JOURNAL OF ELEMENTARY SCIENCE EDUCATION VOL, 5, NO. 2, Pp. 37-51, (1993) (C) 1993, Curry School of Education, University of Virginia

PRESERVICE ELEMENTARY TEACHERS' VIEWS TOWARD A SCIENCE METHODS CURRICULUM

By William J. Boone

A b s t r a c t One factor effecting the success of an elementary science methods curricu/um are preservice teachers' perceptions of a course's usefu/ness. /n the fa// of I991, over 100 e/ementary science methods students were administered a survey to assess their viaws toward a curricu/um. Survey resu/ts supp/y a distinct ordering of curricu/ar components. Some c/ass components were viewed in a favorable manner, wht/e others were viewed /ess positive/y. Three c/ass components etŸ unpredictable student responses. Survey resu/ts and imp/ications for reforming this methods course ate presented.

Introduct ion One important aspect of elementary education is the

"science methods ctass" presented to teachers in training, for often such courses provide students with their only exposure to a variety of science teaching techniques. Usually a methods curriculum is built by instructors who carefully select topics they gauge as useful for futura elementary science teachers.

Certainly the success of a methods curriculum, as is true for all curricula, can be influenced by many factors unrelated to subject matter (i.e. teachers, class meeting time); however, this study emphasizes the gathering of basic attitudinal data to provide useful information on students' attitudes toward a curriculum. Once collected, these types of data can be used to evaluate and improve a course.

Previous Research and Goals of this Study A number of researchers have previously developed

and/or utilized attitudinal instruments to supply information helpful for science education effo¡ Enochs and Riggs (1990) usecI a Likert scale to measure the science teaching efficacy beliefs of elementary science teachers. Stefanish and Kelsey (1989) utilized the Shrigley Science Attitude Scale for Preservice Elementary Teachers (Shrigley, 1971) to measure

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Science Methods

the beliefs of preservice elementary science teachers toward science and science teaching. Hartly et al. (1984) employed ah att i tudinal instrument (Shrigley & Johnson, 1974) to investigate, in part, whether differences in preservice teachers' attitudes could be traced to differences between two methods courses.

In an effort to extend the research base involving the collection and evaluation of Likert scale data and to improve elementary science methods courses, this project was conducted to collect and evaluate student attitudes toward a science methods curriculum (Thurstone, 1928). Many researchers have considered how to change student attitudes toward science teaching (i.e. Morrisey, J.T., 1981), but research regarding attitudes toward class topics presented in a science methods course seems to be lacking. Ir students use methods they view as being most useful, then it certainly is important to collect these types of data.

Data Collection At the end of the fall 1991 term ah attitude survey

(topics listed in Table 1) was administered to students completing a science methods course at Indiana University- Bloomington. This course was taken sotely by elementary education majors who were primarily of junior standing and near the end of their formal course work. The instrument asked students to evaluate how important they believed 21 class components to be in preparing them for elementary science teaching. A six step Likert scale (excellent, very good, good, fair, poor, terrible) was provided. The 21 surveyed class elements represented major segments of the course. Many other topics were covered during the semester, but in order to present students with a manageable survey, a limited number of class components were used for survey construction. Surveys were administered during the final week of classes in December of 1991, and were completed by more than 95% of the enrolled students. Ir is important to note that while these students completed this course they were concurrently enrolled in ah elementary field teaching experience. The field experience involved teaching science once a week for four weeks at local elementary schools.

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Science Methods

Table 1

Surveyed Course TopIcs

1) Portfolio Item #1- Look up 5 science books and journals. 2) Portfolio Item #2- Writing an organization or corporation for free

teaching materials. 3) Portfolio Item #3- Writing a national or state teacher's

organization about membership. 4) Portfolio Item #4- The clesign of a bulletin board. 5) Portfolio Item #5- Listing five fielcl trip sites. 6) Attencling the clinosaur lectures in October. 7) Writing 10 single page Science Journals. 8) Writing the Post-Critiques of your 4 fielcl science teaching

experiences. 9) Developing your own lesson plans for 3 field science teaching

experiences. 10) Being supplied with an already made lesson plan for your first

field science teaching. 11) Being provicled with classroom time to refine ancl clevelop your

four field science lessons. 12) Developing your own teaching tools ancl props for the fielcl science

lessons. 13) Developing a science game or learning center for your field

science teaching. 14) Lectures on Cognition (Piaget's findings, how students learn). 15) Lectures on the Scientific Method. 16) Lectures on how to write test items. 17) Your four field science teaching experience. 18) The consumer product lab. 19) The paper-clip and string "pendulum" labs. 20) The electrical circuit labs with aluminum foil, light bulbs, wire

and so on. 21) The university furnishing science supplies (science kit) for the

four field science teaching experiences.

Table 1 lists the survey topics administered to the fall 1991 elementary science methods course. Students who completed this class were concurrently enrolled in a science field teaching experience. Respondents were supplied with six Likerl scale responses lor each topic (excellent, very good, good, fair, poor, terrible). These were selected by students on the basis of whether or not the class aclivity was viewed in light of preparing each student,

Data Evaluatlon The s tochas t i c Rasch mode l (Rasch , 1960) was used to

e v a l u a t e t h e s e data. Th is eva lua t i on t e c h n i q u e was se lec ted b e c a u s e the ord ina l a t t i tud ina l sca le mus t be first c o n v e r t e d to an in terva l scale. Th is s tep can bes t be unde rs tood by not ing that a s tep in a t t i t u d e ' f r o m ' "exce l l en t " to "very good"

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does not necessarily represent the same quantifiable change in attitude as steps from "very good" to "good" (i.e. Thurstone, 1929; Wright and Masters, 1982). For example, in coding attitudinal data using the categories "excellent", "very good", "good", "fair" and "poor" many evaluators assign weights to each response. In this case an "excellent" might be named a "5", while a "very good" is considered a "4" a n d a rating of "good" is assigned a "3". This naming of categories with numbers is fine; however, one can not immediately use these numerical identifiers for statistical calculations. If calculations are made at once, an implicit assumption is made that an attitude of "very good" is indeed equal distant from a view of "excellent" and "good". Therefore, after respondents answer an attitudina] survey such as the one presented here, one must take into consideration that the numbers used to code the data imply ah ordering of attitudes (5 is greater than 4, thus "excellent" is a more supportive statement than "very good"), but not a known spacing.

A basic probabilistic (stochastic) model which can be used to convert "raw scores" of coded responses to true "measures" is presented below. For the sake of this explanation the case involving the evaluation of dichotomous data is presented. In a "rating scale" scenario this can best be viewed as the situation in which responses such as "excellent", "very good", and "good" are all considered a "positive" answer and are coded a s a "1", while responses of "fair", "poor", and "terrible" are judged a s a "negative" answer are coded a s a "0". For the data reported in this paper the formula can be viewed as having been used a number of times for each person and item combination so that the six rating steps could be taken into account. Rating Scale Analysis (Wright and Masters, 1982) presents a detai led discussion that goes beyond the dichotomous case. Rasch (1968), Andersen (1973, 1977) and Barndorff-Nielsen (1978) are additional references which can furnish further information.

Much of the data presented in this paper is reported in terms of "logits" derived from the formula presented betow. What does it mean for a certain survey item to be given a higher Iogit rating than other class components? First off, the general relationship between Iogit values can be seen by Iooking at the "measure Iogit" and "average" columns of table

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Science Methods

2. The "average" column repo¡ the raw average response to each item using the coding 1 (excellent), 2 (very good), 3 (good), 4 (fair), 5 (poor), and 6 (terrible). Thus "lectures on the scientific method" were rated on average as "good" by the students. However, this average raw numerical value must take into consideration that only numerical labels were used to calculate this average va]ue. ThŸ raw average must be converted to a scale that takes the potential unequal spacing in attitudes between rating categories into consideration. That is why reporting the average in terms of "logits" is so important.

Iog (Pni/1-Pni)= Bn - Di

Pni is the probability of a person "n" answering a survey item "i" in a positive manner (excellent, very good, good). Bn is a calculated "attitude" of person "n", while Di is a measure of how "difficutt" it was, in general, for the respondents to positivety respond to the particular survey item "i". The units of Bn, Di, and the left side of the formula are in "logits". This name comes from the fact that the left side of the equation involves the Iogarithm of the odds of a person's response.

The key component of this formula is that it involves probabilities. More specifically, the probability of a particular response is viewed as a predictor of each person's overall "attitude" as determined by their own responses to all the survey items and the overall rating of a particular Ÿ when all respondents' views toward a single item are taken into considerat ion.

What is the implication of one survey item being 1 or 2 Iogits greater than another Ÿ To best understand the meaning of a 1 or 2 Iogit separation one should first consider someone who has ah attitude that is on the fence (they can not make up their mind whether they are positive or negative to a particular statement). If they are exactly "on the fence" with regard to one item their probability of responding with a favorable rating is .5 . For that same person the probability of their furnishing a positive response for an item 1 Iogit more positive is .27 The probability of their answering positively to an item 2 Iogits greater than the first Ÿ is .12, while the probability of their supplying positive views on an item 3

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Iogits higher than the first item is .05 . With each change in 1 Iogit the probability roughly decreases by 50%. The same changes in the probability of a response takes place for this fictitious person when they answer an item that is 1 Iogit below the first item discussed. For this item they have a .73 probability of considering this class component in a positive manner. With each change in 1 Iogit the probability roughly increases by 50%.

Another way to visualize the "meaning" of a "logit" is to also Iook at the traditional "raw score" in table 2. By doing so the relationship between the reported "logit" measure and the "raw average" can be best gauged. Some may ask if one can use "raw average scores" to determine the typical responses circled on a particular survey, why report the data in terms of Iogits? Again- by converting the raw data to Iogits the possible non-equal spacing between categories can be corrected. A careful review of figure 1 reveals that the Iogit spacing between the category boundaries (e.g poor-fair, fair- good) is not equal! If this probabilistic model had not been used, this characteristic of the rating system used in this survey would not have been detected and taken into consideration.

This method of analysis is also well suited for these data because 1) it allows an evaluation of persons and items when data is incomplete (i.e. each survey respondent must not respond to every item), 2) errors of each surveyed item and respondent are reported, 3) statistics which help indicate the relevance of items are provided, and 4) persons and items are plotted on the same scale. The FACETS computer program (Linacre, J.M. and Wright, B.D., 1991) was utilized.

Data Interpretation-ltems In Figure 1 the results of the students' class components

ratings are presented. The class component with the highest Iogit value (Item 6: Dinosaur Lecture) was viewed least favorably by students. This item was rated, on average, as being between "fair" and "poor" by students. Items positioned below this Ÿ (less positive togit calibration) represent class activities viewed in a more favorable manner by students. The item at the base of Figure 1 (ttem 9: Developing Own Lesson Plan for Science Teaching) was viewed most

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Table 2

Science Methods

I t em C a l i b r a t i o n s

I t em Meas. E r ro r Score Count Avg. Ou t - Log i t Log i t f i t

Std, 6 At tending Dinosaur 1,46 . 0.11 419 118 4.6 3

Lec tu res 7 Writ ing Science Joumals 0.54 0.10 340 121 3.8 0 1 Look Up Books or Journafs 0.13 0.10 307 123 3.5 0 3 Write for Membership -0.05 0.:11 289 122 3.4 1

16 Lecture on Test Items -0.34 0.11 261 121 3,2 -1 1 8 Lab-Consumer Product -0.34 0.11 264 122 3.2 -1 1 9 Lab-Pendu lum -0.34 0.11 264 122 3.2 -1 14 Lecture on Cognition -0.49 0.11 250 122 3.0 -4 15 Lecture on Scientif ic -0.54 0.11 246 122 3.0 -3

Method 5 Field Trip List -0.55 0.11 244 120 3,0 0

20 L a b - C i r c ¨ -0.74 0.11 232 123 2.9 0 2 Write for Free Materials -0.83 0.11 224 122 2.8 3

1 0 Suppl ied Lesson Plan -0.96 0.12 201 114 2.8 3 21 Furnished with Science -0.96 0.12 202 115 2.8 2

K i t 8 Wri t ing Post Crit ique -0.98 0.11 213 123 2.7 -2 4 Making a Bulletin Board -1.13 0.11 199 121 2.6 0

1 2 Developing Own Props -1.34 0.12 181 121 2.5 -1 1 1 Pract ice in Class -1.39 0.12 175 118 2.5 0 1 7 Science Teaching -1.70 0.12 152 118 2.3 1

Exper ience 1 3 Developing a Game -1.76 0.12 152 122 2.2 0

9 Developin 9 Lessons -1.88 0.12 143 122 2.2 -1

Legend and Explanatlon for Table 2 ltem- Survey Ÿ number described in rabie 1; Meas. (Logits)- Item measure in Iogits;

Error (Logits)- The standard error of Ihe item measure in Iogits; Score- The raw score given by all survey respondents to a single Ÿ Count- The number of respondents answering a survey Ÿ Av9.- The average raw score made to each survey item by respondents; Outfil SId- Fit stalistic indicate unexpecled students responses. A value greater than 2 is unexpected. See Table 1 for the futl text of each survey item.

The more negative a class componenl's IogJt measure, the more imporlant a component was viewed by survey respondenls. For instance, Ÿ 9 (Developing your own lesson plans for science field teaching) is rated as the most important part of such a class. Item 6 (Attending Dinosaur Lectures) is rated as the least important component cA the elemenlary science melhods class.

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Science Methods

Figure 1

Category Logit Measure

+3

Studenl

~x

Survey Ilem

)Poor +2

)00(

x

X

)0(

XK

XX<~0C~XX XXX~X XX~(X)00(

Fair +1

Go:d

;~0C~0C~0(XX

0 X~(

-1

XXXXX~3(XXX XXXXX XXXXX X;~00000(XX

!6- Dinosaur Lecture

i

7- Writing Science Journals

-2 Very Good X

3- Writing a Teacher's Organization

16- Lecture r 18 & 19- Labs 14 & 15- Lectures~ 5- Fie|d trip lis't"

XXX;~CO0( XXXXXX 20- Lab: Circuir XXXXXX XX~K

X~X ;,0(

2- Write Ior Materials 10- GJven Lesson Plan, 21- Science Kit 8- Writin,q CritŸ of Field Teachin,q 4- Making a Buttetin Board 11- Practice in Class, 12- Dev. Props

13- Dev, Game, 17- Field Experience 9- Develop Lessons for Field Teaching

Figure 1: Survey respondents (X} and the twenly-one class components (numbers and abbrevLaled Ÿ description) calibrated on the same Iogit scale. Aclivities with a calibralion Iower than a particular student are those which the student hada high probability of rating as importanl. Activities wilh a calibration higher than a student ate those the same student had a high probability ol viewing as less important. Average standard error of each item is .11 Iogils. Average standard error of each person is 27 Iogits. Note the importance of converting these data with a probabilistic model can be se,en by the un equal spacing in Iogit caqibralion between the named categories in Ihe first column.

4 4

Science Methods

positively by students with an average rating of approximately "very good". By noting the error present in each calibraled Ÿ true statistical differences in items can be observed. Tables 2 and 3 display summary data for each rated item.

Data In terpretat ion-Persons The ordering and spacing of individuals (Figure 1) can be

interpreted in the same manner as ilems. Methods students plotted at the top (most positive logit) pa¡ of the scale were most suppo¡ of the 21 coª components. For instance, the student (X) at the top of the page (3 Iogits measure) rated each component on average between "very good" and "excellent". Students with Iower measures rated class components less favorably. As was true for the items, even when student calJbration error is taken into consideration, an ordering of students defined by attitude emerges. Table 4 presents summary data of survey takers.

Data Interpretatlon-Persons and Items The Iocation of each person with respect to plotted items

in Figure 1 indicates which items students h a d a high probability of viewing positively (excellent, very good, good), and which items respondents had a high probability of viewing negatively (fair, poor, terrible). For instance, persons wŸ a calibration of 0 Iogits have a 50/50 probability of viewing item 3 negalively or positively. Items plotted above persons with a 0 Iogit measure (a more positive Iogit value) have a probability greater than 50% of being rated as an inadequate course componen! by these students. Items plotted below the 0 Iogit persons have a probability greater than 50% of being rated a s a helpful course component by these students.

Detectlng Student Disagreement With Ciass Component Ratlngs

Table 2 supplies "fit" statistics which help indicate whether students responded in an expected manner to items. By detecting items which generate unexpected student responses, this statistic can greatly aid in the interpretation of these data. For these data an item with a positive fit

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Science Methods

Table 3

Summary Stat ist ics of Item, Cal ibrat ions

Measure Model Average Logi t Error

Mean 2.0 -0.68 O. 11 SD 0.5 0.77 0.00

RMSE=.11 Adj S.D.=.76 Separation=6.83 Reliabi lJty=.98 Fixed (all same) chi-square: 1035.32 d.f.:20 significance: .00 Random (normal distribution) chi-square: 20.01 d.f.:19 significance: .39

Table 4

Summary Stat ist ics of Student Cal ibrat ions

Measure Model Average Logit Error

Mean 1.9 0.00 0.27 SD 0.6 0.84 0.02

RMSE=.27 Adj S.D.=.79 Separation=2.91 Rel iab i l i ty=.89 Fixed (all same) chi-square: 1084.07 d.f.:122 significance: .00 Random (normal distribution) chi-square: 120.32 d.f.: 121 significance: .50

Legend and Explanatlon ror Tablas 3 and 4

Measure Logits- Measure in Iogits; Model Error- The standard error of Ihe measure in Iogits; RMSE- Rool mean square standard error; Adj S.D.- Adjusted Standard Deviation after removing measuremenl error; Separation- Adj SD/RMSE whFch is a measure ol 1he spread of the data (there are roughly seven groups ol items, and three groups ol studenls); Reliability- True Variation/Observed Variation; Fixed (all same)- Tests Ihe hypothesis lhal all items or people are of the same measure (rejecled for both ilems and students); Random (normal dislribution) chi-square- Tests the hypothesis that the set ol items or students can be regarded asa random sample irom anormar dislribulion (not rejecled for eilher sludents or items).

s t a t i s t i c g r e a t e r t h a n 2 i n d i c a t e s t ha t s o m e s t u d e n t s r e s p o n d e d u n e x p e c t e d l y to an i tem w h e n al l o f t he i r o t he r Ÿ responses are c o n s i d e r e d . For ins tance, Ÿ 6 (a t t end ing a d i nosau r tecture) h a d a h igh pos i t ive fit s tat is t ic . Th i s means tha t a l t hough the a v e r a g e ra t ing of th is i tem w a s fa i r /poor , a n u m b e r of i n d i v i d u a l s w h o , f rom a p r o b a b i l i s t i c s t a n d p o i n t ,

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Science Methods

should have rated this as a inadequate course component, did not do so. Having statistics such as these are quite useful in the evaluation of a methods curriculum.

An Analysls of Selected Ciass Components The sixth survey item (attending one dinosaur lecture

offered by paleontologists) was rated as being the least beneficial class component. However, fit statistics indicate that some students viewed this activity positively. This was probably due lo the facl that some preservice teachers considered the attendance of such science lectures potentially beneficial even though the actual presentations were too technicai to clearly furnish practical instructional ideas to teachers. The lectures had been advertised for future and active teachers.

Three class components viewed less negatively than the dinosaur lectures were 7 (Writing science journals), 1 (Looking up 5 science books or journals), and 3 (Writing a national of state organization about membership). Students probably viewed these three activities as busy work which would not immediately prepare lhem for teaching.

The ratings of selected class lectures and labs indicated that students evaluated these parts of the class near the middle of the rating scale (i.e. a rating of "good"). It is interesting to note that class lectures and labs, in general, were all raled in a similar manner.

Two activities, 2 (Writing for Free Teaching Materials) and 10 (Supplied Lesson Plans for First Science Teaching) were rated about the same a s a large number of other items with a "good" rating. However, these two items have high positive fit statistics which indicate unpredictable responses of students toward these two class components. This means that some students thought the idea of writing for free teaching materials and being supplied with a lesson plan for one period of student teaching was great, while others had an opposite outlook. One possible explanation for this mixture of responses may be that some students felt constrained by the supplied lessons. Since students taught a variety of grades and were supplied different lessons a s a function of grade, this misfit might also be due to students disliking the lesson supplied for one or two specific grades. The mixed reaction to

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Science Methods

the writing for free materials probably resulted from the wide range of corporate replies. Some students were mailed a great deal of support material, while others received very little in reaction to their letter.

The surveyed class components 8, 4, 11, 12, 17, 13, and 9 were all viewed very favorably by students. Items 8 and 4 were rated a little less approvingly than the rest of this Ÿ set, but still in a positive manner. It is interesting to note that all these activities involved giving preservice teachers time to develop, practice and critique their own teaching materials.

Implications for this Sclence Methods Course The rated items reveal a fairly well-defined ordering of

class components from the least liked to the most liked. Activities that allowed students to develop materials for field teaching were viewed favorably. Lectures and labs which involved science topics, psychology and testing were viewed as being less beneficial. Those activities viewed as least helpful were those which were probably considered least directly applicable to students' immediate concerns. Responses to one class component (10-Supplied Lesson Plan) suggest that some students liked being given ah initial lesson for the semester's science field teaching experience, while others disagreed- possibly feeling the supplied materials were too constraining.

The following course changes were madeas a result of this study:

1) Although attending a lecture by scientists (Item 6) would seem to be a good curriculum component, required student attendance of future lectures wilt be determined with great care. The goal of encouraging students to attend these lectures was to increase preservice teachers' interaction with scientists, however the presentation was so scientifically technical, preservice teachers possibly became (or remained) skeptical toward this way of preparing for future science teaching. One future lecture topic might involve scientists discussing the importance of elementary science teachers.

2) As part of the methods class students were asked to write 10 single page journals on any science topic. Once a week single page journaFs were collected and later returned with instructor's comments. During the semester some students

48

Science Methods

complained they did not know which science topics to select for each journal. In reaction to the stuclents' ratings, the number of science journals students are asked to write was decreased and specific topics with regard to science and education were supplied for guidance.

3) For the time being, asking students to Iocate science books to familiarize themselves With science resources and writing organizations with regard to membership was removed a s a homework assignment. Students may have considered these components a waste of time. If these activities are reimplemented, the instructor will try to sell students on the importance of familiarity with science education publications and the benefits of science teacher organizations. Creating a field Irip wish list (component 5) was viewed a little more approvingly than the journals. Writing an organization for free leaching materials (component 2) was rated positively. These lwo activities might have been considered as busy work, and rated disapprovingly, but this was not the case. Perhaps students could see clear benefits for future science teaching. These two components are being retained in the present methods curriculum.

4) Lectures on i) constructing tests, ii) cognition and iii) the scientific method have been revamped so students must consider the specific grade they plan to teach. These three components now emphasize the "development" of activities involving the core of each lecture topic as it relates to the grade each preservice teacher wishes to teach.

5) Students will still be furnished with science supplies and a first teaching lesson for their field experience. Since fit statistics have indicated unexpected student ratings regarding the lessons, students are now being encouraged by each section leader not to feel constrained by supplied lesson plans. Students will be invited to add or subtract from supplies as they wish.

Conclusion One important parl of science teacher education is the

curricuEum selected for inclusion in a science teaching methods course. This study shows that rating scale surveys can be easily constructed, administered, and evaluated in order to better understand students' views of course material.

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Science Methods

An ordinal scale can be converted to an interval scale, and error terms for each person and item accurately calculated. In this course, the most highly rated activities were those which involved developing, practicing, and critiquing materials for a concurrent field teaching experience. Those act iv i t ies not v iewed in a favorable manner were ones presumably considered by future science teachers as less relevant to immediate teaching needs. If ir is true that future teachers remember and util ize course material they view as most appropriate while completing a course, then instructors of courses (not just methods courses) must slrive to make the usefulness of topics and activJties apparent to students.

R e f e r e n c e s

Andersen, I=.B. (1973). Conditional inference and models for measuring. Copenhagen: Mentalhygiejnisk Forlag, 1973.

Andersen, E.B. (1977). Sufficient statistics and latent trait modets. Psychometrika, (42), 69-81.

Bamddorff-Nielsen, O. (1978). Information and exponential families in statistical theory. New York: 3ohn Wiley and Sons.

Enochs, L.G. & Riggs, I.M. (1990). Further devetopment of an elementary science teaching efficacy belief instrument: A preservice e]ementary scale. School Science and Mathematics, 90 (8), 694- 706.

Harlly, H., Andersen, H.O., & Enochs, L.G. (1984). Science teaching attitudes and class control ideologies of preservice elementary teachers with and without ear~y field experiences. Sc ience Education, 68(1), 53-59.

Linacre, J.M. & Wright, B.D. (1991). Facets Computer Program, ChJcago: MESA Press.

Morrisey, J.T. (1981). An analysis of studies on changing the attitude of elementary student teachers toward scJence and science teaching. Science Education, 65 (2), 157-177.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen, Denmark: Danmarks Paedogogfske Institut (reprinted, Chicago: University of Chicago Press, 1980).

Rasch, G. (1968). A mathematical theory of objectivity and its consequences for model construction. In Report from European Meeting on Statistics, Econometrics and Management Sciences, Amsterdam.

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Science Methods

Shrigley, R.L. (1971). Scale for measuring science attitude of preservice elementary teachers. Paper presented at the annual convention of the National Science Teachers Association, Washington, D.C.

Shrigley, R.L., & Johnson, T.M. (1974). The attitudes of inservice elementary teachers toward science. Schoot Science and Mathematics, 74(5), 437-446.

Stefanich, G,P. & K.W. Kelsey. (1989). Improving science attitudes of preservice elementary teachers. Science EducatŸ 73(2), 187- 194.

Thurstone, L.L (1928). Attitudes can be measured. American Joumat of Sociology, 33, 529-554.

Thurstone, L.L. & Chave, E.J. (1929). The measurement of attitude. Chicago: University of Chicago Press.

Wright, B.D. & Masters, G.N. (1982). Rating Scale Analysis, Chicago: MESA Press.

William J. Boone, Ph. D., is a psychometrician and Assistant Professor of Science Education at Indiana University-Bloomington.

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