MICROCOPY RESOLUTION TEST CHART

NATIONAL BUREAU OF STANDARD - 1963 -A

ND 164 419

DOCUNEIT INSURE

SO 011 422

AUTHOR Walstad, William B.TITLE Estimatitkthe Effects of Economic Problem Solving in

Elempintary Schools. . i

SPONS AGENCY Bu = ;'Foundation, St. Paui,:fMinn.; Minnesota State

DATECol it on Economic Education, Minneapolis.

PUB Sep 78 .

NOTE 26p.; Paper presented at Annual Meeting of thej National Association of AffiMated Economic Education

Directors (Portland, Oregon, October.6i 1978); Tables1 and 2 may not reproduce clearly due to small printtype of original document

EDRS PRICE MF-$0.83 HC-$2.06 Plus Postage.DESCRIPTORS Academic Achievement; Affective Behavior; Cognitive

Development; *Curriculum Evaluation; *EconomicEducation; Elementary Education; *Inservice' TeacherEducation; Interdisciplinary Approach; *ProblemSolving; Program Evaluation; Research Methodology;Social Studies; Student Attitudes .

IDENTIFIERS *Unified Science Mathematics for ElementarySchools

ABS R1CTThis study evaluates the Unified Sciences and

mathematics for Elementary.Schobl ( USMES) curriculum developed by theEducation Development Center in Massachusetts. Effects of using USMESmaterials on students' economic understanding, attitudes, andproblem-solving ability were explored. The research also examined thesimultaneous relationShip between' achievement and attitudes. In thestudy, control and experimental classes were selected on the basis ofwhether the classroom teacher had participated in a-sumer inserviceprogram on economic problem sblving. Teachers in the experimentalinservice group were exposed to basic economic concepts and, theirapplication to problem solving in the classroom. Students in theirclasses worked on an economics-oriented USMES unit, "Manufacturing,during one semester. Teachers in the control group received noinstruction in economics or problem solving during the evaluationperiod. Results indicated that intermediate elementary studentssignificantly,improved their economic understanding by working oncomprehensive, realistic, and economic-oriented problems as found inthe USMES curricula,. These students seemed to develop asubstantially more _positive attitude toward economics. Also, theprogram seemed appropriate for both cognitive and affective learning.However, an untrained teacher would probably have difficulty addingan economic dimension to student problem solving from simply readingthe USMES materials. Therefore, insbrviCe training will improveteachers' economic understanding and will prepare them for ,

*introducing USMES material into the classroom. (Author/KC) /

.

*********************************#********************44***************.* Reproductions Supplied g EDRS are.the.best that call. be Made* .

e frOm'fWe original documents. *******************************************!****************************,

0

7"PERMISSION TO REPF40bUCE THIS -MATERIAL IjAS BEEN GRANTED BY

1)/411/a ni 11-ii,57/tid

TO THE EDUCATIONAL RESOURCESII ORMATION CENTER (ERIC) AND

SERS OF THE ERIC SYSTEM."

Estimiting the Effects of Economic

'Problem Solving in EleMentary SChOols*.

,s

WS. DEPARTMENT C10741ZALTN,. EDUCATION &WIMP U

NATIONAL INSTITUTI OPIDUCATIDN

THIS DOCUMENT WAS BEEN REDUCIO EXACTLY AS RECEIVED *ONTHE PERSON OR ORGANIZATION ORIGIN.RTING'IT POINTS OF VIEW ON OPINIONSSTATED DO NOT NECESSARILY REPRE-SENT OFFICIAL NATIONAL INSTITUTE OFEDUCATION POSITION OR POLICY

'BEST CrVAILABLEillipm B. Waisted'

AssistantBcofessor of Economicsand Director, ter for Economic Education

UniversI 6f HIssouri-St. Louis

Ir

<Septe4per, 1970

0.44

o-

--,

*Acknowledgement is made of the 'financial contribution from the. Bush Folundationand the. Minnesota State Council on Economic Education to conduct this research.Assistance from the St. Paul (MN.) Public Schools Administration ,and,seleciedteachers madethe data collection possible. The author is indebted to Bill, Beckerfor his suggestions on a previous vaxsion of the study. Darrell Lewis andAndrew Ahlgren also provided helpful comments on that version. All re ponsibility,however, foi any errors or omissions in this paper rests with the authOr.

1:4

O

IT4roduction

The stated purpOde of economic education is'to provide stetents'withV

theeconomic understanding and,problem-solvingl'ability to deal with both(

personal and societal economic problemis [15].. To achieve this purpose,

students

Iproblems

for eleme

appreach.'

at'all ages may need experience in working on meaningful economic

[12,' 26]; 'Unfortunately, fe4.apportunities have been proVided!+,

ntary studtnts to learn economics using' a realistic probledi,4Olving

5-

Oue reason for the failure to implement various approaChes2

`..10 Ai

for teaching

-econOmAs may be the- inadequate' economic understanding of elementary teachers

r21, 25]. .Although extensive resources lave beellUsed to' upgrade teachers', ,

stock,of econpmicknowledgethrough,ih;serVice,course'work [23], more research94 . -

is needed.Onthe

effective. in',the

Methods, Materials and types of in-service training which are94

classroom .[6].- Combining in÷service economic instruction with.-

trading in'he use of a realistic 'problem solving, approach may improve economic2 02 2

,=ding of elementaryteachers and; their students.

.estimating the effects of piograms on student _economic

"evaluiOtion studies in economic .education have only measured

thegnitive domain and have often neglected to

.athe WeCtive &main [20]. Moredier,

relatienahithetweeiachieliement fn economics and attitude towards economics

learning, most

student outcomes

measure student learning

the passibility of a simultaneous

may leilii4*th'e 14:of simultaneous

,

f'

equations techniques, such as' tw7 stage,a.

least.,squares,:(TSW4 to obtain consistent estimates of model parameters.

2

( This statisticalvproc durellas received limited use. in economic education studies.

This study,investigates the effects of a teacher in- service program that

provided instrUciion.in econemics'and problem solving as it applied to the

1Unified S-ciences-and. Mathematics for Elementary School.(USMES) curriculum L9J,

Single and simultaneous equation modelp are itieilfied and estimated to account.A

rD

4

3

2

for the effects .of achieyement and attitude ,on one another in student

economic learning. To'measure the effects of the in-service training on

teachers' economic, understanding, a single equation model is also estimated.- .

Finally, summary estimates 9ftheffull elate; of the in- service program on,

.

.

students are obtained using -combined results from student and teacher data.

,

analyses.(

2 2 41. it

Design of the Study 4.

This study was conducted in the St. lquil (Minnesota) Public Schools.

q13,

4.

P

.,A, non-e ;ivalent control group dedign wasVused with control and experimental

'classes selected on the basis of6whether Ole elassrooi teacher participated

in-'a summer in-service program On,, economic problem solving.4

Treatment for

the experiMental teachers involved exposure for three hours a day over a four-

week period to basic economic concepts and their appliCation to problem

.511117ing in the classroom. Experimental treatment for students included working

on an economics- oriented USMES:unit Manufacturing [10]', ove.t an eight-week

-peg& in the subsequent fall. 5Control 'teachers and students received no

`instruction in economics or problem solving during theevaluation period.

Information on student age, sex, grade level, and absences was obtained

from a quemaopnaire. 6Student socioeconomic status (SES) was 4stimated from

census tract estimates of the average value of housing on, the block where the

studentclived.T. Studedt understanding of economics was measured by a reduced

29.question form of the Test of Elementary Economic (TEE) [36]. Student .

. attitude towards economics was assessed by a semantic differential instrument.

4° Pre and postLtest teacher knowledge of economics was measured with forms A .and

c

B, respectiA41y, of the Test of EconbmV Literacy (TEL) [17].l All test

instruments showed suitable reliabilitY:iand validity.

Experimental teachers were pre7 CCed on the first day of the in-service

workshop and post-tested on the lastly of the workshop. Control teachers

j

were pre-tested at the beginning of the workshoand post-tested at the time

their students received their pre-teats. Students were post-testi:1d eight weeks

later. The teacher sample consisted of 17 teachers,. seven control and 101

experimental. For the analysis of student economic underStling, the sample

size was 333, 156 control and 177 eXperimenti1.9

A Simultaneous Economic learning Modell,

The model used for analysis of student economic learning is presented

in equations 1 and 2 in Figure 1. In4structural equation 1), post -test

economic understanding (Y1) is considered to be a funcition of: poit-test

attitude towards economics (Y2), age (X1), sex pC2),.SES (X3), pre-test economic

understanding (1/4), grade levels (X6, X7); absences (X8), teacher post-test

economic understanding (X9),' and group (control or-experimental, X10), plus

a constant term (X0) and an error term (1.11). On the nfher,hand, post-test

attitude towards economics (Y2) is expressed in at4uctual equation 2 as a function of

post-test economic understinding.(Y1), age (X1), sex (X2), SES (X3), pre-test

'Attitude 'towards economics (X5) , grade levees -(X6,,X7), absences (X8), teacher

post-test economic understanding (X9), and iro Rid, plus a constant term

(X0) and an error 1term CEld'yhe'two structur Lequations form a model that

incorporates a reciprocaOcasudflinkibetween the two endogenous variables,

post-test economic understanding Cfil and pst -test attitude towards economics

(r2).10

,

In this silaultaneoUs: equationmodel pre-test attitude towards

economics (X5) is excluded from equation,l, where post-test economic un

standing (Y1) was- the dependent variable. .TheHreason for this exclusion 0

based on the following 7"chaining" and was not frbitrary Pre-test attitude

(X5) is directly related to post-test attitude, (Y2), as shown in equation 2 of

the model. Post-test attitude towards economics (Y2), in turn, directly

where:

FIGURE .1

AN ECONOMICS LEARNING MODEL

(structural equations 1 and 2)

12y2+ y

10X0* 711 X + y

12X2

+ 713 X3+ Y

4.y17X7118X8

y8 119

X +y9 110

X10

t;1

Y2= 8

21Y1+ Y

20X0+iY21X14 Y

22X2+ Y

23X3

725X5 + Y206 Y27X7

Y1

= post-test economic understanding

Y2

X0

X1= age

X2 = sex

+729

X9 +7210

X10

= post-test attitudes towards economics

= 1 (constant t4m)

X3= socioeconomic status.

X4= pre-test econo ic understanding

X5

= pre -test attit de towards economics

X6.= grade 4

Xi -= grade 5

X8

= absences

X9

teacher post- cst economic understanding

X10

= group

U1

and U2= error terms.

U2

1D

a

influences post-test economic unders?Unding (y1), as shown by the inclusion. of

Y2 as a regressor in ,equation 1.. Only a contemporaneous direct,link is assumed

to exist between1the cognitive and affective domains. In other words, the

model specifies that pre-test attitude towards economics (X5) only has an

indirect effect on post-test economic understanding (Y1) through its direct

effect on post=test attitude towards economics (Y2). In each structural equation

(1 or 2),..only the direct effects of variables are included. Hence, the exclusion .

of pre-test attitude towards economics (X5) from equation 1, but its inclusion

in equation 2.11

In this.manner, the model specifications for the structural

equations Tiovide an explicit way to discribe the complex relationships between

variables, rather than just assuming that all effects are direct, with one-way .

causality, as would be done in a single equation learning model.

A cumulative or total descriptionPf these complex relatlenahips can be

provided by solving for the reduced form equations.for-the economic learning

model.as given in Figure 2. The reduced form and the structural equations are

alternative representations.of the same model in.that bothdescribe the relation-

E

ship between economic.achievement and attitude in the classroom. But the reduced

form and the structural equations provide different Information and require '

different estimation procedures. The reduced fori equations summarize the entire

structural model in terms of the total change expected in each endogenous variable

from a change in any one of the exogenous variables. The structural equations, on

the Other hand, merely provide the direct effect. 'of relevant variables on an

endogenous variable.

Problems in Parameter Estimation

If the use of OLS estimation procedures is to be applied to each of the

structural, equations pf the model, then a wither of assumptions about the error,,

71

(3) Y1

FIGURE 2 ./A

P/AN ECONOMICS LEA NQ MODEL

(reduied form equations 3 and 4

.1a

0121'20 N 10 Y. 1 1112 2a X + X"12P21 0 1 8:

2.821J.

+ 13121'23 + Y13 81024 +Y14X 4'

",12621 3 1 -°12"21i.

+ 8121'2 6 Y161 -

12021

812;29 4' Y19

812821

1321Y104. Y20,,

2I.,

*(4)Y

1 1- 821812 AO

8211'13 T 23+

'21812

9.

812Y27.4. Y171 -

512821 7

12Y22 + Y12

1 012821 X2

5121'25X

812021

Y1)8

1 - 812 521

8

812:21k Y110

1 612821 10

5211'11 Y21Xi

.41 821612

.812U2 + U1

1 -12 21

112.11'12:/- Y22 x \1 821812 2

821Y14 8211'15 4.,Y25

12X4+ 1'

812X3, 821'5

82016 Y 26X

71 621812 6

4'

8211'19 '29.1

821812

where s

B1024!1.44 error

1.-B12P21._

.11/41141 III-error

"p21p12

X0 thru Xio.were defined previously in Figure

B21Y17 Y27 X + 8212(18 Y287 1 - 0

21012

81. 821'12.

os

4'

-B21Yil0 20 i 21UL U2

&21'12 10 1 821812'

term for equation 3

term foiiquation 4

)

I

LI

terms would have..to be bet [16]. Namely, the, conditions (1) through (5) would

be assumed.about the error terms, where U represents the error terms and E( )

represents the expected value operator:

(1) E(Ui) 0 for all'i

(2) E(UiUj)

d2, where i j

..(3) E(U1j) 0, where i 0

(4)1E(UtU,413) 0 0, where i rsfers to all time Ieriods other.

than the period indicated by t

(5) .E(UiXi) 0, where X dedotei the regressors.

Conditions (1) through (5) state that the residuals have a zero mean, oonstant

and finite variance, zero between or within 'an equation, no residual\

correlation across time within or between equations, and that the independence-,

gro

of the error term with respect to the regressors is zeTok.

/,The simultaneous equations bide inherent 1n-the structural equations can

be shown by examining the relationshipni en the error term in equation 1 (U1)(

and the endogenous explanatory variable For n(bias to exist, the correlation,

between the variables would be expected to be zero. It is known, though,, from .

solving for the reduced form equa on,4 for Y that U., is part 6f7iWi error term

for Y2.

Therefore, the explanato variable Y2 in the structural equation -1 is

correlated with the error term U1 ;since:

..-

8 21 11.1

1+ U

2, 8

21. (

EfUl(Y2 - E(Y_))} U 2... _1..

12

,21 12

E(U1) 4 0 .

N. .

.

, . .

Similarly, pe error term for the structural equation 4.3 (U2) is, correlated(/ Z .

f 4

%with the endogenous explanatory variable Y1 since:.

r 11.

0 U + U 0 t

U2t7- - Ea )])

1 1.

; 812821 1 - 0 0

E(14) # 0 .12 2 U1 12

12 214 i .

As a consequence of the correlated e ii ror terms (violation of conditions 5),

OLS estimation of each of the structural equations can be expected to produce

inconsistent estimates of a parameter. 12Also, the estimated variances of the

regressor coefficients are dependent on the estimation of the variance of the

error terms (U1 or U2) causing estimated variances of the coefficients to be.

, .

biased. All test'statistics are suspect and cannot be relied on for the rejection

of the null hypOtheses concerning regressor effect's. To correct this situation,

a TSLS estimation 'procedure can be employed [16]."

Post-Test Economic Understanding

Table 1 reports the OLS and TS15S estimates of the structural equation 1.

Both estimation procedures showed the equation to b significant (F 36.602):

The R2

of .517 indicated that more than half of the variance in the data is

explained by the equation. The direction of the aigr and the size of the0111

coefficients were similar for each. estimation procedUre:

The most striking difference between the two procedures was the

significance test of the effect of the endogenous regressor, post-test attitude

'towards economics (Y2), on the, dependent variable, post-test economic understanding

(Y). The OLS estimate indic that The relationship was significant

(t = 2.445; p < .01). The TSLS estimate showed the effect of attitude on

achievement in economics to be\insignificant (t.-= .745; p> .05): The use of the

inconsistent OLS estimate would lead to the rejection of a true null hypothesis;'

thgt post-test attitudehad no significant effect on poet-test economic under-

standing, after controlling for the influence of other variables. In other words,

use of the OLS estimates may have resUlVed in a tn, I,atatilical error.14

. Also important for the purposes of this study was the effect of

-"experimental treatment (X10)on post-test economic understanding. After

controlling for the influence of'other variables, 1.88 out-of a posaible 29

10

jpoints was the direct contribution of the problem solving program to post-test ... . 4..

economic understanding,, as estimated by OLS and TAS. Students in the1

experimental group showed a significant difference in their/ economic understanding

when compared to control students as a direct consequence, of thel;experience

in economic,problem:solving."' k

Previous research had also indicated that a significant positi've relation--

*

i

iship ext is betweehrtudents' SES, as measured in. numerals ways, and economic.f'

understanding [5, 7, 24]. The TSLS and OLS estimates for students' SES"'(X3) in

equation 1 supputed these finding.. With this student sample, each increase

in $100 in the estimated average valUe of houiing on the block where the student

lived contributed a small but significant .005 of a point to the prediction of

the post-test economics score. In contrast, each point a student' achieved on

the pre -teat in economics (X4) added .755 of a point to the post-teSt economics

score. This variable (X4) as expected, was highly'signIficarit and served to

explain most of the variance in the post7test economics'score.

Other student background characteristics showed less influence on post-test4

eCOnOWIC understanding.(Y1). The OLS and TSLS'estimates for equation 1 indicated4

that the effects of: age (X1), sex (X2), grade level (X4,X.,), and absences (x9)16

". '

were all insignificant in predicting student-economic understanding. Of most

interest, howevez, was the apparent insignificance of a teacher's post-testp .

.

economic under!tanding in influencing their students' learning of the subject.

i Thia'res.ult may have been due to the stronger inTrence of other variables in

explaining the post-test scores; or the level of teachers' knoWledge

may not be sufficient to effectively contribute to student economic learning.

Whatever the reason, this finding wan not unexpected. The direct effect ofor.

teacher economic understanding has, not been consistently 'demonstrated in

economic education studies at either the secondary or thh elementary level.

[3, 19].

7

'RECR811$1UN RESULTS FROM ECONOMIC PROPLCH-SOLVINC STUDY,(t-statistic inparemtheses)

4

T1

Depardent,Variablet Post-test economic,taderstAn4im

OZ1 Estimate TSLS Estimate 015 1.1st ipate ofof Structural' of Structural Reduced FormPavement's , .-ttluritinn i kuiljon 1 -LW e 1"

.' .086

(2.445)*

.

.087(.745)

111.A.

-.027, ' -.024 -.025

.(-.821) ',i' (-.821) (-.762)t-'

4-.121 -.121 -.091' (-.344) (-.341)

/- (-.259)

.005 .005. .006

(2.391)* (2.179)* ' (2,475)*

.755 ) .255 .769(15.177)** (13.283)** (15.34)**

LA: 11.4: .027

(.739)

-1.580 -1.579 -1.654(-1.779) (-1.757) (-1.847)

(-1.179), -1799"

(-1.172)-.803

(- 1.16)

Post-test attitudetows d economies(7 5) I=

105 to 161)

X2 Six (0, 1) 1 sale,0 !mimic

X3

SC$ (87 to 600)

X4

Pre -test eednomic

understanding(2 to 29)

X5Pre-test attitu0towards economics(7 to 35)

X6

Crade 4 (0, 1)1 grade 4,0 ..other

X7

Csade 5 (0, 1)grade 5,

11.

0 other

XR Absences (0 to 12)

i9 Teacher.post-test

ecomOhic under-standing (18 Co 39)

X Croup (0, 1)0 ....control,

experimental

Constant

AdjustedR2

SEE

Total equation F

' . .071

c. (.867)

.013

(.385)

1.885(3.879)**

2.864.

i

(.603)

.317

3.174

36.602**

.071(.865)

. ).013

(.375) ---.

1.882

(2.942)45

2.847 .

, (.548)

.517

. 3.174

36.602** .

.065(.790)

. .018

(.557).

2.218(4.00)**

3.704-(.762)

.509

3.201

35.462**

N f 333 .333 ° 333

.Sictilficant at the .05 level.

"Significant at. the .01 level.

12

_ _-

-

_

-

_

I

'r

ti

'

h

A

ti

II'

r)

r.

The OLS estimates'of the reduced form equation 3, aS did the OLS and

TSLS estimates of structural equatiOn 1,. indicated that the total d'ffeCt oe

SES pre-adhievement in-economic (X) ,d treatment (X10), were allcA-S

positivdly and signifidantly important in prediction of thepost-test score. .in economics. Also, variables found to be insignificant in structural equa;,lon

1 were also insignificant.in their estimate&total effect in reduced form. .

equatibn 3: In fact,' results from both' the reduCed form'and' structural eqUation_

Otimates showed no significant influence of either pre-test attitude (X5) or4

post-teat attitude (Y2) towards economicw:on post-test achievement in

(Yl). MuCh speculation about the importance of attitude in influencing

economic achievement is called into question by these results.

Post-Test Attitude towards Economics

The OLS and TSLS estimates of the' structural parameters for equation 2

revealed that, the endogenous varlake, post-test economic understanding (Y1),

Significantly influhced post-test attitude towards economics (Y2). In fact,

as shown by.the TSLS estimate in 'Table 24 each point a - student obtained on, the post.--

teAt in economics contributed on average .223 of a point to the prediction of

;the post-test attitude score. OLS estimate of this relationship was lower

(.205)4,but the Significance of the:effect was greater (p < .01) than the

significance level of. the TSLS estimate (p < .05). A.gain, the reason for this

difference may be due to-the simUltaneous9e9uatidon bias. In this case, the

standard 'errors for each variable,.as derived by'OLS estimation would be

Incorrect, and all test statistics. using OLS.estimates would be suspect.

The overall equation F for equation 2-was:highly 'significant

(g < Al) by,both the OLS and TSLS estimates The adjusted 4,232:

indicated the equation explained 23 percent of the'*a*iand# ins; he

good reSultdpn.relation to much attitude research-1n ,education.' Besides

,

TABLE i

REGRESSION RESULTS FROWECONONIC PROBLEM - SOLVING STUDY(t-stattstic in porentheies)

4

Regressors

Dependent Variable: Post-test attitudetowards economics

OLS Estimateof StruCturalEquation

VSLS Eitimateof StructuralEquation

OLS Estivateof Reduced FormESUation'4

1 '1 Post-test economic.

understanding(2 to 25)

.

X 1 Age (105 to 161).

'X2 Sex (0, 1) 1 * male,

.0 * female

X3

SES (87 to 600)

Y

.20(3.275)**

.025

(.496)

.358,(.684)

..002(.496)

.223(2.316)*

.025(.518)'

.357. -(.180)

.002(.421) -

N.A:

.019(.398)

.335(.635)

.003(.804)

X4 Pre-test economicun-

derstanding (2 to 23)N.A. N.A. .172

(2.297)*XS Pre -test attitude

.310. .309' .3145toward economics (5.68 ** (5..562)** (5.680)**(7 to 35)

X6 Grade 4 (0, 1)

0 *.other,,'-.535(-.402)

-.494(-.368)

-.864(-.646)1 - grade 4

X7 Gra .129 -.0420 other ,(.127) (4135) (- .408).1 * grade 5

X8 Absences.(0 to' 12) -.082 -:081

(-.664)..067(-.539)

X9 Teacher pre-.7tes .062 .062 .066economic under=

standing (18 to 39)(1.280) (1.350)

TI '1>X10 Group (0, 1)

0 * control,3.406

X4:757)** N *3.36 .

(4.542)**'3.855

(5.472)**1 * experimental,.

Constant .9.029 8.997 ; 9.824.(1.256) (1.251) (1.355)

Adjusted R2

.232 .232 .220

SEE 4738 4.739 4.777

Total equation F 3.1.D47** 11.036**, 10.338**

333, 333 333

igniticaut at the.-..05 level.

7**Sigiiiitcaat at thy! .01 level.

lrpost -test achievement (Y1), two other variables were important in

explaining the'variance. Not surprising was tl0,strong impact of pre -test

attitude_(X5) on post -test attitude towards. conomics.: Each point the

student received. on the pre-test attitude tiasure. provided. .369 of.a point on.

average_ to the determination of the post-test attitude scores, as estimated by

"TSL.S Also significant was the effectOof experimental treatment (X10) on

pbst-test economic understanding- In receiving instruction id economics

through the real problem solving unit, Manufacturing student post-test attitude

towards economics became significantly more positive by 3.38 points out of a

possible 28 points, as estimated by TSLS in equation 2.. ,Experience in economic

problem solving, then, had subitantial input on both the affective and the

cognitive learning of students'.

The OLS istimatesrbf'thereduced form:equation 4 showed that the

pre -=test economic understanding variable (X4) significantly Contributed to

the prediction of post-test. attitude (Y2), together with Pre-test attitude (X5),

.and the group treatment variable (X10). The significance of pre -test economic, .

achievement (X) indicated another clear link between achievement and attitude

in economics learning. In this case, the link was an indirect one based on the

simultaneous equations model: Pre-!teat economic understanding (X4) directly

influenCed post-test economic understanding (Y1), which in'turn directly $h/t.

influenced Post-teA attitude towards economics (Y2).

Iq conclusion, results front estimating the reduced form eqUation 4 by

OLSaid

ihestrUctural equation

. .

2 by TSLS,.showed both, a significant effect

of either pre-test achievement (X4) or ,post-test achievement (YI) in economics

on post-test attitude towards economics (Y2). In contrast,,results,from

, .

estimating the reduced form equation 3 by OLS and the structural equation 1 by,

TSLS, revealed no Substantial influence of either pre-test attitude (X5)" or

)

Lei

post-test attitude (Y2) towards economics on post-test.achieveme

1). These findings suggest, then, that achievement in economi

-a- =ch stronger effect on attitude towards economics than attitut

-;.economics has on achievement in the subject. Cldequently, one. of improving students attitude towards economics appears to be t

of their economic. nderstanding... Air,

t in economics

s Probably' has

e towards

irect means

improvement-

Teacher Post -test Results

To estimate the effects of the sumaer in- service program o teachers,4

. . ._.

,

..,

a-single' equation model was specified. Teacher post-test scores in economicswere considered te be a function of the foiloWing variables in. equatiOn 5:dge (Xi), pre-,Cast economic understanding (X.2), pre-test interest in economies(X3), the nutber of-graduate credits received (X4), and whether the teacherparticipated in the in- service program (control or experimental, .X5). These,variables were selected since they would control for such important factors-esmaturity, previousknowledge of and interest in economics, and the educational.

. _

background of the teachers: [34, pp. 54 -67].,

The.results in.Table 3 showed that pre-rteat'econOmicsscore (;) was most

important in determining the post-test score in economics. In addition, theexperimental

teachera,gained-aaignificant 7.03 out of ai possible 46 points,:*ioa average, in their economic. understanding We direct result of attendingthe sutter,inr-service program,.after controlling for the effects of other

#.variables. Research in economic education has indicated that in-service

/. .

training cancontribute' td the improvement of the .economic understanding of_teachers [3]. Results from'equation S. lend support to the growing evidence

-that teachers, including elementary teacher, are able to learn a significant

amount of economics,even When that. lust ction is combined with pedagogy. [6,

1,

TABLE. 3

.1

,REGRESSION RESULTS FOR ECONOMIC PROBLEM-SOLVING STUDY(t-statistics in parentheses)

Reiressors

Xi Age :(28 to 53)

X2 Teacher pre-test under-standing offeconomics

X .Teacher pre-test interest3in economics (1 to1 low, 5 Q high

Dependent Variable: Teacherpost-test understanding of

economics

OLS Etimate' of Equption 5

.

k

X4 Number of graduate credits

to 60)

GrO(uP (0, 1) 1 - experimental,.10 8, Control

COnStant

AdjuSted R..

SEE

Total equation

N*

r

-.243(1.699).

,.659

(3.295) **

.1.6111.0w)

.122(1.906)

(72:(3)4;

7.306..

(.701)

Asni

4.260

3.585.

17

...

6

4 *SignifiC`antat the ...05

f*Significant at -the .01 level.

1-7ks

A"

LO

Combining Results: The'Total In-Service Impact

Results from post-test student and teacher data analyses can be combined

using a procedure suggested by Thornton and Vredyeld[32]..1Thile'thisfrocedure.

does:not permit hypothetis testing, it does provide a means of deriving a summary

estimate of the.full effect of the in-service prog Also the estimates from

.the simultaneous equation:4 model can be used to calculat a summary measure of

learning in both the cognitive and affective domains.

To illustrSte this approach, the results from es ting the reduced

fornaquation-3 of the simultaneous equation model show that experimental

students differed from control students by 2.218 points in post-test.ecciptimic

understanding.17

This 2.248 point difference'understates the full impact of

-the in-service program sinlpe their is an additional impact. on student from

the summer in-service program on teacher economic understanding. The reduced

forriestimate for the total effect of teaCher post-test economic understanding

(X9) on student post-test economic understanding in, equation 3 was .018. Also,

it is known froin es5mating equation 5 that expeiimantal teachers' knowledge of

economics differed from control teachers by 7.05 points on average as a result

of participating in the in-service program: Using,all three estimates, the in-,

service program had the total effect of increasing experimental studentS average

test.tcore by 2.35.points (2.218 + .018 x7.032),-or by aboirt 8%.

Similar procedures can be employed to derive an' estimate of the full

impact of the in-service program on student attitudes towards economics. From

the reduced form equation 4, the total effect of the expeiimental treatment

on student attitudes towards economics was estimated to be 3.855 points. The

contribution of teacher post-test knowledge of economics on student postftest

attitudes towards economics was estimated in equation 4 to be' .066. The

experimental teachers differed from control teachers by-7.032 points on "average

1.s

I

A

in their'economic understanding,

'service program had the combined

attitude towards economics score

1/

as shciwn by Table .5. Consequently, the in-

effect of raising experimental studdnts' average

by 4.319 points (3,.855 4- .066 x 7.632)', or a

15% increase.

Implications.

The above findings suggest a =lumber of implications for research,:!

classroom instruction, and in-service training in economic education. Most

research in pre-college

comes and has failed to

economic education has measured only achievement out-

assess attitude outcomes. In fact, there may exist

a simultaneous relationship between achievement and attitude outcomes in

economic learning. Using OLS to estimate a simultaneous relationship between

economic'achievement and attitude may produce inconsistent estimates of model

parameters. Use of TSLS permits consistent estimates to be obtained. For

example, when the PSIS procedure was used to estimate the parameters, of the economic

problem solving model, it appeared that achievement in economics had a

significant direct influence'on attitude towards economics, hut not vice-versa.

The use of OLS estimates of this relationship would result in a'type I

statistical error.

One major purpose of economic educdtion is to provide students with the

economic knowledge to handle both personal

Ihtermediate elementary students appear to

understanding by working on comprehensive,

problems', as found in the USMES curriculUm.

and societal economic decisions .

sigpificantly improve their economic

realistic, and economic-oriented

In the process, students seem to

develop a substantiallyvmore positive attitude towards economics:, This program

apPeati to-be well suited to the level of cognitive deVelopment of students and

contributeiCto their affective as well as their cognitive learning.

19

The economic understanding of most elementary teacherq(is Amited. Also,

since USMES materials contain no specific economic content, an untrained teacher7

would probably have difficulty adding an eConomic-Aimension to student problem-

solving work from simply reading the materials. Instruction is basic economic

concepts 6113 pioblem solving.methods may be necessary for teachers to help them

integrate economics into the interdisciplinary'problem-solving approach

found in;SSMES. This type of'in -service training appears to improve teachers'

economic understanding and to prepare,teachers for introducing economic

problem solving inthe classroom:

In short, the findings indicate that teachers and students gain

substantial benefits from the in-servite program in practical economic problem

,solving. Simultaneous equation techniques can improve estimates of program

effects on economic achievement and attitude.

k

Footnotes

1For

-

-a distinction between "real" and "contrived" problem solving see[9, Op. 5-15]. For a discussion -of the use of Drobl9m solving models_ orinstruction.An economics and the social sEudiei, see [34, Op.,'3-19].

l ' , .2In economic education research, attitude has been considered in some

41Nnstudies to a.function o ievement [13, 18, 29]. In fat '74t; Ceiy [13]estimated a learning el d concluded that "-attitudes and feelings maybe just as significan as-aptitude and achievement 'in explaining economicunderstanding." [p. 158]. On the other hand, studies haVe also indicatedthat economic understanding may sigdificantly influence student attitudetowards economics [33, 35]. No studies appear, to have examined the jointrelationship between economic achieveMent and attitude towards economics.Learning theory also suggests that a reciprocal. -relationship may exist betweenthe cognitive and affective domains [26, p. 24; 27, p. 76].

'3The most notable exception is a study by Becker and Salemi [2]. For a diwuisionof the application of various statistical procedures to non-recursive models ineducational research, see Anderson N. Soper [30, p.2393 recommends multiple linearregression analysis'or most economic education studies.

4.Although classes were not randomly assigned to treatment, there was."noreason to.suspect differential recruitment" related .to treatment. Followingthis distinction by Campbell and Stanley. [4, pp. 219-220], he study designcan be considered as a non-equivalent control group design.

5The problem in the Manufacturing unit was "to produce in quantity an itemthat is needed." [10,. p I. Among the items made were stuffed animala,clay pots and ornaments, .Fookmarksv jewelry, 1978 calendars,-pillows, schoolemblem folders, candles, and pencil or ticket holders.

6Age was expressed in months since this variable would serve to explain morevariance in the data than iage in years. A grade level variable was included'to refleCt possible differences in curricula experienced by. students.

7Ag has been noted.by Others [31], the income- housing relationship may not beperfect and corrections may be necessary. HOwelier,' for this:study, thecorrelption between the average valueof housing and family income in each ofthe school enrollment areas was .94.

k -

(1) The selection of itens'to eliminate from the TEE was based on theirextreme, difficulty level. in a preViOus beparate sample study. The increasein reliability (.53 to .65; KR-20) did not come at the expenpe of contentvalidity since all areas sei the test skstrix'were still covered. (2) Scalesin the semantic differential includedA valeable-worthless;: serious-funny;important-unimportant;Tuseful-useless; careful-haphazard; logical-illogical;important for the future-unimportant for the future.' The value of this approachhal been established [8, 35]. TI)k pre- and post -test reliability of theattitude measure was .82 and .88,Crespectively.. (3):The TEL was used with

emen ary teachers since the.test measured understanding, of basic economiccep s, as taught in the program The pre- and post-tes

it reliability was

1an .81I

respectively. $

\it

1 Extensive hypothesis testing was conducted to provide a basis for aggregation'of classes into control and experimental groups. Also, pre-test differencesin.economic understandingand attitudes towards economics were examined.Although4the'pre-test grot4) differences were slight,othe pre-test scorcscontinued to be as regressors in the post -test analysis 134, pp. 1101131]

!"-10

For this study, pre -test economic achievement and attitude are beingconsidered exogenous vari4les. Using a more technical distinction, thesevariables are lagged endogimous Irariables. It is customary to

anexogenous d lagged endogenou variables ad pre-determined variables. Thereare some problensAwith this di tinction, as has been noted by Maddala[22, p. 238] among AWrs.

11Following a s -liar reasoning process, pre-test economic understanding (X4)

was excluded rqm structural equation 2. P

12The reduced form equation, expresses each endogenous variabie as a function-of only exogenous, variables.. In the reduced form equation, condition (5) isnot violated; OLS'estimation procedures can be used to obtain Consistenttotal, but not direct', estimates, assuming, that conditions (1).to (4), are met.

13Indirect least squares can, also be used to derive the unique, consistent'

parameter estimates. These estimates will be,identical to TSLS estimates whenthe equations are exactly identified, as is the case in this moddl [16].

14.One of the'reasons for the different test results is the nature of the standard

errors for the OLS and TSLS estimates. In general, the standard errors obtainedfrom the TSLS estimates are greater than those far the OLS estimates. This doesnot mean, though, that the OLS method is better. The standard errors from theOLS method cannot be the correct ones, a 5.,..tpere estimates fail to control forthe simultaneous relationship that may exist. The TSLS estimates, then, are atleast assymptotically the correct estimates within the equational system considered.[22, p..241].

22

Similar results have been found,by.Ellis and Glenn [11] using the.Manufacturing unit with upper middle class students.

-

26The absence liable (Xe wasto reduce' the skewness throughresults.

.

;17The reduced form es

one reason. The rean-the dependent varTSLS estimates.

ghly positively skewed. However,\attemptsriable transformations failed to change the

mates. are used here instead of the TSLS estimates forfoim estimates measure the total effect of a variableaild'not just the direct' effects, as is the case. with

23

a

References/-. k

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lb

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.

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r

26

ti

gir 'It ia..-

_

./

..