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Brazil lecture questionnaire development
- 1. Musical Self-efficacy Assessment and Measurement Professor Laura Ritchie
- 2. Bandura (1977) Initial investigations • Studies of ophidiophobia fear of snakes • Presented with an experience • Small numbers • Confirm strength of influences Image CC-BY by DVIDSHUB ‘A person’s beliefs about their capabilities to accomplish a specific, criterial task’
- 3. Influential Scales: Schwarzer & Jerusalem (1979) general self-efficacy scale • No validation study • 10 items • Not task-specific Sherer & Maddux (1982) general self-efficacy scale • Validation study • 17 items • For use in academic settings • Still general… Image CC BY by Trending Topics 2019
- 4. Self-efficacy PerformanceLearning Schunk, D. (1996). Self-efficacy for learning and performance. In Annual Meeting of the American Educational Research Association. April, New York. Suggestion of division of TYPES of self-efficacy (Schunk, 1996): Important theoretical shift:
- 5. • Self-efficacy relates to performance in music as it does in other academic contexts • Self-efficacy assessed as a *general* construct, with this single question • “I have fully mastered the requirements for today’s examination.” (p.45) Self-efficacy in music: McCormick & McPherson (2003) Image CC BY by Marco Verch Professional
- 6. • Self-efficacy provided the strongest direct path to performance outcomes • Self-efficacy still assessed as a *general* construct, with a single question for each task. Self-efficacy in music: McPherson & McCormick (2006)
- 7. Guidance on Measuring Self-efficacy: Bong, M. (2006). Asking the right questions. In F. Pajares & T. C. Urdan (Eds) Self-efficacy beliefs of adolescents (pp.287–305). Greenwich, CT: Information Age Publishing. “To accurately assess these beliefs means to envisage and reflect all necessary competencies and situational constraints in the assessment instrument. Context-specific self-efficacy measurement thus requires that important features of tasks and situations that could wield tangible influence on performance outcomes be clearly spelled out in the items (Bandura, 1997). This helps respondents to evaluate their self- efficacy more accurately, which consequently predicts their ensuing thought patterns, emotional reactions, and performance quality with increased precision (Mischel, 1977).”
- 9. Musical Self-efficacy Questionnaires: Learning: Skill acquisition “I am confident that I can successfully learn the music for this performance.” Performing: Skill use / delivery “I am confident that I can give a successful performance.” Ensuring specificity and correspondence with criterial tasks to achieve specific outcomes
- 10. Specificity
- 14. Factor Analysis Scree plots showing factor loadings:
- 18. Adapting questionnaires Understanding: • The task • The setting • Requisite skills • External influences • Physical processes • Cognitive processes Image CC BY by Barb Ignatious
- 19. Self-efficacy for Musical Learning (children’s version) Note: Items 5, 8, and 10 are reverse coded
- 20. Children’s Self-efficacy for Learning and…
- 21. Self-efficacy for Musical Performing (children’s version) Note: Items 3, 4, and 6 are reverse coded.
- 22. Tasks, Measurement, & Understanding: Is performing the same in Sport and Music? Image CC BY-ND by Eric Wong Image CC BY-NC-ND by eye/see
- 23. Music to Sport: very small changes
- 24. Self-efficacy for Performing (Sport) Skills: Correlations with both Self-efficacy for performing in both Sport and Music: • Technical skill • Nuance in interpretation • Managing performance fears • Motivation and drive to excel • Managing stress • Working with others Reliability: The Self-efficacy for Performing in Sport questionnaire was found to be reliable (Cronbach α=.79). Factor analysis: Confirmed the structure of the Self-efficacy for Musical Performing questionnaire found in original validation study.
- 25. Self-efficacy for learning Did not work Not reliable: (Cronbach α=.52) Demonstrating the need for • A full understanding of a task’s requisite skills when choosing to adapt an established questionnaire • Adequate testing to ensure reliability and appropriateness to each setting. Self-efficacy for Learning in Sport questionnaire
- 26. Adapting Self-efficacy Questionnaires Back to the construct… Influenced by: 1. Mastery experiences 2. Vicarious experiences 3. Verbal persuasion 4. Physiological symptoms Image CC BY by James Lee
- 27. Measurement Understanding & Application Image CC BY by ʎɔ.
- 28. Consider suitability: Task Setting Skills External influences Physical processes Cognitive processes Image CC-BY by Eugene Anghel
Notes de l'éditeur
- Historical investigations? Generality? Questions asked? The Schwarzer and Jerusalem scale is specifically designed to measure ‘general’ self-efficacy, which is automatically at odds with the construct. Self-efficacy has to be specific. This scale however has been translated into over 33 languages and is very readily Available. This has meant that many people have used it. There were no specific validation studies. http://userpage.fu-berlin.de/health/engscal.htm Sherer & Maddux general scale 1982 Created for use in academic settings. This was also a general scale, but it had a strong validation study. I used this one for my research.
- Problems with asking the wrong questions: Confusion with other constructs that relate to the self (often self-concept and self-esteem, both of which are global and not task-specific) Lack of accurate understanding in context specific nature of self-efficacy Failure to ensure correspondence between self-efficacy and the task-target “ …efficacy beliefs are multifaceted and contextual, but the level of generality of the efficacy items within a given domain of functioning varies depending on the degree of situational resemblance and the foreseeability of task demands. But regardless of the level of generality, in no case are the efficacy items dissociated from context and level of task demands” (Bandura, 1997, p.50).
- “ …efficacy beliefs are multifaceted and contextual, but the level of generality of the efficacy items within a given domain of functioning varies depending on the degree of situational resemblance and the foreseeability of task demands. But regardless of the level of generality, in no case are the efficacy items dissociated from context and level of task demands” (Bandura, 1997, p.50).
- Introduction was added to make sure the respondents were thinking about the task
- Explain how the items were deleted from the performing scale
- Explain the factor analysis – different methods. and then how the 2 factor solution here represents the reverse-coded items. – which really reinforces that there is one underlying factor being assessed by each scale. Data reduction tests the internal structure of a questionnaire and can confirm external links between a measure and other variables explored (Anderson & Gerbing, 1988). These tests can be used when searching for underlying factors, testing or confirming a hypothesised model that involves various factors and influences, or when confirming a single underlying component where a larger external model of constructs is not involved. Two methods for analysis, Principal Component Analysis (PCA; Pearson 1901; Hotelling 1933; Kelley 1935) and Factor Analysis (FA; Harman 1976; Anderson & Gerbing 1988; Joliffe 2002) both identify the component factors within a measure. However, the methods make different assumptions about the treatment of error within the resulting factors, and these have an impact on the interpretability of the results in different situations; an informed choice must therefore be made in order to employ the appropriate procedure for the data gathered (Shur, 2005). In studies where there is no hypothesized relationship within a fixed model, there is a tendency to use PCA because it is the default method in the commonly used analysis package SPSS v.15-18. PCA aims to extract components that represent the maximum amount of variance within the model, including both discrete variance as represented specifically by that component and error variance. PCA is acceptable if identification of components is the goal, but, because of the inclusion of error in the measurement of components, they should not be interpreted as having theoretical significance (Pedhazur & Schmelkin, 1991) and PCA is not an appropriate method if the components are being extracted to explain the understanding of a construct. Following Pedhazur (1982), when PCA is employed only the initial, un-rotated solution should be considered in analysis; methods of rotating the factors around the axis are known to enhance the clarity of solutions in FA, but as PCA includes error within the components, any rotation will create distorted results. FA can be used in two forms: either as an exploratory tool or for confirmation within a structure. Exploratory Factor Analysis (EFA; Spearman 1904, 1927; Thurstone 1931; Tucker 1955) extracts factors that are discretely responsible for the variance, allowing the researcher to make interpretative judgement about the resulting factors. EFA is preferable to PCA when exploring the structure of cognitive abilities (Carroll, 1993, p.vi) as with discrete variance represented by extracted components, theoretical meaning can be at- tributed to the resulting factors. When performing EFA, the resulting factor loadings can be rotated to make the interpretation of the relationship of the factors more clear. How- ever, the method of rotation for factors needs to be considered; there are either orthogonal or oblique rotation methods which rotates the factors by different angles, depending on whether there are hypothesised correlations between factors. Both methods of rotation are suggested, for example in the validation of the well-established State-Trait Anxiety In- ventory (Gaudry et al., 1975). The Varimax rotation method (Kaiser, 1958) is the default in SPSS and is the most commonly used orthogonal rotation because of the simplicity of its output, but it is not appropriate when a single underlying factor is hypothesized (Gorsuch, 1983), and in this situation the Quartimax method has been suggested as most appropriate (Kaiser, 1958).
- 250 music students completed Self-efficacy for Musical Learning and Self-efficacy for Musical Performing questionnaires for the validation study First step – adapting the Sheerer and Maddux scale to music Then looking at the correlations of learning and performing with various skills. (Ritchie & Williamon, 2008)
- Full list of skills tested against the three iterations of the developing questionnaire: Direct translation from the Sherer et al, Learning, and Performing
- Adapting and validating an established questionnaire to test a task in another domain allows less-researched domains to draw upon experience from disciplines with an established self-efficacy research history. This does not imply that results transfer across domains, but that methods and procedures can be drawn upon in order to further the research within the domains to which the questionnaire adapts successfully.
- This research followed the initial validation of the adult version of the scale to confirm a single underlying factor in the scale adapted for use with primary school children. Exploratory Factor Analysis (EFA) was undertaken using the Maximum Likelihood method, with the Quartimax method of orthogonal rotation employed (as is appropriate when a single underlying factor is hypothesized; Gorsuch 1983). Both the Kaiser rule, where factors with eigenvalues of 1 or greater are considered for retention (Kaiser, 1960), and the examination of the Scree plot (Cattell, 1966) were used to test the initial hypothesis of a single underlying factor for the adapted self-efficacy scale. This research followed the initial validation of the adult version of the scale to confirm a single underlying factor in the scale adapted for use with primary school children. Exploratory Factor Analysis (EFA) was undertaken using the Maximum Likelihood method, with the Quartimax method of orthogonal rotation employed (as is appropriate when a single underlying factor is hypothesized; Gorsuch 1983). Both the Kaiser rule, where factors with eigenvalues of 1 or greater are considered for retention (Kaiser, 1960), and the examination of the Scree plot (Cattell, 1966) were used to test the initial hypothesis of a single underlying factor for the adapted self-efficacy scale. The present study reveals links between self- efficacy for learning in music and other pursuits in music, as well as with extra-musical activities and other psychological measures. The time spent listening to music correlated positively with Self-efficacy for Musical Learning scores. Listening to music can influence instrumental learning (c.f. the group of “best” violinists reported by Ericsson et al. 1993), as students can gain an understanding of an entire piece and hear polished musical interpretations. Listening also engages high-level skills involving musical analytic understanding and the interrelationship of music components. It is unlikely, however, that children who have such limited experiences with making music have yet developed or refined the skills to listen to music in the same way as more experienced musicians (Nielsen, 1999a; North et al., 2000; Ericsson, 2006). The impact of listening on the actual processes of learning an instrument may not be clearly shown at these early stages of learning in music. The correlations to extra-musical activities found in the present study demonstrate the wider relevance of self-efficacy in children’s lives. There were positive correlations with the physical activities of dancing and participating in individual sports. Both of these structured physical activities rely on teacher or coach input for learning, and the pattern of 131 learning takes place through scheduled rehearsal sessions, building to performance goals. In this sense, the processes of learning and performing in dance and individual sports are similar to those involved in music. When students learn a musical instrument, they typically have weekly lessons with a teacher and give a concert or a recital when pieces have been learned. The link between reading and self-efficacy beliefs may be less immediately obvious. However, there are again similarities with fundamental, underlying processes (Gardener, 1983). Reading skills involve processes of verbal decoding and comprehension of both detail and of a complete story. Music learning involves skills to decode musical notation, comprehend phrases, and attribute meaning to the music in preparation for a performance. The correlation of reading for pleasure and children’s perceived self-efficacy could be ex- plained in the similarity of processes between these tasks (Hansen & Bernstorf, 2002). According to Bandura (1986), vicarious experiences are the second strongest influence on people’s perceived self-efficacy beliefs, and the skills students have already mastered in literary interpretation could reinforce their belief in their abilities to learn music.
- The children’s Self-efficacy for Musical Performing questionnaire tested here was shown to have a robust Cronbach alpha, with EFA showing one overriding internal factor. The students showed significant differences in beliefs about their performing capabilities de- pending on their level of engagement with the subject being questioned. All of the students in the study had experience performing in music as part of the National Curriculum in music, which requires that students at Key Stage 2 (i.e. this level) will all perform, com- pose, and appraise music in school lessons (QCA & DEA, 1999). However, those who engaged with specialist musical tuition had noticeably higher self-efficacy for performing scores, which correlated with different aspects within their everyday lives.
- Can self-efficacy beliefs be measured in a similar manner when related tasks share core skills?
- Above: Self-efficacy for Musical Performing and the minimal adaptations to Self-efficacy for Performing in Sport. A sample of 98 university sport science students completed adaptations of each of these to determine whether it was possible to transfer to another context.
- Same process of testing…
- Performance, which is often a public event, is a clearly observable task, making comparison more transparent across domains. Learning is less visible. It is not unique to a specific setting or bound by a set methodology. The learning process is more private, potentially occurs in isolation, and is somewhat more abstract as compared with a scheduled performance. As performance has obvious similarities across the domains of music and sport, one might assume that the learning processes also exhibits similarities. In these domains, learning requires the acquisition and subsequent development of skill into a polished high-level state. This does not, however, necessitate that the processes or means of achieving the learning are the same across domains. It is possible, therefore, that the task of learning may not be comparable, because of hidden differences.