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model A drawback of existing indexes is that they estimate no known population parameters A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI) FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics An example illustrates the behavior of these indexes under conditions of correct specification and misspecification The new fit indexes perform very well at all sample sizes
21,588 citations
"Movement imagery ability: developme..." refers methods in this paper
...…chi-square statistic (Satorra & Bentler, 1994), the root mean square error of the approximation (RMSEA; Steiger & Lind, 1980), the comparative fit index (CFI; Bentler, 1990), the non-normed fit index (NNFI; Tucker & Lewis, 1973), and the standardized root mean square residual (SRMR; Bentler, 1995)....
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...To assess model fit for both the single factor analyses and the CTCU analysis, the following fit indices were employed: the Satorra–Bentler chi-square statistic (Satorra & Bentler, 1994), the root mean square error of the approximation (RMSEA; Steiger & Lind, 1980), the comparative fit index (CFI; Bentler, 1990), the non-normed fit index (NNFI; Tucker & Lewis, 1973), and the standardized root mean square residual (SRMR; Bentler, 1995)....
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TL;DR: In this paper, the authors examined the change in the goodness-of-fit index (GFI) when cross-group constraints are imposed on a measurement model and found that the change was independent of both model complexity and sample size.
Abstract: Measurement invariance is usually tested using Multigroup Confirmatory Factor Analysis, which examines the change in the goodness-of-fit index (GFI) when cross-group constraints are imposed on a measurement model. Although many studies have examined the properties of GFI as indicators of overall model fit for single-group data, there have been none to date that examine how GFIs change when between-group constraints are added to a measurement model. The lack of a consensus about what constitutes significant GFI differences places limits on measurement invariance testing. We examine 20 GFIs based on the minimum fit function. A simulation under the two-group situation was used to examine changes in the GFIs (ΔGFIs) when invariance constraints were added. Based on the results, we recommend using Δcomparative fit index, ΔGamma hat, and ΔMcDonald's Noncentrality Index to evaluate measurement invariance. These three ΔGFIs are independent of both model complexity and sample size, and are not correlated with the o...
10,597 citations
"Movement imagery ability: developme..." refers result in this paper
...Furthermore, the difference in CFI between the two models was greater than .01, with higher a CFI for the two-factor model, indicating that the two models were not invariant (see Cheung & Rensvold, 2002)....
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TL;DR: The present interpretation of construct validity is not "official" and deals with some areas where the Committee would probably not be unanimous, but the present writers are solely responsible for this attempt to explain the concept and elaborate its implications.
Abstract: Validation of psychological tests has not yet been adequately conceptualized, as the APA Committee on Psychological Tests learned when it undertook (1950-54) to specify what qualities should be investigated before a test is published. In order to make coherent recommendations the Committee found it necessary to distinguish four types of validity, established by different types of research and requiring different interpretation. The chief innovation in the Committee's report was the term construct validity.[2] This idea was first formulated by a subcommittee (Meehl and R. C. Challman) studying how proposed recommendations would apply to projective techniques, and later modified and clarified by the entire Committee (Bordin, Challman, Conrad, Humphreys, Super, and the present writers). The statements agreed upon by the Committee (and by committees of two other associations) were published in the Technical Recommendations (59). The present interpretation of construct validity is not "official" and deals with some areas where the Committee would probably not be unanimous. The present writers are solely responsible for this attempt to explain the concept and elaborate its implications.
9,935 citations
"Movement imagery ability: developme..." refers methods in this paper
...…may reflect the processes of formation, transformation, and maintenance, and it is also likely that the ease of image creation assessed by the MIQ-R requires the same processes.1 Construct validity can be assessed using a variety of methods (cf. Cronbach & Meehl, 1955; Thomas et al., 2005)....
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