Calibrating a measure of gender differences in motivation for learning technology.

Calibrating a measure of gender differences in motivation for learning technology. This paper reports on the theory, design, and calibration calibration/cal��i��bra��tion/ (kal?i-bra��shun) determination of the accuracy of an instrument, usually by measurement of its variation from a standard, to ascertain necessary correction factors. of aninstrument for measuring gender difference in motivation for learningtechnology. The content of the instrument was developed based upon themotivational theories The introduction to this article provides insufficient context for those unfamiliar with the subject matter.Please help [ improve the introduction] to meet Wikipedia's layout standards. You can discuss the issue on the talk page. of Eccles and others. More specifically, thelearners' self-concept of ability, perception of technology,perception of parental beliefs, causal attributions (success andfailure), value factors, and gender issues in using technology wereinvestigated. The function of the instrument was evaluated according to according toprep.1. As stated or indicated by; on the authority of: according to historians.2. In keeping with: according to instructions.3. the principles of Measurement theory, using a Rasch rating scalemeasurement model. ********** The Global society is increasingly technological. Consequently,keeping pace with other industrialized in��dus��tri��al��ize?v. in��dus��tri��al��ized, in��dus��tri��al��iz��ing, in��dus��tri��al��iz��esv.tr.1. To develop industry in (a country or society, for example).2. nations requires educators toensure that students who are future entrants into the workforce have thenecessary knowledge and skill in learning and using technology (Gore,1999). Unfortunately, the number of American students pursuing degreesin the sciences or technology is smaller than those in the poorestdeveloping nations (Committee on Equal Opportunities in Science andEngineering, 2000). Within the American population, it is apparent that there is asignificant gender gap in pursuing scientific and technical careers.According to Fountain's report (1999), from 1984 to 1999 thepercentage of undergraduate computer science degrees awarded to womenhas decreased from 37% to less than 20%. The rate of female and malehigh school students completing the advanced levels of mathematics andscience courses is almost same. More than half of female students tookAdvanced Placement tests. However, only 10% of computer science testtakers in 1999 were female. Why are women choosing not to pursue technology careers? Althoughmany factors contribute to the gender gap in pursuing careers intechnology, one is of particular interest to educators and researchers:motivation. In the past decade a number of mathematics and scienceresearchers have identified the important role of motivational factorsrelated to students' career choices. According to expectancy-valuetheories IntroductionExpectancy-value theory was originally created in order to explain and predict individual's attitudes toward objects and actions. Originally the work of psychologist Martin Fishbein, the theory states that attitudes are developed and modified based on assessments (Eccles, 1983,1987, 1994; Eccles & Wigfield, 1995; Wigfield1994; Wigfield & Eccles, 2000), motivation to perform a certain taskis strongly influenced by one's expectation of success or failureand the value or appreciation the individual places on the task. Forexample, those who believe that using technology/computer is importantor easy are more likely to desire a career in a technology/computerfield and outperform OutperformAn analyst recommendation meaning a stock is expected to do slightly better than the market return.Notes:Exact definitions vary by brokerage, but in general this rating is better than neutral and worse than buy or strong buy. those who do not hold such beliefs. Theexpectancy-value model has been shown to predict students' careerchoices and their academic performances in different subject matters(Eccles, 1994; Pintrich & Schunk, 1996). However, compared to thenumerous studies on the academic subjects of mathematics and reading,there has been little research on gender differences in motivation ofusing technology. Therefore, our understanding of gender imbalance intechnology career choices is still largely based upon researchconcerning motivation in mathematics and reading. As a first step toward understanding what women's technologyrelated motivations may be, the present study investigates the functionof an instrument for measuring motivational factors in learningtechnology. The content of the instrument was developed based upon themotivational theories of Eccles and others (Eccles, 1983,1987,1994;Eccles & Wigfield, 1995; Pintrich & Schunk, 1996; Weiner, 1985,1992, 1995; Wigfield 1994;Wigfield & Eccles, 2000).The function ofthe instrument was evaluated according to the principles of Measurementtheory, using a Rasch rating scale measurement model. Achievement expectations and value theories can be conceptualizedin terms of five components: tasks values, students' perceptions oftheir own ability, perceptions of parental beliefs, perceptions oftechnology, and causal attribution at��tri��bu��tion?n.1. The act of attributing, especially the act of establishing a particular person as the creator of a work of art.2. patterns for success and failure. Task Values Value of a task is one of the important determinants in whetherindividuals enroll and engage in the task. People have a tendency toperform tasks when they hold positive value in them but avoid tasks whenthey negatively value (Atkinson, 1957; Eccles et al., 1983; Feather,1982). There are three major components of task value: Attainment,intrinsic, and utility values (Eccles & Wigfield, 1995; Meece,Parsons Parsons,city (1990 pop. 11,924), Labette co., SE Kans.; inc. 1871. It is a shipping point for dairy products, grain, and livestock. Manufactures include ammunition, wire and paper products, plastics, and appliances. , Kaczala, Goff, & Futterman, 1982). Attainment value refers to the individual's perception of theimportance of doing well on a specific task. Individuals affirm orquestion prominent characteristics of the self by engaging in anintellectually challenging task. For example, those who have a highattainment value of technology would perceive using technology as animportant and challenging task that they believe smart students shoulddo well. Eventually, the individuals' valued components inself-concept of technology would be confirmed by doing well in usingtechnology. Intrinsic value Intrinsic Value1. The value of a company or an asset based on an underlying perception of the value.2. For call options, this is the difference between the underlying stock's price and the strike price. stems from internal, personal factors such asinterests and enjoyment or curiosity, whereas extrinsic value Extrinsic ValueThe difference between an option's price and the intrinsic value.Notes:For example, an option that has a premium price of $10 and an intrinsic value of $5 wouldhave an extrinsic value of $5. isdirected by observable ob��serv��a��ble?adj.1. Possible to observe: observable phenomena; an observable change in demeanor.See Synonyms at noticeable.2. external factors such as rewards, punishment, orpeer/parents pressures (Deci & Ryan, 1986). A collective of pastfindings has shown that students who are primarily intrinsicallymotivated persist longer, tend to seek out and conquer more challenges,and show deeper knowledge and higher academic outcomes than those whoare extrinsically motivated (Ames, 1992; Deci & Ryan, 1986; Dweck& Leggett, 1988; Elliott & Dweck, 1988; Nicholls, 1984; Pintrich& De Groot, 1990; Pintrich & Garcia, 1991). Task-involvement issimilar to intrinsic value in that one values learning as an end initself and focuses on the material to be learned (Nicholls, 1978, 1984).In contrast, ego-involvement is highly related to extrinsic value. Thefocus of an ego-involved learner is on the self perception as well asothers' perception of one's work. Finally, utility value is related to reaching a variety of futuregoals, apart from any feelings of enjoyment. Students may take advancedcomputer courses in order to get a promising job even though they arenot interested in computers. In this case, the value of learningcomputers is high because the value motivates individuals to undertakethe task as a means for reaching their goals. Self-Concept of Ability Self-concept of ability is the process and product of theself-evaluation of individuals' competencies to perform specifictasks and is influenced by an interpretation of their behavior and theirperceptions of others' attitudes and expectations for themselves(Eccles, 1983). Several studies (Eccles et al., 1983; Sherman, 1980;Sherman & Fennema, 1977; Armstrong, 1980) related to courseselection found that those who perceived themselves as having high mathability are more likely to take optional math courses than those who hadlow self-concept. However, recent studies (Stevenson & Lee, 1990;Whang & Hancock, 1994; Whang & Hwang, 1996) on Asian American A��sian A��mer��i��canalso A��sian-A��mer��i��can ?n.A U.S. citizen or resident of Asian descent. See Usage Note at Amerasian.A self-concept of ability showed that students who were outperformed onmath tended to report low self-concept of ability. According to studiesdone by Stevenson and Lee (1990), and Whang and Hancock (1994), lowself-concept of abilities may be due to students' high standards,which motivate them to work hard. Perceptions of Parental Beliefs The achievement literature documented there was a high correlationbetween students' self-concepts of ability and their perceptions ofparents beliefs about their abilities (Fennema & Sherman, 1977;Eccles et al., 1983; Parsons, Adler & Kaczala, 1982; Whang &Hancock, 1994; Whang & Hwang, 1996). These studies found thatstudents who had positive perceptions of their parental expectationstended to take advanced courses. According to Parsons, Adler, andKaczala (1982), socialization socialization/so��cial��iza��tion/ (so?shal-i-za��shun) the process by which society integrates the individual and the individual learns to behave in socially acceptable ways. so��cial��i��za��tionn. factors such as peers, teachers, andparents have effects on students' abilities not so much through thesocializers' attitude itself but through students'expectations of the socializers' perceptions. Hence, studyingone's perceptions of parental beliefs is important to understandindividual's choice and achievement. Perceptions of Technology People form their perceptions of a certain task based uponinterpretations of reality (Whang & Hancock, 1994). One'snegative experiences related to using technology, includingparents' and teachers' attitudes may have an effect ondeveloping a misconception mis��con��cep��tion?n.A mistaken thought, idea, or notion; a misunderstanding: had many misconceptions about the new tax program. of technology. Eventually, the inaccuracy in��ac��cu��ra��cy?n. pl. in��ac��cu��ra��cies1. The quality or condition of being inaccurate.2. An instance of being inaccurate; an error. ofself-concept may reflect in the student's task behavior (Frank,1988). Causal attributions According to attribution theory Attribution theory is a social psychology theory developed by Fritz Heider, Harold Kelley, Edward E. Jones, and Lee Ross.The theory is concerned with the ways in which people explain (or attribute) the behavior of others, or themselves (self-attribution), with something (Weiner, 1985, 1992, 1995), thecausal perceptions an individual holds about an event have an importantdeterminant determinant,a polynomial expression that is inherent in the entries of a square matrix. The size n of the square matrix, as determined from the number of entries in any row or column, is called the order of the determinant. of subsequent action. The following are three dimensions forclassifying all attributions: Locus Locus - A distributed system project supporting transparent access to data through a network-wide file system. of causality causality,in philosophy, the relationship between cause and effect. A distinction is often made between a cause that produces something new (e.g., a moth from a caterpillar) and one that produces a change in an existing substance (e.g. , stability, andcontrollability. Locus of causality defines the location of the cause ofan event as internal or external to the individual. Stability defines anevent's cause as constant or changeable. Finally, controllabilityrefers to the extent to which an individual influences the cause of anevent. It is generally assumed that those who attribute their failure toexternal, stable, and uncontrollable factors such as difficult tasks orbad luck are more likely to consider the perceived barriers as permanentobstacles to their success rather than overcome the barriers. Incontrast, those who believe their failure is caused by internal,unstable, and controllable factors such as lack of effort are apt toperceive the barriers as an important opportunity for success and seekbetter strategies to prevail over them. In sum, much research has been done in examining how students'expectancy-value influences their academic career choices andeducational behaviors. However, in comparison to the numerous studies ongender differences in mathematics, there has been little research ontechnology. A necessary step in studying gender differences intechnology is to develop and test an instrument for measuringmotivational factors in technology use. This present study thereforeexamined the functioning of an instrument for measuring junior highschool students' motivation to learn about technology. Data wereanalyzed an��a��lyze?tr.v. an��a��lyzed, an��a��lyz��ing, an��a��lyz��es1. To examine methodically by separating into parts and studying their interrelations.2. Chemistry To make a chemical analysis of.3. using a combination of Rasch measurement models (Luce &Tukey, 1964; Krantz Krantz is the name of two persons: Kermit E Krantz Physician and inventor Grover Krantz Bigfoot researcher , Luce, Suppes, & Tversky, 1971; Michell, 1990,1997; 1999). More specifically, the learners' self-concept ofability, perception of technology, perception of parental beliefs,causal attributions (success and failure), value factors, and genderissues in using technology were investigated. If the variables can bemeasured quantitatively, as shown via data fit to a fundamentalmeasurement model, further research will examine potential sources ofdifferences in learning and using technology between female and malesixth graders. Method Participants The participants for this study were 129 sixth grade studentsrecruited from a public junior high school in a southern state city. Themean age for the students was 10.4 years. Gender was consistentlydistributed (65 males and 64 females). Informed consent was obtainedprior to the collection of data from both parents and children. Instrument The survey used in this study was revised based on Whang andHancock (1994) and current motivational theories (Eccles, 1983, 1987,1994; Eccles & Wigfield, 1995; Pintrich & Schunk, 1996; Weiner,1985, 1992, 1995; Wigfield 1994; Wigfield & Eccles, 2000). The 36items in the survey, thought to bear on a measure of motivation to learnabout technology, involve self-concept of ability (6 items), perceptionof technology (4 items), intrinsic casual attributions (4 items),extrinsic EVIDENCE, EXTRINSIC. External evidence, or that which is not contained in the body of an agreement, contract, and the like. 2. It is a general rule that extrinsic evidence cannot be admitted to contradict, explain, vary or change the terms of a contract or of a casual attributions (3 items), task-involved motivation (3items), ego-involved motivation (3 items), parent's perception (7items), female gender issues (3 items), and male gender issues (3items). These items are constructed using a Likert scale Likert scaleA subjective scoring system that allows a person being surveyed to quantify likes and preferences on a 5-point scale, with 1 being the least important, relevant, interesting, most ho-hum, or other, and 5 being most excellent, yeehah important, etc anchored byvery true (1) and not true at all (4).All of the questions are listed inthe Appendix. Procedure The survey was administered orally to groups of students in theirhomerooms using standardized standardizedpertaining to data that have been submitted to standardization procedures.standardized morbidity ratesee morbidity rate.standardized mortality ratesee mortality rate. instructions by a trained graduate student.Participants were told that the purpose of the study was to learn theirpoint of view on learning and using technology/computer and how theyfeel about their ability in the particular field. Approximately 30 minwas needed to complete the questionnaire. Care was taken through thesurvey to give no indication of what would be considered the appropriateor "right" answer. If the student did not understand astatement, it was repeated or paraphrased in simpler language, but stillno examples or suggested answers were given. Analysis Fundamental measurement theory (Luce & Tukey, 1964; Krantz,Luce, Suppes, & Tversky, 1971; Michell, 1990, 1997; 1999) provides astrong program for testing the hypothesis that a given variable isquantitative. According to Mundy (Mundy, 1986, p. 392), the hallmark ofa meaningless proposition is that its truth-value depends on what scaleor coordinate system coordinate systemArrangement of reference lines or curves used to identify the location of points in space. In two dimensions, the most common system is the Cartesian (after René Descartes) system. is employed, whereas meaningful propositions havetruth-value independent of the choice of representation, within certainlimits. Fundamental measurement theory's approach to testing thequantitative hypothesis examines the extent to which mathematicalpropositions concerning the variable will be meaningful or meaningless.In other words Adv. 1. in other words - otherwise stated; "in other words, we are broke"put differently , fundamental measurement theory establishes whether aconstruct's quantitative expression depends upon the particulartest or survey questions asked (what might be called the"brand" of instrument) and/or upon the particular sample ofpersons responding to the questions. A particularly easy to use and convenient way of testing thequantitative hypothesis is via fit to a probabilistic (probability) probabilistic - Relating to, or governed by, probability. The behaviour of a probabilistic system cannot be predicted exactly but the probability of certain behaviours is known. Such systems may be simulated using pseudorandom numbers. conjoint con��joint?adj.1. Joined together; combined: "social order and prosperity, the conjoint aims of government"John K. Fairbank.2. Raschmeasurement model (Rasch, 1960; Perline, Wright, & Wainer, 1979;Wright & Masters, 1982; Andrich, 1988; Fisher &Wright, 1994;Wright, 1999). When data fit one of these models, the axioms This is a list of axioms as that term is understood in mathematics, by Wikipedia page. In epistemology, the word axiom is understood differently; see axiom and self-evidence. Individual axioms are almost always part of a larger axiomatic system. ofsimultaneous conjoint measurement and the meaningfulness criterion aresatisfied. The model employed here is the rating scale model ln([p.sub.nij]/(1-[p.sub.nij])) = [b.sub.n] - [d.sub.i] - [k.sub.j] read as the natural logarithm Natural logarithmLogarithm to the base e (approximately 2.7183). of the odds (p / (1-p)) thatdifference between the ability b of person n is greater than thedifficulty d of item i at the level k posed by category j. Surveyapplications typically assume that measures are not affected by anyfactors other than the properties of the questions asked and theattitudes or abilities of the respondents. Fundamental measurementmodels do nothing more than check these assumptions for their tenability ten��a��ble?adj.1. Capable of being maintained in argument; rationally defensible: a tenable theory.2. in the face of actual observations. Given the sample size of 129 and the 36-item instrument, overallsuccess in the measurement effort will be indicated when the standarddeviations In statistics, the average amount a number varies from the average number in a series of numbers. (statistics) standard deviation - (SD) A measure of the range of values in a set of numbers. of the information-weighted and outlier-sensitive model fitstatistics are less than 2.0, and when individual scores function assufficient statistics (i.e., the pattern of responses across the itemsfor a person, or the pattern of responses across the persons for anitem, is reproducible from the score alone) (Smith, 1986, 1998; Wright& Masters, 1982). Results Table 1 shows the summary statistics for the student measures andthe item calibrations. The data matrix of 129 students times the 36items contains 4,644 possible observations; 98.8% of these (4,587) arepresent. Raw scores range from 67 to 115, within the maximum range of 36to 144. On average, the students responded to 35.6 questions. Overall model fit appears acceptable for the item calibrations,given that the standard deviations of the standardized fit statisticsare considerably less than 2.0. Several students, however, appear to have provided inconsistentresponses, as the maximum information-weighted (infit) andoutlier-sensitive (outfit) statistics are 5.7 and 5.2, respectively.Examination of the residuals indicates several highly unexpectedresponses to items 21 and 10, which ask the students about theirparents' opinions concerning girls' need to study technology(21) and concerning whether the respondent In Equity practice, the party who answers a bill or other proceeding in equity. The party against whom an appeal or motion, an application for a court order, is instituted and who is required to answer in order to protect his or her interests. hates technology (10). Modeled reliability, at .62, allows for barely 2 statisticallydistinct strata, measurement ranges with centers at least three errorsapart (Wright & Masters, 1982). The average measurement error of .19is close to that predicted by Rasch generalizability theory Generalizability theory (G Theory) is a statistical framework for conceptualizing, investigating, and designing reliable observations. It was originally introduced by Lee Cronbach and his colleagues. (Linacre,1994/1989) for a survey of 36 four-category items, but the lowmeasurement standard deviation (SD) of .29 makes for a measurementseparation (ratio of the SD to the error) of only 1.29. Table 2 shows the summary statistics for the rating scalecategories. Note that the observed counts in each category range from749 to 1714, and the step calibrations are not ordered from less to moreas the category labels progress from 1 (true) to 4 (not true at all). The distribution of the measures averaging .18 logits isillustrated in Figure 1, showing that the measures are most closelyaligned with the calibrations of the items on the average step of therating scale, which approximates the step from Somewhat True to Not VeryTrue. The average measure is about one error above the center of theitem scale (0.0). Were the instrument better targeted, error would besomewhat lower and reliability higher. Figure 2 shows the items in measure order, along with the positionsof the category transitions on the number line. The statements at thebottom of the figure are rated least true, and those at the top, mosttrue. Students rate the assertion that their parents do not valuetechnology education important for girls untrue un��true?adj. un��tru��er, un��tru��est1. Contrary to fact; false.2. Deviating from a standard; not straight, even, level, or exact.3. Disloyal; unfaithful. (item 21), and they denylearning about technology to prevent getting in trouble (item 9).Students also tend to find statements concerning the technical gendersuperiority of either boys or girls untrue. At the other end of the continuum, statements that the studentsfind very true involve learning about technology because it isinteresting, obtaining good technology course grades because of hardwork, expecting good grades in technology classes, and the importanceparents place on learning about technology. Figure 3 shows a box plot of the items' calibration values bythe theoretical construct groupings. Items involving task-orientation,ability self-concepts, perceptions of technology, and internal causalattributions are rated most true, whereas statements concerningego-involved motivation, external causal attributions, and gender issuesrated least true. Analysis of the measure, error, standardize stan��dard��izev.1. To cause to conform to a standard.2. To evaluate by comparing with a standard. fit statistic statistic,n a value or number that describes a series of quantitative observations or measures; a value calculated from a sample.statistica numerical value calculated from a number of observations in order to summarize them. , andpoint biserial correlation Noun 1. biserial correlation - a correlation coefficient in which one variable is many-valued and the other is dichotomousbiserial correlation coefficient variances (via ANOVA anovasee analysis of variance.ANOVAAnalysis of variance, see there ) revealed fewrelationships that could clearly be interpreted as statistically orsubstantively significant. The most suggestive sug��ges��tive?adj.1. a. Tending to suggest; evocative: artifacts suggestive of an ancient society.b. findings indicate thatthe information-weighted fit statistics are elevated for black males andHispanic females (there were no Hispanic male respondents and only twoHispanic females), meaning that the survey may not validly measureattitudes toward technology for these groups. Discussion and Conclusions The present study examined the functioning of the instrument formeasuring junior high school students' self-concept of ability,perception of technology, perception of parental beliefs, causalattributions, value factors, and gender issues in technology by using acombination of Rasch measurement models. Based on the results of the study, it seems that our attempt todevelop an instrument to measure motivation in learning about technologyattained a limited degree of success. The homogeneity HomogeneityThe degree to which items are similar. of the measures,as indicated by the low SD, and the high model fit statistics suggestthat further research into the construct and the items bearing on it maybe warranted. In many respects, the technology attitude questionnaireitems behaved fairly well as measures of the components ofexpectancy-value model in learning about technology according to thecombination of Rasch measurement models (Luce & Tukey, 1964; Krantz,Luce, Suppes, & Tversky, 1971; Michell, 1990,1997; 1999). However,eight of the items exhibited low point-biserial correlations. Removingthe four worst-fitting cases and another four individual observations onitem 21 reduced the standardized outfit SD from 2.1 to 1.8, and thestandardized infit SD from 2.3 to 2.2. Ideally, each category's probability curve should have its ownpeak, indicating the point at which it is the most probable response fora given range of measures. The curves in Table 2 indicate thatcategories 2 and 3 are never the most likely response. Respondentsapparently cannot distinguish four separate degrees of truth in thesestatements. Future analyses of these data should explore various ways ofcombining categories to linearize lin��e��ar��ize?tr.v. lin��e��ar��ized, lin��e��ar��iz��ing, lin��e��ar��iz��esTo put or project in linear form.lin the step calibrations. And if theinstrument is to be administered to another sample, the response optionsought to be expanded from the current four to six, with the categorylabels modified to more clearly demarcate de��mar��cate?tr.v. de��mar��cat��ed, de��mar��cat��ing, de��mar��cates1. To set the boundaries of; delimit.2. To separate clearly as if by boundaries; distinguish: demarcate categories. increasing amounts. Forinstance, an Agree/Disagree continuum, with "Very Strongly","Strongly", and "Mildly" as modifiers, might moreclearly convey a wider range of possible responses than the currentlabels' focus on truth. [FIGURE 1 OMMITTED] [FIGURE 2 OMITTED] [FIGURE 3 OMITTED] [FIGURE 4 OMITTED] Appendix Technology/Computer Attitude Questionnaire Items 1. My classmates Classmates can refer to either: Classmates.com, a social networking website. Classmates (film), a 2006 Malayalam blockbuster directed by Lal Jose, starring Prithviraj, Jayasurya, Indragith, Sunil, Jagathy, Kavya Madhavan, Balachandra Menon, ... are better at using technology/computer than I am. 2. My parents think that learning more about technology/computer isnot important for boys. 3. Technology/computer is easy to learn. 4. I am good in using technology/computer because I work hard. 5. I work harder in using technology/computer than the otherstudents do. 6. The reason I try to learn how to use technology/computer isit's interesting. 7. My parents think it is very important for me to do well in usingtechnology/computer. 8. I expect good grades (high percentages) in computer/technology. 9. The reason I try to learn how to use technology/computer is thatI'll get in trouble if I don't. 10. My parents think I hate technology/computer. 11. Boys are better at using computers/technology. 12. Technology/computer projects are time-consuming. 13. I am good in using technology/computer because I am lucky. 14. Using technology/computer helps me study other subjects. 15. The reason I try to learn how to use technology/computer isthat I don't want to look dumb. 16. My parents help me to use technology/computer. 17. Girls are better at using computers/technology. 18. Technology/computer projects are difficult for me. 19. When I get a good grade in technology/computer it'sbecause I work hard. 20. I feel I can do better in technology/computer. 21. My parents think that learning more about technology/computeris not important for girls. 22. Technology/computer is mainly memorization mem��o��rize?tr.v. mem��o��rized, mem��o��riz��ing, mem��o��riz��es1. To commit to memory; learn by heart.2. Computer Science To store in memory: . 23. To do well in using technology/computer is very important tome. 24. My parents think that I can do better in technology/computer. 25. Using technology/computer requires me to think. 26. I am happy with the grades I get in technology/computer. 27. When I get a good grade in technology/computer it'sbecause it's a matter of luck. 28. The reason I try to learn how to use technology/computer is tomake the teacher think I'm a good student. 29. When I get a bad grade in technology/computer it's becauseI didn't study hard enough. 30. My parents think that using technology/computer is difficultfor me. 31. Girls are better at using computers/technology because it takesa lot of creative thinking and girls are more creative. 32. When I get a bad grade in technology/computer it's becauseI'm just not good at using technology/computer. 33. My parents think I need to spend more time studyingtechnology/computer. 34. Boys are better at using computers/technology because acomputer is a machine, and they are better with machines. 35. When I get a bad grade in technology/computer it's becauseof careless carelessadj., adv. 1) negligent. 2) the opposite of careful. A careless act can result in liability for damages to others. (See: negligent, negligence, care) mistakes. 36. My parents think that I am one of the best students intechnology use in my class. References Ames, C. (1992). Classrooms: Goals, structures, and studentmotivation. Journal of Educational Psychology, 84, 261-271. Andrich, D. (1988). Sage University Paper Series on QuantitativeApplications in the Social Sciences. 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This manuscript included only original material that has not beenpreviously published and is not under review elsewhere.TABLE 1Technical Motivation Instrument Calibration Summary Statistics129 students, 36 items, 4 CategoriesSUMMARY OF 129 MEASURED PERSONSI RAW REAL SCORE COUNT MEASURE ERRORMEAN 94.0 35.6 .18 .19S.D. 9.6 1.6 .8 .03MAX. 115.0 36.0 .87 .29MIN. 67.0 21.0 -.70 .17 INFIT OUTFIT MNSQ ZSTD MNSQ ZSTDMEAN 1.00 -.4 1.07 -.2S.D. .53 2.3 .75 2.1MAX. 2.79 5.7 4.52 5.2MIN. .33 -4.2 0.32 -3.4REAL RMSE .19 ADJ.SD .21MODEL RMSE .18 ADJ.SD 0.23SEPARATION 1.10 PERSON RELIABILITY .55SEPARATION 1.29 PERSON RELIABILITY .62S.E. OF PERSON MEAN .03VALID RESPONSES: 98.8SUMMARY OF 36 MEASURED ITEMS RAW REAL SCORE COUNT MEASURE ERRORMEAN 336.7 127.4 .00 -.10S.D. 93.6 1.5 .75 .03MAX 487.0 129.0 1.20 .23MIN. 188.0 123.0 -1.54 .08 INFIT OUTFIT MNSQ ZSTD MNSQ ZSTDMEAN 1.04 -.1 -1.07 -.2S.D. .19 1.4 .231MAX 1.64 1.9 2.01 2.6MIN. .67 -4.1 .73 -3.5REAL RMSE .11 ADJ.SD .74MODEL RMSE .10 ADJ.SD .74SEPARATION 6.80 ITEM RELIABILITY .98SEPARATION 7.33 ITEM REQABILITY .98S.E. OF ITEM MEAN .13TABLE 2Technical Motivation Summary of Measured Steps on the Rating Scale129 students, 36 items, 4 CategoriesSUMMARY OF MEASURED STEPSCATEGORY OBSERVED MEASURE COHERENCE LABEL COUNT AVRGE EXPECT M>C 1 1229 -.44 -.47 71% 2 895 -.14 -.11 29% 3 749 .28 -.31 23% 4 1714 .76 .74 81%CATEGORY COHERENCE INFITOUTFIT STEP LABEL C->M MNSQ MNSQ CALIBRATN 1 18% 1.07 1.27 NONE Very True 2 53% 0.86 .80 .02 Somewhat True 3 59% 0.88 .84 .28 Not Very True 4 35% 1.00 1.06 -.30 Not True at AllAVERAGE MEASURE is mean of measures in category.M->C = Does Measure imply Category?C->M = Does Category imply Measure?CATEGORY STEP STEP LABEL CALIBRATN S.E. 1 NONE 2 .02 .04 3 .28 .04 4 -.30 .04CATEGORY SCORE-TO-MEASURE THURSTONE LABEL AT CAT. ----ZONE---- THRESHOLD 1 (-1.52) -INF -.95 Very True 2 -.37 -.95 .02 -.55 Somewhat True 3 .42 .02 .96 .06 Not Very True 4 (-1.47 .96 +INF .52 Not True at All