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    Construct Validity Issues in the Measurement of Motivation to Learn

    AnneMarie M. Conley and Stuart A. Karabenick

    University of Michigan, Ann Arbor

    Mailing address:

    1400 SEB, 610 East University

    Combined Program in Education and Psychology

    The University of Michigan

    Ann Arbor, MI 48109

    Phone-734-763-1386

    Fax-734-615-2164

    Email: [email protected]

    Presented at the biennial meeting of the Society for Research on Adolescence, SanFrancisco, March 2006. Research reported herein was supported by a grant to the

    Math and Science Partnership Motivation Assessment Program (MSP-MAP) fromthe National Science Foundation (EHR No. 0335369). Views expressed are the

    authors and are not necessarily representative of the funding agency.

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    Validity and Motivation to Learn 2

    Construct Validity Issues in the Measurement of Motivation to Learn

    Motivation plays a critical role in student learning and achievement; it is intimately

    related to the ways students think, feel, and act in schools. Evidence from research on student

    learning in general (see Pintrich & Schunk, 2002; Pintrich & Maehr, 2004), and mathematics and

    science in particular (e.g., Fennema, 1989; Schoenfeld, 1992), demonstrates that students

    motivation, affect, strategies, and beliefs about knowledge in these disciplines can influence their

    learning and performance. Furthermore, research suggests that students motivation and related

    outcomes are sensitive to characteristics of the learning context, including teachers instructional

    practices as well as school and classroom climate (Ames, 1992; Anderman & Maehr, 1999;

    Eccles & Midgley, 1989). It is important, therefore, for reform efforts to determine how their

    programs affect student motivation, especially since such changes can precede, or even occur in

    the absence of, targeted cognitive outcomes. The primary goal of the research reported on here

    was to develop and make available reliable, valid, and practical tools to assess student

    motivational beliefs for mathematics and science. These tools are being used with different math

    and science reform projects to support evidence-based claims about the effects of their

    interventions, and to explore the role of motivation-related outcomes as mediators and

    moderators of student achievement in intervention models (Maehr & Karabenick, 2004).

    There exist a number of different approaches to the study of motivation. For example,

    consider three different approaches to the question, What makes students want to learn in

    school? One approach is to consider this question in terms of interest, as an individuals

    attraction to, or liking or enjoyment of, a particular task or domain. Another perspective

    conceptualizes wanting in terms of value, a subjective judgment of the degree to which a task

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    Validity and Motivation to Learn 3

    or domain can fulfill needs, facilitate reaching goals, or confirm aspects of ones self-schema. A

    third approach attends to students goals, or their reasons for participating in achievement-related

    activities. There often is considerable overlap among these constructs, with consistent, moderate

    correlations among them. Though some have highlighted the importance of doing research that

    considers these components simultaneously, such research has been limited to date. This paper

    presents results from a large-scale study of middle and high school students that aimed to address

    definition and measurement issues by considering multiple constructs deriving from different

    theoretical traditions simultaneously.

    Evidence in support of the construct validity of this set of measures to assess motivation

    to learn is offered using a framework proposed by Messick (1989). Historically, construct

    validity has been dealt with in different ways, with recent conceptualizations rejecting the

    traditional three-part (construct, criterion, content) validity approachin favor of a more unified

    validity theory (Messick, 1989; Pintrich, Wolters, & Baxter, 2000). In Messicks (1989) unified

    framework, construct validity is central and other forms of validity are subsumed under it.

    Messick (1989) proposed a multidimensional framework for thinking about construct

    validity, and described five kinds of evidence that can be used to support claims of construct

    validity: content, substantive, structural, external, and generality of meaning. Content-related

    evidence concerns how well the items reflect the content of the domain. Substantive evidence

    concerns the relation between data and theory, and the guiding question is whether the data

    generated by the instrument are consistent with the theory. Structural evidence, on the other

    hand, concerns the relation between theory and the way the data are reduced: Do the scores

    obtained reflect the complexities of the theoretical model? External evidence is perhaps the most

    often considered, and questions include how the instrument relates to other measures of the same

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    Validity and Motivation to Learn 4

    construct, and whether the instrument relates to other constructs in theoretically sensible ways.

    Finally, questions of generality of meaning revolve around how the findings generalize across

    different populations, contexts, or subject-matter domains. In more recent work, Messick (1995)

    specified an additional source of evidence, concerned with the intended and unintended

    consequences of score use. This aspect of construct validity is key when tests are used for

    assessment or placement decisions, as in the case of performance assessments or standardized

    tests. Since a complete discussion of all of Messicks sources of evidence is beyond the scope of

    this paper, the focus has been narrowed to present substantive, structural, consequential and

    generality of meaning evidence of the validity of a set of motivation-related measures.

    The set of motivation-related measures included here draws from the most often-

    researched theoretical frameworks in motivation literature today. These theoretical traditions are

    characterized by different approaches to the study of the basic questions most research on

    motivation in education tries to answer: What makes students want to learn in school? What

    makes students feel competent? How do students wants and beliefs in the classroom influence

    whether and how they approach learning? The research described here draws heavily on

    expectancy-value theory, achievement goal theory, work on personal and situational interest, and

    self-efficacy theory.

    Self-efficacy refers to students beliefs that they have the resources and confidence to do

    the tasks in the classroom (Bandura, 1986; Pintrich & Schunk, 2002). It is important that self-

    efficacy be calibrated to ones actual accomplishments (Pintrich & Schunk, 2002). As

    intervention projects make changes and improve instruction, these reforms may require students

    to think differently, to do math or science differently, and to engage the material in different

    ways than is usual in mathematics and science classrooms. Besides beliefs about efficacy and

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    Validity and Motivation to Learn 5

    control, task value beliefs are another important motivational component (e.g., Eccles et al.,

    1998; Pintrich & Schunk, 2002). Longitudinal research by Eccles and her colleagues (e.g.,

    Eccles, et al., 1998; Fredericks, et al., 2002; Jacobs et al., 2002) has shown that student beliefs

    about the importance and utility of mathematics leads them to enroll in more math courses in the

    future. In addition, this research has shown that task value beliefs lead to enrollment or choices

    to take more mathematics courses, but that once enrolled in the actual course, efficacy beliefs are

    more strongly related to actual performance or achievement.

    Personal interest refers to an individual's attraction to, or general liking and enjoyment of,

    a specific activity or domain (Pintrich & Schunk, 2002). Eccles and her colleagues (Eccles, et al.,

    1998) have shown that personal interest is an important component of motivation and functions

    similarly to importance and utility value beliefs. In addition, other researchers have shown that

    high levels of personal interest lead to more cognitive engagement, self-regulation, and

    achievement (e.g., Koller, et al., 2001; Pintrich & Schunk, 2002). In many mathematics and

    science reform projects, the goal is to increase student interest and positive attitudes towards

    mathematics and science domains as well as interest in careers in these areas. It is an important

    outcome in its own right, as well as a potentially important mediator of achievement (Koller et

    al., 2001).

    Another important component of student motivation concerns general achievement goals,

    or students goals for academic learning in classroom contexts. The general distinction between

    mastery and performance goals contrasts students who are mastery-oriented and focused on

    learning and understanding and those students who are performance-oriented and focused on

    doing better than others in terms of grades or other outcomes that invite interpersonal

    comparisons (Pintrich, 2000a, b). Generally, mastery goals are positive and adaptive and lead to

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    Validity and Motivation to Learn 6

    more interest, engagement, and learning. Performance goals, on the other hand, can be adaptive

    or maladaptive depending on whether students adopt an approach or avoid focus. Performance-

    avoid goals where students are concerned about looking dumb or trying to avoid getting the

    lowest scores are clearly maladaptive and are associated with less interest, engagement, and

    lower levels of performance (Pintrich, 2000a, b). As intervention projects make changes and

    improve instruction, it is important to understand how these different goals may motivate

    students to learn and perform in different mathematics and science classrooms.

    Method

    Design

    Data presented here were obtained through a collaboration between the

    Motivation Assessment Program at the University of Michigan and a standards-based,

    data-driven intervention program in the Southwest improve students academic

    performance in mathematics. The collaboration included providing teachers with the

    knowledge and tools to accurately diagnose students deficiencies, assess their progress,

    adjust the curriculum and pedagogy, and transform the departmental culture to maximize

    student learning in mathematics. Over the last two years, the partners have collaborated

    to assess changes in motivation of the more than 14,000 students over the course of the

    school year. Aggregated analyses of these data were disseminated to teachers and project

    staff as part of professional development activities that serve as a major component of the

    intervention, as well as through individual reports to teachers. The professional

    development activities were designed to change administrative practices in the schools

    and in the classrooms, effecting a cultural change that creates a sustainable climate of

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    Validity and Motivation to Learn 7

    improvement and achievement.

    The partnership to date has involved five waves of student motivation surveys

    over two school years, as well as two waves of teacher attitude and belief surveys. Data

    from the beginning and end of the first year of student surveys are presented here.

    Students were administered questionnaires by research assistants in their regular math

    classrooms four weeks after the start of the school year and again approximately four

    weeks before the end of the school year. All students in class on the day of administration

    participated. Students were told that the purpose of the confidential survey was to elicit

    their thoughts and feelings about the subject of math and their own math class. Students

    were guided through a sample item and then completed a 110- question survey during

    their math period. Items were read aloud to the middle school students; high school

    students worked through the survey independently after receiving instructions from

    trained research assistants. The survey took approximately 30 min. to complete. The

    teacher was present in the room while the survey was being completed, but remained

    seated and unobtrusive, unable to view any of the survey responses.

    Participants

    Analyses presented here are based on 8,429 students (49% female) from 487

    classrooms in 14 ethnically diverse, working class public middle and high schools in

    California (72% Latino/a, 16% Vietnamese, 6% Caucasian, 6% Other - primarily SE

    Asian). Between 60 and 75% of the students in these schools were eligible to receive free

    or reduced lunch. Two of the four districts have been characterized as high-need districts

    by the state and there is a sizable population of English Learners.

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    Measures

    The student motivation survey included measures of self-efficacy for learning

    math and solving math problems, task value for, and students personal achievement

    goals. Items were rated on a 5-point Likert scale (1 = not at all true; 3 = somewhat true; 5

    = very true), and all questions were worded to have students focus on the domain of

    mathematics.

    Task Value was measured with 18 items, which included four components

    adapted from previous work. Interest (6 items, != .95) referred to students attraction to,

    liking for, and enjoyment of math.(e.g., I find math very interesting). Utility (6 items,

    != .87) was concerned with students beliefs about the usefulness of math as an area of

    study(e.g., Math is useful to me for things I do outside of school). While utility value

    focused on the importance of math as a means to an end, attainment value focused on the

    value of math as part of a students identity. Attainment value (6 items, != .87) referred

    to students judgments about the importance of math for their sense of who they are (e.g.,

    Thinking mathematically is an important part of who I am). Cost value (2 items,!=

    .81) tapped students judgments about the amount of effort required to be successful in

    math (e.g., Success in math requires that I give up other activities I enjoy).

    Efficacy (8 items, != .88) items assessed students judgments about their ability

    and confidence to perform adequately in math(e.g., How sure are you that you can do

    even the most difficult math work).Achievement goals (three 5-item scales items, !s =

    .87, .84, .79) referred to students purposes when approaching, engaging in, and

    responding to math instruction. Mastery goals focused on learning and understanding

    (e.g., My goal in math is to learn as much as I can), performance-approach goals

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    Validity and Motivation to Learn 9

    focused on demonstrating ability and outperforming others (e.g., My goal in math is to

    look smarter than other students), and performance-avoid goals focused on not looking

    dumb (e.g., My goal in math is to avoid looking like I cant do my work).

    Substantive Evidence

    Substantive evidence is concerned with the internal relations among the items in

    an instrument. The guiding question is whether the data generated by the instrument are

    consistent with the theory of the construct. The measures of task value and achievement

    goals included in our assessment would show substantive evidence of construct validity,

    according to Messick (1989), if the number and type of scores generated was consistent

    with the theories from which the items were developed. In the case of task value, four

    components are predicted: interest, utility, attainment, and cost. Previous studies have

    sometimes had difficulty finding the predicted distinctions between utility value and

    attainment value. Results from exploratory factor analyses of the task value items are

    presented in tables 1 (beginning of school year) and 2 (end of school year).

    Structural Evidence

    This component of construct validity asks whether the scoring of the instrument

    reflects the complexities of the theory. A single total score indicates a unitary construct,

    while a combination of composite scores and subscores indicates a hierarchical construct.

    Subsumed under the structural component are issues related to scale reliability. With

    older validity theories, reliability was separate from validity. This made it possible to

    have scales that were valid, but not reliable, or scales that were reliable, but not valid.

    With Messicks (1989) unified approach to construct validity, issues of reliability are

    factored into judgments of validity. Reliabilities for the achievement goal scales for this

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    Validity and Motivation to Learn 10

    sample are in line with previous work, which usually shows high reliability as assessed

    by indices of internal consistency (!".85) for the mastery and performance approach

    scales, but lower reliability for the avoidance scales. Table 3 shows reliabilities for both

    waves, presented separately for the middle and high school students.

    Consequential Evidence

    A critical component of construct validity for our project concerns the intended

    and unintended consequences of score use. Messicks discussions dealt with performance

    assessments or standardized tests andthe use of those scores for assessment or placement

    decisions. Motivation-related data are not typically associated with these same kinds of

    immediate consequences for students, however the nature of our partnership is not

    typical. Much of our work is focused on disseminating data to teachers through

    professional development that targets change in teacher practice to support adaptive

    motivation for students. Figure 1 presents a sample report generated for a participating

    school. Such reports are used in professional development workshops to help teachers

    and schools find areas of strength and weakness from a motivational perspective. The

    decision to present these results in their full complexity was made in response to a

    general tendency to oversimplify motivation in the classroom. For example, teachers

    would report that their students were simply unmotivated. Therefore, one aim of our

    project was to show that there were different ways for students to be motivated (and

    unmotivated), and that these different ways required different interventions from teachers

    when problems arose.

    Results were presented to teachers separated by course, because teachers wanted

    to address motivational concerns in course-alike teams. The quality of the motivational

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    Validity and Motivation to Learn 11

    problems with Algebra 1 students was quite different from the quality of the problems

    with Pre Calculus students. In response, we structured the reporting of data to support

    these conversations.

    The intended consequences of the use of these scores included changes in the

    content of professional development and changes in the schools action plans. Along with

    the graphs, we provided interpretations designed to facilitate discussion. This is an

    excerpt describing Algebra 1A students in middle school A:

    Algebra 1A students started the year with the lowest scores on interest, mastery,and efficacy (e.g., they saw math as less interesting than other math students, they

    were less likely to focus on understanding, and were the least confident in theirmath ability). However, they saw math as just as useful as other students in the

    school, and had similar levels of focus on competition.

    o Change Algebra 1A students had a more adaptive pattern of change thanother students at this school; the drops were generally smaller than for

    students in other courses. They saw math as less useful and were less

    focused on learning but slightly more confident in their math ability.

    o Goals for next year Help students see how math is useful, and moreimportantly, use TARGET TIpS to help focus students on learning and

    developing (rather than just demonstrating) ability.

    With 14 different schools as collaborators, we have seen variation in the degree to

    which the motivational data have had consequences in terms of teacher practices, and

    student outcomes. In some schools, detailed yearly action plans have been revised to

    include a focus on motivation. In one school the drop in student interest presented the

    biggest concern for teachers, and the math department has made supporting student

    interest a major focus. It is more difficult to report at this point on the unintended

    consequences of score use, but such questions will be important to examine as we

    continue to examine the construct validity of our set of motivation measures.

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    Validity and Motivation to Learn 12

    Evidence of Generality of Meaning

    This component of construct validity concerns how the scores on an instrument

    generalize to other populations and contexts. Of particular interest for this set of

    motivation measures are characteristics of the sample. This study included an ethnically

    diverse sample of 6th

    through 12thstudents, and we found the decline in motivation across

    middle and high school reported in other studies (e.g., Anderman, Maehr, & Midgley,

    1999). Figures 2 and 3 show beginning and end of school year motivation profiles,

    separated for each grade level.

    Students showed expected drops in motivation over the course of the school year.

    They became less interested, saw math as less useful, and felt less confident in their

    ability to understand math. In addition, they reported lower levels of achievement goals,

    with lower means on all three goals. While a decreased focus on mastery goals of

    learning and understanding is problematic, the associated decrease in a focus on

    competition should be considered an adaptive change.

    The overall decrease in motivation across the school year played a smaller role in

    professional development than the variability we found across schools and between

    courses. Looking at variability across courses formed the basis for much of the dialogue

    during professional development activities. For example, sixth graders were particularly

    disadvantaged over the school year, with the greatest drop-offs over the year. They were

    less interested, considered math less useful, and were less confident in their math

    abilities. A positive change was the decreased focus on competition and not looking

    incompetent, but this was accompanied as well by less of a focus on learning and

    understanding. Targeted professional development with sixth grade teachers has focused

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    Validity and Motivation to Learn 13

    on supporting mastery goals, interest, and value over the school year. Other questions of

    generality of meaning concern possible gender or ethnic differences, or even domain-

    specific differences, in the structure of these constructs. As data from different

    populations and science intervention projects becomes available, more evidence of the

    generality of these measures will be investigated.

    Discussion

    Exploratory factor analysis, reliability analysis, and figures showing grade-level

    differences are offered here as substantive, structural, and generality of meaning evidence

    in support of the construct validity of this set of measures of motivation to learn. Further,

    a discussion of the ways in which these data have been reported and used serves as

    consequential evidence. Though these sources of evidence have been separated for clarity

    of discussion, it is important to remember that there exists considerable overlap, and that

    these aspects of construct validity are part of a unified validity theory that does not rely

    on nor require any one form of evidence.

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    Validity and Motivation to Learn 15

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    Table 1

    Factor Loadings for Task Value Measures at Beginning of School Year (N = 8,429)

    1 2 3 41. I enjoy doing math. 0.952. I like math. 0.933. I enjoy the subject of math. 0.884. How much do you like doing math? 0.855. Math is exciting to me. 0.836. I am fascinated by math. 0.757. Math will be useful for me later in

    life. 0.948. Math concepts are valuable because

    they will help me in the future.

    0.83

    9. How useful is learning math for whatyou want to do after you graduate andgo to work? 0.71

    10. In general, how useful is what youlearn in math? 0.60

    11. Being good at math will be importantwhen I get a job or go to college. 0.52

    12. Compared to most of your otherschool subjects, how useful is what

    you learn in math? 0.4613.

    I have to give up a lot to do well inmath. 0.79

    14. Success in math requires that I giveup other activities I enjoy. 0.77

    15. It is important for me to be someonewho is good at solving problems thatinvolve math. 0.79

    16. Being someone who is good at mathis important to me. 0.79

    17. Being good at math is an importantpart of who I am. 0.74

    18. It is important to me to be a personwho reasons mathematically. 0.63

    19. I feel that, to me, being good atsolving problems which involve math

    or reasoning mathematically is 0.6120. Thinking mathematically is an

    important part of who I am. 0.55Note: Factor loadings under .20 have been omitted.

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    Validity and Motivation to Learn 18

    Table 2

    Factor Loadings for Task Value Measures at End of School Year (N = 8,429)

    1 2 3 41.I enjoy doing math. 0.962.I like math. 0.943.I enjoy the subject of math. 0.864.How much do you like doing math? 0.865.Math is exciting to me. 0.836.I am fascinated by math. 0.767.Math will be useful for me later in life. 0.968.Math concepts are valuable because

    they will help me in the future. 0.889.How useful is learning math for whatyou want to do after you graduate and

    go to work? 0.7510. In general, how useful is what you

    learn in math? 0.6511. Being good at math will be

    important when I get a job or go to

    college. 0.6112. Compared to most of your other

    school subjects, how useful is whatyou learn in math? 0.51

    13. I have to give up a lot to do well inmath. 0.8314. Success in math requires that I give

    up other activities I enjoy. 0.8215. It is important for me to be someone

    who is good at solving problems that

    involve math. 0.8116. Being someone who is good at math

    is important to me. 0.8017. Being good at math is an important

    part of who I am. 0.8018. It is important to me to be a personwho reasons mathematically. 0.7319. I feel that, to me, being good at

    solving problems which involvemath or reasoning mathematically is 0.65

    20. Thinking mathematically is animportant part of who I am. 0.63

    Note: Factor loadings under .20 have been omitted.

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    Table 3

    Reliabilities for Efficacy, Task Value, and Achievement Goal Measures for Beginning of

    School Year (N = 8,429)

    Middle School High School CombinedInterest Value

    0.95 0.96 0.96Utility Value

    0.87 0.84 0.87Attainment Value

    0.87 0.86 0.87Cost Value

    0.79 0.71 0.75Personal Mastery Approach Goals

    0.86 0.87 0.87Personal Performance Approach Goals 0.84 0.84 0.84Personal Performance Avoid Goals

    0.80 0.78 0.79Efficacy

    0.89 0.87 0.88

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    Table 4

    Reliabilities for Efficacy, Task Value, and Achievement Goal Measures for End of School

    Year (N = 8,429)

    Middle School High School CombinedInterest Value

    0.95 0.96 0.95Utility Value

    0.90 0.88 0.89Attainment Value

    0.90 0.88 0.89Cost Value

    0.83 0.78 0.81Personal Mastery Approach Goals

    0.88 0.88 0.88Personal Performance Approach Goals 0.86 0.86 0.86Personal Performance Avoid Goals

    0.84 0.82 0.83Efficacy

    0.91 0.91 0.91

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    Figure 1

    Sample School Report

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    Figure 2

    Cross-sections of Beginning-of-Year Motivation Profiles for Middle and High School

    Students (N = 8,429)

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    Validity and Motivation to Learn 23

    Figure 3

    Cross-sections of End-of-Year Motivation Profiles for Middle and High School Students

    (N = 8,429)