The Student Motivation Scale: further testing of an instrument that measures school students' motivation.

Martin, Andrew J.

This study examines the refined Student Motivation Scale applied to a sample of 2561 Australian high school students. The Student Motivation Scale measures six motivation boosters and four motivation guzzlers. Analysis of the data reveals a strong factor structure comprising reliable factors. Students scored relatively higher in self-belief, value of schooling, and learning focus but also relatively higher in anxiety. Senior and junior high school students reflect a more adaptive pattern of motivation than middle high school students--as do girls over boys. Boosters are more strongly (positively) correlated with mathematics and English achievement while guzzlers are more strongly (negatively) associated with literacy and numeracy. Data analysis also reveals ethnicity effects and effects associated with socioeconomic status. Taken together, examination of the data shows that the Student Motivation Scale is psychometrically sound and can be usefully implemented to determine groups of students at risk of disengagement, disinterest, and underachievement.

Introduction

Motivation can be conceptualised as students' energy and drive to learn, work effectively, and achieve to their potential at school and the behaviours that follow from this energy and drive. Motivation plays a large part in students' interest in and enjoyment of school and study. Motivation also underpins their achievement (Martin, 1998, 2001, 2002; Martin & Debus, 1998; Martin & Marsh, 2003; Martin, Marsh, & Debus, 2001a, 2001b, 2003; Meece, Wigfield, & Eccles, 1990; Schunk, 1990).

There are many instruments that measure student motivation. For the most part, however, they tend to reflect motivation that is underpinned by a single theoretical perspective (see for example, the Multidimensional Multiattributional Causality Scale--Lefcourt, Von Baeyer, Ware, & Cox, 1979; the Multidimensional Measure of Children's Perceptions of Control--Connell, 1985; the Self Description Questionnaire--Marsh, 1990; the Motivation Orientation Scale--Nicholls, 1989; the Cognitive Engagement Scale--Miller, Greene, Montalvo, Ravindran, & Nichols, 1996).

From the perspective of a practitioner seeking to enhance students' motivation, instruments reflecting single theoretical perspectives will yield directions for intervention that target only a few (at best) dimensions of motivation. Ideally a test of motivation would be multidimensional, drawing together a number of different theoretical perspectives that better reflect the totality of students' motivational profile in the classroom. The Student Motivation Scale is an instrument that draws together a number of theoretical perspectives and measures aspects of motivation that reflect its multidimensionality.

The Student Motivation Scale has been refined since publication of initial data on its psychometric properties (Martin, 2001) and articulation of its conceptual rationale in a previous issue of this journal (Martin, 2002). An additional subscale has been added, items within subscales have been refined and reduced, and data have been collected on over 2000 additional students across a more representative selection of Australian high schools. This paper presents these data as well as motivation effects related to gender, year level, achievement, literacy, numeracy, socioeconomic status, and ethnicity.

The Student Motivation Wheel and the Student Motivation Scale

There have been numerous theoretical contributions to our understanding of motivation. Among the more influential theories are need achievement theory, self-worth motivation theory, self-efficacy theory, expectancy x value theory, attribution theory, control theory, choice theory, and motivation orientation theory. Taken together, these theories tell us (a) why students do what they do, (b) how they do it, (c) their confidence in being able to do it, (d) their ability to surmount obstacles and challenges before them, and (e) their capacity to pick themselves up after academic setback or hold their ground in the face of study pressures.

Martin (2001, 2002) developed the Student Motivation Wheel that comprises constructs central to these theories and the Student Motivation Scale to measure each facet of the Wheel. The Student Motivation Wheel (and the Student Motivation Scale) separates motivation into factors that reflect enhanced motivation and those that reflect reduced motivation. These are called 'boosters' and 'guzzlers' respectively. Figure 1 presents the Student Motivation Wheel and Table 1 shows theoretical perspectives that underpin the Wheel.

As Figure 1 and Table 1 show and as discussed fully in Martin (2001, 2002), boosters include self-belief (central to self-efficacy theory), learning focus (motivation orientation theory), and value of schooling (expectancy x value theory and choice theory), persistence (expectancy x value theory and choice theory), study management (self-regulation theory), and planning and monitoring (self-regulation theory). Guzzlers include anxiety (need achievement theory and test anxiety research), low control (control theory, choice theory, and attribution theory), failure avoidance (need achievement theory), and self-sabotage/self-handicapping (self-worth motivation theory).

The strength of the Student Motivation Wheel is that it can be easily communicated by practitioners to students and, following from this, is readily understandable by students. The practitioner and student can easily separate the 'helpful' (boosters) motivation from the 'unhelpful' (guzzlers). Thus this model is an easy way for students to understand their motivation and an easy way for practitioners to explain it to them. When students understand motivation and the dimensions that comprise it, intervention is more meaningful to them and, as a consequence, is likely to be more successful.

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Purpose of the present investigation

The Student Motivation Wheel and the Student Motivation Scale have been refined since publication of preliminary psychometric statistics based on the Scale's original form. The original model and instrument did not include a measure of study management and comprised subscales of five items each. The new Scale includes a measure of study management, includes wording refinements, and now comprises four items per subscale so as to be more easily and quickly administered in class. This investigation tests the hypothesised factor structure and also examines motivation in relation to gender, year level, achievement, literacy, numeracy, socioeconomic status, and ethnicity.

Method

Sample and procedure

Respondents were 2561 students in Year 7, Year 9, Year 10, and Year 11 from eight government and three independent high schools in New South Wales (NSW) and the Australian Capital Territory (ACT). Ten schools were located in urban areas of Sydney and Canberra and one was located in a regional area of NSW. Schools primarily drew on middle to upper middle-class areas. Of students for whom gender and year-level data were available (n=2351), 39 per cent were from Year 7, 37 per cent from Year 9, 8 per cent were in Year 10, and 16 per cent were in Year 11; 46 per cent were males and 54 per cent females. Of students for whom ethnicity data were available (n=1243), a total of 177 were identified as having English as a second language. Teachers administered the Student Motivation Scale to students during class. The rating scale was first explained and a sample item presented. Students were then asked to complete the Student Motivation Scale on their own and to return the completed instrument to the teacher at the end of class.

Materials

The Student Motivation Scale is an instrument that measures high school students' motivation. It assesses motivation through six boosters and four guzzlers. Items from the ten subscales were interspersed through the instrument.

Boosters include self-belief, learning focus, value of schooling, persistence, planning and monitoring, and study management.

Self-belief(e.g. 'If I try hard, I believe I can do my schoolwork well') Self-belief is students' belief and confidence in their ability to understand or to do well in their schoolwork, to meet challenges they face, and to perform to the best of their ability.

Value of schooling (e.g. 'Learning at school is important to me') Value of schooling is how much students believe what they learn at school is useful, important, and relevant to them or to the world in general.

Learning focus (e.g. 'I feel very pleased with myself when I really understand what I'm taught at school') Learning focus is being focused on learning, solving problems, and developing skills.

Planning and monitoring (e.g. 'Before I start an assignment, I plan out how I am going to do it') Planning and monitoring is how much students plan their schoolwork, assignments, and study and how much they keep track of their progress as they are doing them.

Study management (e.g. 'When I study, I usually study in places where I can concentrate') Study management refers to the way students use their study time, organise their study timetable, and choose and arrange where they study.

Persistence (e.g. 'If I can't understand my schoolwork at first, I keep going over it until I understand it') Persistence is how much students keep trying to work out an answer or to understand a problem even when that problem is difficult or is challenging.

Guzzlers include anxiety, low control, failure avoidance, and self-sabotage.

Anxiety (e.g. 'When exams and assignments are coming up, I worry a lot') Anxiety has two parts: feeling nervous and worrying. Feeling nervous is the uneasy or sick feeling students get when they think about their schoolwork, assignments, or exams. Worrying is their fear about not doing very well in their schoolwork, assignments, or exams.

Low control (e.g. 'I'm often unsure how I can avoid doing poorly at school) Students are low in control when they are unsure about how to do well or how to avoid doing poorly.

Failure avoidance (e.g. 'Often the main reason I work at school is because I don't want to disappoint my parents') Students have an avoidance focus when the main reason they do their schoolwork is to avoid doing poorly or to avoid being seen to do poorly.

Self-sabotage/self-handicapping (e.g. 'I sometimes don't study very hard before exams so I have an excuse if I don't do as well as I hoped') Students self-sabotage when they do things that reduce their chances of success at school. Examples are putting off doing an assignment or wasting time while they are meant to be doing their schoolwork or studying for an exam.

Measurement and statistical analysis

Each booster and guzzler is comprised of four items. To each item, students rated themselves on a scale of 1 ('Strongly disagree') to 7 ('Strongly agree'). Each student's answers to the four items on each motivation area were then aggregated and converted to a score out of 100. Hence each student was assigned ten scores out of 100. All mean scores presented in this report are rounded to whole numbers. If a student answered less than one third of the instrument, he or she was dropped from further analyses. If a student answered less than three items in a subscale, he or she did not receive a score for that subscale. Data were analysed using LISREL 8.3 and SPSS for Windows. Analyses included confirmatory factor analysis, tests of reliability, independent samples t-tests, one-way ANOVAs, and correlations.

Results

Confirmatory factor analysis

Before aggregating items to form ten motivation subscale scores, confirmatory factor analysis (CFA) was carried out to justify forming these subscales. CFA was conducted using LISREL 8.3 (Joreskog & Sorbom, 1999). A detailed presentation of the conduct of CFA is beyond the scope of the present report and is available elsewhere (e.g. Bollen, 1989; Joreskog & Sorbom, 1989; Pedhazur & Schmelkin, 1991). Maximum likelihood was the method of estimation used for the models. The raw data were used as input to PRELIS 2 (Joreskog & Sorbom, 1999) and a covariance matrix was produced which was subsequently analysed using LISREL. In terms of goodness-of-fit indices, the Tucker Lewis Index (TLI) is emphasised, as simulation studies have shown that it is relatively independent of sample size and also imposes an appropriate penalty for inclusion of additional variables in a given model (Marsh, Balla, & Hau, 1996). Following Marsh et al., the Relative Noncentrality Index (RNI) and Root Mean Square Error of Approximation (RMSEA) are also emphasised as measures of goodness of fit. TLI and RNI values above .90 and RMSEA below .05 are typically considered to indicate acceptable fit of the data to the model.

The CFA yielded an acceptable fit to the data (chi square=4018.91, df=695, TLI=.91, RNI=.92, RMSEA=.045). Factor loadings are presented in Table 2. Taken together, the loadings are acceptable.

Descriptive statistics and reliability

Given the strong factor structure, it was considered appropriate to aggregate items to form subscales. Subscales were formed by generating the mean of the set of four items for each booster and guzzler. This mean was then converted to a score out of 100. All scores/100 in this paper are presented as rounded whole numbers.

Descriptive and reliability statistics for each booster and guzzler are presented in Table 3. Results show that all boosters and guzzlers are reliable (range .77 to .83). Distributional data also show that each booster and guzzler is approximately normally distributed.

In terms of boosters, students score highest on self-belief, value of schooling, and learning focus and score relatively lower in planning and monitoring (particularly), study management, and persistence. In terms of guzzlers, we see that students' anxiety is of most concern.

Correlations among boosters and guzzlers

The relationships among all boosters and guzzlers were examined using correlations drawn from the CFA conducted above. Results are shown in Table 4. Predictably, all boosters were highly positively correlated and all guzzlers were highly positively correlated. The median correlation among boosters was .62 (range .46 to .83). The median correlation among guzzlers was .54 (range .30 to .60). The median correlation between boosters and guzzlers was .02 (range -.33 to .27).

Year-level and gender effects

Year-level effects on each facet of motivation were explored using a series of one-way ANOVAs. Significant effects were followed up by post-hoc comparisons using the Newman-Keuls test. Results are shown in Table 5. Clearly there are differences between junior, middle, and senior high school students such that middle high school students are significandy lower on all boosters. Senior high school students generally perform best on motivation boosters and lowest on the guzzlers; however they are also highest in anxiety. Although junior high school students are stronger than their middle high school counterparts on boosters, they are also significantly lower in control, higher in failure avoidance, and higher in self-sabotage. When interpreting these results, it is worth noting that middle high school students primarily comprise Year 9 students and junior high students comprise only Year 7 students.

Gender effects in motivation were explored using a series of independent samples t-tests. In Table 6 are gender effects on each facet of motivation. Girls are significantly higher in value of schooling, learning focus, planning and monitoring, study management, and persistence. However girls are also significantly higher in anxiety. Boys are significantly higher in self-sabotage.

Correlations between motivation and achievement, literacy, and numeracy

The relationship between each booster and guzzler and achievement was examined using a series of Pearson product moment correlations. Mathematics and English data were available for 269 students. These students were drawn from only one school, were in Years 9, 10, and 11, and their achievement data comprised school test scores. Results are presented in Table 7. Taken together, each booster is significantly correlated with achievement such that higher scores on each booster are associated with higher achievement. In terms of guzzlers, students low in control and higher in self-sabotage tend to achieve at a lower level on the achievement measures, five of the six negative correlations.

Correlations between each facet of motivation and students' literacy and numeracy were explored. Literacy data were available for 1604 students and numeracy data were available for 1597 students. Literacy scores were computed by generating the mean of students' state/territory-based standardised test scores on reading, spelling, spelling in writing, writing content, and writing language. Numeracy scores were computed by finding the mean of students' state/territory-based standardised test scores on measurement, number, and space. Literacy and numeracy data were drawn from eight schools and from Year 7 and Year 9 students. Literacy and numeracy data were collected earlier in the same school year. Correlation coefficients are presented in Table 8.

Relatively modest effects are found for boosters. Interestingly the strongest effects are found for guzzlers such that low control, failure avoidance, and self-sabotage are negatively correlated with literacy and numeracy. Anxiety is negatively correlated with numeracy. In terms of boosters, the data show that self-belief and persistence are correlated with literacy and numeracy. Although small, there is a negative relationship between planning and numeracy. This may be because planning as measured by the Student Motivation Scale is related to assignments, study, and homework involving more extended and carefully thought-out tasks than briefer numeracy testing which may be preferred by some poorer planners.

The role of ethnicity and socioeconomic status

Of students for whom ethnicity data were available (n=1243), a total of 177 (14.2%) students were identified as having English as a second language (ESL). This was treated as a proxy for ethnicity. The relationship between ethnicity and motivation was explored using a series of independent samples t-tests. Table 9 presents findings. In terms of boosters, data show that ESL students are significantly higher than non-ESL students in value of schooling, learning focus, planning and monitoring, and study management. However ESL students are also significantly lower in perceived control. Given the relatively small size of the ESL group studied here, it is recommended that caution should be applied before generalising to the broader ESL population.

A total of 1549 students could be linked to state/territory departmental socioeconomic data. Socioeconomic status (SES) was determined through an index of relative socioeconomic disadvantage (IRSED). Students were grouped into three groups (lower 25%, middle 50%, and upper 25%). The effect of SES was explored using a series of one-way ANOVAs. Significant effects were followed up by post-hoc comparisons using the Newman-Keuls test. Table 10 presents findings. These data show that there were no differences between any SES levels on any of the boosters. However, lower and middle SES students are significantly lower than upper SES students in control and higher in failure avoidance. Middle SES students are also significantly higher than upper SES students in self-sabotage. It is not clear how representative this group of students is of SES statuses in the broader community and so it is recommended that generalising to the broader population should be carried out cautiously.

Discussion

This investigation explores the psychometric properties of the refined Student Motivation Scale as well as considering gender, year level, ethnicity, achievement, literacy, numeracy, and SES effects relevant to motivation. Taken together, results show that the factor structure of the Student Motivation Scale is clear and each subscale representing the proposed facets of motivation is reliable. In terms of boosters, students are higher on self-belief, learning focus, and value of schooling and relatively lower in planning and monitoring, study management, and persistence. In terms of guzzlers, anxiety is highest.

Middle school students are lower on boosters than junior and senior school students but junior high students are also lower in control and higher in failure avoidance. Girls are significantly higher than boys on most boosters but also higher than boys in anxiety. Boys are significantly higher than girls in self-sabotage. ESL students are significantly higher than non-ESL students in value of schooling, learning focus, planning and monitoring, and study management. Lower and middle SES students are significantly lower than upper SES students in control and higher in failure avoidance. Middle SES students are also significantly higher than upper SES students in self-sabotage.

Each booster is significantly correlated with achievement such that higher scores on each booster are associated with higher achievement. In terms of guzzlers, students low in control as well as students who are higher in self-sabotage tend to achieve at a lower level on the achievement measures. Self-belief and persistence are positively correlated with literacy and numeracy; however the strongest effects in literacy and numeracy are found for guzzlers such that low control, failure avoidance, and self-sabotage are negatively correlated with literacy and numeracy.

Boosters and guzzlers of note

It was found that the cognitive booster components are a strength among students. Data show that students are relatively high in self-belief, value of schooling, and adopt a mastery and learning approach to their studies. However, work is needed to translate these adaptive orientations into adaptive behaviour in the form of greater study management, planning, monitoring, and persistence (see Martin, 2001, 2002; Martin & Marsh, 2003).

Anxiety is the highest of the guzzlers and this is consistent with findings elsewhere (Martin, 2001). In many respects, anxiety is a hallmark of the competitive education system Australia-wide. In this sense, at least some level of anxiety is unavoidable. Although a certain level of anxiety may be arousing and required for peak performance, excessive anxiety can be counterproductive for some students and, to the extent that this is the case, it needs to be reduced (Martin, 2001, 2002; Martin & Marsh, 2003). Notwithstanding this, although there was a significant negative relationship between anxiety and numeracy, there was no significant effect on any achievement measures (see Table 7). This raises a question concerning its status as an unqualified guzzler.

Year-level and gender effects in motivation

A dominant finding was that junior high school and senior high school students are higher than middle high school students on boosters. These data suggest that younger high school students' adaptive motivation represents a significant window of opportunity through which to launch them into their middle high schooling. The question is what happens to students between junior high and middle high? What is it about the individual and joint effects of the demands placed upon students, their cognitive development, the emotional and social changes they experience, how they are assessed, how teachers respond to them, the way curriculum is delivered, and life events that render students significantly lower on all boosters by middle high schooling?

Existing research suggests that there is a lack of fit between the stage adolescents are at and the learning environment they experience when they move to middle high (Midgley, Middleton, Gheen, & Kumar, 2002). This research suggests that goal theory provides information on how to deal with this through the central role of teachers enhancing students' learning focus and reducing the emphasis on performance goals (see also Kumar, Gheen, & Kaplan, 2002). Other research has highlighted the social and personal instability during this time and that this can have the effect of distracting attention from academic goals (Hardy, Bukowski, & Sippola, 2002).

Taken as a whole, girls are significantly higher than boys in ratings of value of schooling, learning focus, planning and monitoring, study management, and persistence. However they are also significantly higher in anxiety. Boys are significantly higher in self-sabotage. These findings provide some insight into factors that may be contributing to boys' lower levels of achievement at state/territory and national levels. Although girls score significantly higher in anxiety, it may be that this anxiety is played out through greater diligence and persistence than with withdrawal, underperformance, and failure acceptance. As indicated above, this is supported by data in Table 6 showing that girls are significantly higher in planning and monitoring, study management, and persistence.

Achievement, literacy, and numeracy

Although self-belief and persistence are significantly correlated with literacy and achievement, they do not share as much variance with these outcomes as do the guzzlers, low control, failure avoidance, and self-sabotage. These guzzlers play a markedly greater role in students' literacy and numeracy than the boosters. It seems that, in terms of core skills such as literacy and numeracy, it is critical to examine the maladaptive dimensions of students' motivation.

Interestingly, however, the data also show that the boosters play a stronger role in academic achievement (mathematics and English) and this implies that some facets of motivation are more relevant to academic achievement whereas others are more relevant to core skills such as literacy and numeracy. Further research is needed to determine why it is that motivation guzzlers seem to have a greater effect than boosters on core skills such as literacy and numeracy. Similar research is needed to determine why boosters seem to have a greater (positive) impact than guzzlers on achievement in key learning areas. It is possible that school achievement in this study (measured through school tests) is more likely to be influenced by study and preparation for which the boosters assessed may have a particular relevance. Hence the differences may relate principally to the type of test and not the boosters and guzzlers per se.

Ethnicity and socioeconomic effects

It is interesting that ESL students score significantly higher than non-ESL students on a number of boosters. This is consistent with findings in previous work which shows more positive orientations towards education among students of non-English-speaking backgrounds. For example, it has been found that young people from non-English-speaking backgrounds are more likely to complete school and go into higher education (Marks & Ainley, 1999). It has been suggested that these students' families may instil a greater valuing of education (consistent with high scores on value of schooling here). Also there are some families who arrive in Australia with high standards of education and strong financial resources behind them. However caution should be exercised in generalising to the broader ESL or ethnic population.

There are no differences on any of the boosters as a function of SES. Lower and middle SES students are significantly lower than upper SES students in control and higher in failure avoidance. This is broadly consistent with previous research into the effects of SES, showing less adaptive educational outcomes for students from lower SES backgrounds (Ainley, 1998; Marks & Ainley, 1997; Teese, 1995). However it is not clear how representative this group of students is of SES statuses in the broader community and so it is recommended that generalising to the broader population may not be appropriate.

Enhancing students' motivation

At a meta-level, intervention designed to enhance students' motivation involves improving students' (a) approach to their schoolwork, (b) beliefs about themselves, (c) attitudes towards learning, achievement, and school, (d) study skills, and (e) reasons for learning. Also at a meta-level, intervention involves dealing with (a) educators' messages to students, (b) educators' expectations for students, (c) how learning is structured and paced, (d) feedback to students on their work, and (e) classroom goals and assessment.

To enhance students' motivation, however, we must move beyond the meta-level to consider the specific ways in which motivation is enacted in students' lives and in the classroom. The proposed model of motivation by Martin (2001) and tested using the Student Motivation Scale holds that educators are to do one or more of the following: keep high boosters high, keep low guzzlers low, increase low boosters, and reduce high guzzlers. In a previous issue of this journal, Martin (2002) has provided detailed analyses of ways to achieve each of these. (See also Martin, 2001; Martin & Marsh, 2003.)

Further conceptualising about anxiety and failure avoidance

There may be a need to extend current conceptualisation of anxiety and failure avoidance in the light of the mixed findings in the present data. For example, failure avoidance is negatively associated with literacy and numeracy, yet is positively correlated with most boosters. Similarly anxiety is negatively associated with numeracy, for example, yet is positively associated with all boosters. This contrasts with self-handicapping, for example, which is unambiguously maladaptive in motivational and achievement terms. To a lesser extent, low control is also more clearly a guzzler in achievement and motivation terms.

It seems that there may be an intermediate level of 'guzzling' that comprises anxiety and failure avoidance and which is not entirely maladaptive in either motivation or achievement terms. This intermediate level may be referred to as a 'muffler'--in which component constructs muffle motivation and achievement but do not necessarily and unambiguously reduce them as one might reasonably think a 'true guzzler' would.

The status of failure avoidance and anxiety as potential mufflers is conceptually defensible. One will recall that failure avoidance is a motivation to work but mainly for purposes of avoiding disappointment, poor performance, or disapproval. Hence a failure avoider is prepared to work (so it is not a guzzler per se) but perhaps not for entirely adaptive reasons (so it is not a booster). It is also conceivable that students will respond to anxiety in one of two different ways. One student responds to anxiety with intensified or sustained effort (hence not a guzzler in this sense) whereas the other responds with avoidance or self-protective aims (hence not a booster in this sense). Indeed, when failure avoidance and anxiety are removed from the guzzler category, the mean correlation between boosters and guzzlers shifts from .01 to -.14, the mean correlation between boosters and mufflers is .12 and the mean correlation between mufflers and guzzlers is .49. Further research is needed to explore the relevance of mufflers in a model of motivation and the utility of including failure avoidance and anxiety within an intermediate category of motivation.

Where to from here?

In addition to further research exploring possible further differentiation of some constructs, there are some areas worth pursuing in future studies. Because the data are derived from self-reports, it is important that future research examines the constructs using data derived from additional sources (e.g. teacher ratings and parent ratings). It may be that on some factors, for example, parents rate their child differently from how the child rates himself or herself. Preliminary data collected by this author suggest that, although there is consistency between parent and child ratings on most factors in the Student Motivation Scale, there are some discrepancies: children rate their anxiety markedly higher than their parents rate it and also rate their self-sabotage somewhat lower than their parents rate it. What are the implications of such discrepancies? Are parents not recognising higher levels of anxiety in their children? Do children self-sabotage more than they are prepared to admit? These are questions well suited to some qualitative research. Indeed Juvonen and Murdock (1993) presented evidence that adolescents employ differential attributional self-presentational strategies concerning their academic success or failure to adults and peers.

It is also important to recognise that the measures relate to school as a whole and not particular school subjects. Future research should test these constructs in the context of specific school subjects. It may be that the more focused the measures are on specific subjects, the greater their association with achievement in those subjects and the more actionable they are from an intervention perspective.

Finally the data were collected at the one time point and so causal statements regarding the ability of the measures to predict achievement, literacy, or numeracy at a later time are not advanced. Future longitudinal work is needed, particularly with a view to determining the relative contribution of each booster and guzzler to educational outcomes over time.

Conclusion

The research presented here confirms the strong factor structure underpinning the Student Motivation Scale and, through this instrument, identifies particular student groups who need further assistance as well as those whose motivation is strong and who need to be sustained. The research also provides some direction as to which particular facets of motivation are relatively high or low and for which student groups. Although the emphasis given to each facet of motivation as well as the strategies to deal with them will vary from school to school, it is important to underscore the importance of maintaining motivation strengths--not just targeting areas of relative concern. Every student, classroom, and school has motivation strengths and these are very much the keys to enhancing other areas of student motivation that require closer attention. Keywords anxiety learning motivation self esteem educational achievement secondary education student motivation Table 1 Theoretical perspectives underpinning the Student Motivation Wheel Student Motivation Wheel Theory Boosters Self-belief Self-efficacy theory Learning focus Motivation orientation theory Value of schooling Expectancy x value theory Choice theory Study management Self-regulation theory Planning and monitoring Self-regulation theory Persistence Expectancy x value theory Choice theory Guzzlers Anxiety Need achievement theory Test anxiety research Low control Control theory Attribution theory Failure avoidance Need achievement theory Self-worth motivation theory Self-sabotage Self-worth motivation theory Student Motivation Wheel Researchers Boosters Self-belief Bandura, 1997 Learning focus Nicholls, 1989 Value of schooling Eccles, 1983; Wigfield, 1994 Glasser, 1998 Study management Zimmerman, 1994 Planning and monitoring Zimmerman, 1994 Persistence Eccles, 1983; Wigfield, 1994 Glasser, 1998 Guzzlers Anxiety Atkinson 1957 McClelland, 1965 Sarason & Sarason, 1990; Spielberger, 1985 Low control Connell, 1985 Weiner, 1994 Failure avoidance Atkinson 1957; McClelland, 1965 Covington, 1992, 1998 Self-sabotage Covington, 1992, 1998 Table 2 Factor loadings for the Student Motivation Scale Learn- Plan & Self- Value ing moni- Study belief school focus tor manage (SB) (VS) (LF) (PM) (SM) SB1 .65 SB2 .67 SB3 .71 SB4 .72 VS1 .56 VS2 .70 VS3 .66 VS4 .74 LF1 .67 LF2 .69 LF3 .75 LF4 .74 PM1 .60 PM2 .76 PM3 .81 PM4 .57 SM1 .73 SM2 .59 SM3 .82 SM4 .75 P1 P2 P3 P4 ANX1 ANX2 ANX3 ANX4 LC1 LC2 LC3 LC4 AV1 AV2 AV3 AV4 SS1 SS2 SS3 SS4 Self- Low Failure sabo- Persist Anxiety control Avoid tage (P) (ANX) (LC) (FA) (SS) SB1 SB2 SB3 SB4 VS1 VS2 VS3 VS4 LF1 LF2 LF3 LF4 PM1 PM2 PM3 PM4 SM1 SM2 SM3 SM4 P1 .58 P2 .69 P3 .76 P4 .80 ANX1 .72 ANX2 .69 ANX3 .66 ANX4 .71 LC1 .67 LC2 .76 LC3 .77 LC4 .75 AV1 .77 AV2 .85 AV3 .50 AV4 .65 SS1 .57 SS2 .76 SS3 .82 SS4 .74 Table 3 Descriptive statistics and Cronbach's alphas Cronbach's M/100 SD Skew Kurtosis alpha Boosters Self-belief 79 14.6 .92 1.4 .79 Value of schooling 79 14.9 -1.1 1.4 .77 Learning focus 79 14.6 -.94 1.2 .81 Planning and monitoring 58 18.9 -.21 -.42 .78 Study management 68 18.2 -.64 .13 .81 Persistence 70 15.9 -.65 .35 .81 Guzzlers Anxiety 61 20.4 -.13 -.62 .79 Low control 51 18.9 .06 -.59 .83 Failure avoidance 50 20.2 .23 -.57 .79 Self-sabotage 40 18.4 .62 -.13 .82 Table 4 Inter-scale correlations SB LF VS PM SM Self-belief (SB) -- Learning focus (LF) .70 -- Value of school (VS) .77 .83 -- Plan & monitor (PM) .46 .52 .58 -- Study manage (SM) .56 .61 .62 .73 -- Persistence (P) .67 .56 .66 .65 .62 Anxiety (A) .08 .27 .19 .23 .24 Low control (LC) -.22 .03 -.03 .02 -.01 Failure avoid (FA) -.05 .09 .05 .16 .08 Self-sabotage (SS) -.33 -.18 -.24 -.10 -.18 P A LC FA SS Self-belief (SB) Learning focus (LF) Value of school (VS) Plan & monitor (PM) Study manage (SM) Persistence (P) -- Anxiety (A) .10 -- Low control (LC) -.15 .60 -- Failure avoid (FA) .02 .48 .57 -- Self-sabotage (SS) -.27 .30 .57 .51 -- Note: Correlations exceeding +/-.06 are significant at p<0.05 Table 5 Year-level motivation effects Junior Middle M SD M SD Boosters Self-belief 81 13.8 76 15.6 Value of schooling 82 14.3 75 15.3 Learning focus 80 14.5 77 15.0 Planning and monitoring 59 18.8 55 19.3 Study management 70 18.6 66 18.4 Persistence 72 14.9 67 16.9 Guzzlers Anxiety 60 21.0 60 20 Low control 53 19.4 50 18.7 Failure avoidance 53 21.2 49 19.4 Self-sabotage 40 19.3 40 17.9 Senior SNK M SD F effect Boosters Self-belief 81 12.1 26.2 *** M < J, S Value of schooling 81 12.3 52.3 *** M < J, S Learning focus 83 10.9 27.9 *** M < J < S Planning and monitoring 65 16.3 40.1 *** M < J < S Study management 74 14.9 34.1 *** M < J < S Persistence 73 13.7 41.3 *** M < J, S Guzzlers Anxiety 63 19.0 4.5 * S > J, M Low control 49 17.6 8.1 *** J > M, S Failure avoidance 45 18.2 18.8 *** J > M > S Self-sabotage 35 16.6 12.2 *** J, M > S * p<0.05 ** p<0.01 *** p<0.001 J=Junior high (Year 7); M=Middle high (Year 9 - primarily - and Year 10); S=Senior high (Year 11) Table 6 Gender differences in motivation Female Male M SD M SD t Effect Boosters Self-belief 79 13.5 79 15.7 1.5 -- Value of schooling 80 13.9 78 15.7 2.25 * G > B Learning focus 81 13.4 77 15.2 6.8 *** G > B Planning and monitoring 61 18.1 55 19.4 7.8 *** G > B Study management 71 16.8 65 19.1 8.2 *** G > B Persistence 72 15.3 68 16.5 5.2 *** G > B Guzzlers Anxiety 64 19.5 57 20.1 8.6 *** G > B Low control 51 18.6 50 19.2 1.3 -- Failure avoidance 49 19.1 51 21.2 1.9 -- Self-sabotage 38 17.7 41 19.0 3.5 *** B > G * p<0.05 ** p<0.01 *** p<0.001 G=Girls; B=Boys Table 7 Correlations between boosters and guzzlers and achievement GPA Maths English Boosters Self-belief .34 *** .20 *** .36 *** Value of schooling .24 *** .14 * .26 *** Learning focus .27 *** .12 .33 *** Planning and monitoring .17 ** .07 .21 *** Persistence .30 *** .19 ** .30 *** Guzzlers Anxiety -.07 -.01 -.11 Low control -.22 *** -.11 -.26 *** Failure Avoidance -.03 .04 -.09 Self-sabotage -.38 *** -.21 *** -.41 *** * p<0.05 ** p<0.01 *** p<0.001 GPA = Mean of maths and English achievement scores Table 8 Correlation between each facet of motivation and literacy and numeracy Literacy Numeracy Boosters Self-belief .13 *** .15 *** Value of schooling .02 .01 Learning focus .05 -.03 Planning and monitoring -.02 -.11 ** Study management .04 -.04 Persistence .08 ** .10 *** Guzzlers Anxiety -.02 -.15 *** Low control -.27 *** -.36 *** Failure avoidance -.18 *** -.20 *** Self-sabotage -.29 *** -.29 *** * p<0.05 ** p<0.01 *** p<0.001 Table 9 Ethnicity and motivation ESL Non-ESL M SD M SD t Effect Boosters Self-belief 80 15.1 78 15.1 1.44 -- Value of schooling 81 15.2 77 15.3 2.49 * ESL>NESL Learning focus 81 14.3 77 15.4 2.65 ** ESL>NESL Planning and monitoring 58 19.9 55 18.9 2.33 * ESL>NESL Study management 70 17.1 66 18.9 2.72 ** ESL>NESL Persistence 71 16.1 69 16.2 1.69 -- Guzzlers Anxiety 60 19.7 59 21.0 .86 -- Low control 54 20.1 50 19.2 2.63** ESL>NESL Failure avoidance 20 19.8 20 20.4 1.39 -- Self-sabotage 43 19.2 40 18.8 1.92 -- * p<0.05 ** p<0.01 ESL=English as a second language Note: A conservative Bonferroni correction would eliminate these ethnicity effects. Table 10 SES and motivation Lower 25% Middle 50% Upper 25% M SD M SD M SD F Boosters Self-belief 79 14.3 78 14.9 79 15.2 .93 Value of schooling 79 14.9 78 15.0 78 15.9 .45 Learning focus 80 13.9 78 14.8 78 16.1 2.63 Planning and monitoring 57 19.9 55 19.2 55 18.3 1.74 Study management 68 18.8 67 19.1 68 18.2 .66 Persistence 71 15.4 69 16.7 70 15.8 2.26 Guzzlers Anxiety 59 20.4 59 20.8 58 21.6 .28 Low control 53 18.7 51 19.7 48 19.6 5.83 ** Failure avoidance 51 20.8 51 20.9 48 19.7 4.31 * Self-sabotage 40 18.7 41 19.8 38 17.6 3.36 * Effect Boosters Self-belief -- Value of schooling -- Learning focus -- Planning and monitoring -- Study management -- Persistence -- Guzzlers Anxiety -- Low control L, M > U Failure avoidance L, M > U Self-sabotage M > U * p<0.05 ** p<0.01 Note: A conservative Bonferroni correction would eliminate these SES effects.

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Acknowledgement

The author would like to thank the ACT Department of Education, Youth and Family Services for its support in the conduct of part of this report.

Author

Dr Andrew Martin works in the Self-concept Enhancement and Learning Facilitation (SELF) Research Centre at the University of Western Sydney, Locked Bag 1797, Penrith South DC, New South Wales 1797. Email: a.martin@uws.edu.au Andrew J. Martin University of Western Sydney