THE WORLD BANK

Discussion Paper

EDUCATION AND TRAINING SERIES

Report No. EDT98

School and Classroom Effects onStudent Learning Gain:The Case of Thailand

Marlaine E. Lockheed

June 1987

Education and Training Department Operations Policy Staff

The views presented here are those of the author(s), and they should not be interpreted as reflecting those of the World Bank.

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Discussion Paper

Education and Training Series

Report No. EDT 98

SCHOOL AND CLASSROOM EFFECTS ON STUDENT LEARNING GAIN:

THE CASE OF THAILAND

Marlaine E. Lockheed

Research DivisionEducation and Training Department

June 1987

The World Bank does not accept responsibility for the views expressedherein, which are those of the author(s) and should not be attributed tothe World Bank or to its affiliated organizations. The findings,interpretations, and conclusions are the results of research or analysissupported by the Bank; they do not necessarily represent official policy ofthe Bank.

Copyright © The International Bank for Reconstruction and Development/The World Bank

ABSTRACT

This paper employs a fixed-effects regression analysis to examine

school effects on individual student learning gain during grade

eight in 99 lower-secondary schools in Thailand. Schools

accounted for 34% of the variance in individual student pretest

score, but accounted for only 6% of the variance in student

achievement gain (posttest controlling for pretest). A set of school,

classroom and teacher variables accounted for 4% of the 6%. School

factors accounted for approximately 7% of the variance in both rural

and urban schools, suggesting that schools are equally important in

both settings. In both urban and rural schools, larger schools and an

enriched curriculum were positively associated with learning gain, and

higher student-teacher ratios at the school level were negatively

associated with learning gain. Other factors, however, operated

differently in the two types of schools. An important policy

implication is that strategies for improving rural schools should not

be derived from research conducted primarily in urban schools in

developing countries.

INTRODUCTION

Despite substantial and rather consistent evidence from North

America that investments in school-related characteristics yield few

benefits in terms of student achievement (Hanushek, 1986), analyses of

student achievement in developing countries have come to the opposite

conclusion. School effects are seen as substantial in comparison with

home and background factors (Heyneman, 1980). Students from schools

with more resources--both material and human--outperform students from

schools with fewer resources.

The research literature from developing countries on this topic,

however, suffers from four serious shortcomings. First, it is based

almost entirely on cross-sectional data, which is incapable of

distinguishing factors related to initial level of performance from

those responsible for improvement in performance. Second, while

research in developed countries has begun to focus more on school

organizational characteristics and classroom practices related to

learning gains, research in developing countries remains focused on

material inputs. Third, advances in methodology, which have begun to

influence school effects research in developed countries, have not

begun to affect the analysis of data from developing countries.

Fourth, research in developing countries typically treats the entire

country as a case study, ignoring significant within-country

differences in resources. This paper contributes to the present

literature on school effects by (a) analyzing longitudinal data on

achievement in a developing country, (b) examining the effects of

organizational structure and classroom practices on student

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achievement gyin, (c) using a fixed-effects model of school

effectiveness, and (d) examining differences between schools in urban

and rural settings.

The paper is organized as follows. This section briefly reviews

the literature on school effects and student achievement in developing

countries. The second section describes the data, measures and

analytic method used in this paper. The third section presents

results, first for the sample as a whole and then separately for rural

and urban populations. The final section summarizes the conclusions.

Review of research

Research on school effects in developing countries has been

reviewed several times over the past decade (Avalos & Haddad, 1978;

Fuller, 1986; Heyneman & Loxley, 1983; Husen, Saha & Noonan, 1978;

Schiefelbein & Simmons, 1981; and Simmons & Alexander, 1978). While

some reviews have emphasized the effects of home and background

factors, most have noted that, controlling for student background,

school effects (particularly material inputs) are much greater in

developing countries than in industrialized countries.

One reason that material inputs are believed to have greater

effects on student achievement in developing countries is that there

is likely to be a greater discrepancy between home and school in

developing countries than in industrialized ones. That is, students

in low-income countries are likely to encounter few reading or writing

materials outside school, and hence schools are likely to be the

primary source of acquired literacy skills. Evidence on this point

comes from Nepal (Jamison & Lockheed, 1987) and from Brazil

(Psacharopoulos & Arriagada, 1987). Within developing countries,

-3-

the same logic holds that school effects will be greater in rural

settings in comparison with urban settings.

School factors consistently found related to student achievement

in developing countries include the presence and use of instructional

materials, time spent on learning, and teacher education. Factors

consistently unrelated to student achievement include teacher salary

and experience, and smaller class sizes (see Table 1).

Most studies of school and classroom effects on achievement in

developing countries have not examined the mediating processes whereby

school, teacher and classroom characteristics affect student learning.

Research in industrialized countries, however, points to the positive

effects of school and classroom organizational characteristics and of

selected teaching practices. Classroom variables found strongly

related to student achievement are: teacher evaluation and feedback

regarding student performance (Brookover, Beady, Flood, Schweitzer &

Wisenbaker, 1979; Walberg, 1984; Bridge, Judd & Moock, 1979); teacher

expectations (Lockheed, 1974; Walberg, 1984); clarity of teacher

explanations (Rosenshine & Furst, 1971); student peer group (Rutter,

1983); and teacher involvement in decision-making (Rutter, 1983).

Purkey and Smith's widely cited comprehensive review of the

effective school literature identifies nine school organization-

structure variables and four process variables that are consistently

found in schools that are effective in promoting academic achievement

(Purkey and Smith, 1983). The organization-structure variables are:

school-site management, instructional leadership, staff stability,

curriculum articulation and organization, schoolwide staff

development, parental involvement and support, schoolwide recognition

of academic success, maximizing learning time, and district support.

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The process variables, which define the "general concept of school

culture and climate" (Purkey & Smith, 1983, P. 444), are collaborative

planning and collegial relationships, sense of community, clear goals

and high expectations commonly shared, and order and discipline.

Despite these apparently stable findings, school effectiveness

research recently has been the center of a lively debate regarding

its methodology and implications for policy. Of the two issues, most

attention has been paid to methodological concerns, specifically those

related to overreliance on standard tests of academic achievement as

outcome indicators (Madaus, Kellaghan, Rakow & King, 1979), lack of

theoretical models in most school effectiveness work (Cuttance, 1985),

and the use of inappropriate statistical models for analyzing

multi-level data (Aitkin & Longford, 1986; Goldstein, 1984; Raudenbush

& Bryk, 1986; Sirotnik & Burstein, 1985). However, the second problem

is also of importance, since it concerns the appropriate use of

conclusions drawn from school effectiveness studies. As Purkey and

Smith note, although it is possible to identify variables that seem

responsible for increased student achievement, "it would be difficult

to plant them in schools from without or to command them into

existence by administrative fiat" (Purkey & Smith, 1983, p. 445).

Irrespective of the obvious difficulty in creating effective schools

and the need for further research, "school practitioners and reformers

have already embraced the precepts of the effective schools model

despite the absence of solid evidence" (Ralph & Fennesey, 1983). The

need for solid evidence is particularly great in developing country

settings.

- 9 -

school effect for this data set. Interactions between schools

and student pretest were also estimated. Then, student posttest was

regressed on pretest and sets of school, classroom, teacher and

teacher practice variables. Finally, separate estimates were made for

rural and urban schools.

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RESULTS

Total school effect

Variance explained. The first step in establishing the potential

size of the school effect on student achievement gain is to compare

the variance explained by pretest alone with that explained by pretest

plus specific "dummy" variables for each school. Summary results of

these regressions are presented in Table 3. In this case, student

pretest explained 48% of the variance in posttest. Adding 98 school

indicator variables to the regressionlincreased the variance explained

to 54%, but interacting the school variables with the pretest scores2

added nothing to the explained variance and virtually all interactions

were statistically insignificant.

Of course, differences between schools could account for much of

the difference in individual student pretest score. In fact, 34% of

the variance in student pretest was explained by the 98 school dummy

variables alone. However, the ambition of this paper is not to

determine the size of the total school effect on student achievement,

but rather to identify those school characteristics that account for

the portion of variation in individual student achievement at the end

of grade eight that is not accounted for by achievement at the

beginning of the year. Thus, while school characteristics may play an

important role in determining achievement at the outset of a given

school year, and while these characteristics may have a cumulative

1. These results are included in Appendix A.2. Since space demands for reporting the school plus school by pretestinteractions are considerable, these results are not reported here,but are available from the author on request.

effect, the initial analysis indicates that the school effect on

student achievement gain during eighth grade is limited to 6%.

Size of effect. To estimate the actual size of the school effect,

an average of the absolute size of coefficients for the 98 school

indicator variables was computed (see Heyneman & Jamison, 1980, for a

rationale for this procedure). The effect is pronounced. On average,

being in a good or bad school can, with pretest score statistically

controlled, affect post-test score by 2.3 points. This is equivalent

to one-half of a standard deviation on the post-test, which is

substantial.

The next question is to determine what -- if any -- school,

classroom and teacher characteristic or practices account for this

effect.

Explaining the school effects

In this section, a number of variables identified as possibly

accounting for the observed school effects are explored. Columns 1-4

of Table 4 present the results for school characteristics, classroom

characteristics, teacher background and teaching practice variables on

student achievement gain. For the subset of students for whom

complete data were available, pretest score again accounts for 48% of

post-test score (column 1).

School level characteristics. Column 2 of Table 4 indicates that

greater learning occurs in larger schools and in schools that operate

more days per year. Less learning occurs in schools that have

mathematics classes grouped by ability, that have a high

school-level pupil-teacher ratio, and --surprisingly -- that have a

higher proportion of teachers qualified to teach mathematics.

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Teacher background characteristics. Column 3 of Table 4 presents

the results for this analysis. Holding constant school level

characteristics, greater learning occurs in the classrooms of older

teachers; however, students of male teachers, teachers

with more postsecondary mathematics education, and teachers with

greater experience learn less.

Classroom characteristics. Column 4 of Table 4 presents the

results for classroom characteristics. Holding constant school and

teacher background characteristics, students learn more in larger

classes, classes that use a more enriched curriculum and ones that use

textbooks.

Teaching practices. Column 5 of Table 4 presents the results for

teaching practices. Holding constant school, teacher background, and

classroom practices, the teaching practice most associated with

student learning gain is frequent individual feedback. Students learn

less in classes in which teachers are required to spend more time

maintaining order, and less in which teachers use published workbooks

frequently.

Total explained variance. As noted at the outset, the total

variance in student learning gain to be explained was 6%; the

inclusion of 18 school, classroom and teacher variables explained 4%,

or two-thirds of the explainable variance.

Size of specific school and classroom effects. As noted above,

the total school effect was, on average, 2.3 points on the posttest,

controlling for pretest. From the coefficients reported in Table 4,

it is possible to calculate the proportion of the total school effect

contributed by each statistically significant school and classroom

variable. In order of size, these are: textbooks (65%), enriched

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curriculum and commercial workbooks (43%), larger schools and male

teachers (35%), ability grouping (30%), feedback to students (17%),

maintaining class order (15%), pupil-teacher ratio in school (13%),

teacher education (3%) and class size (2%). Several of these school

and classroom characteristics have a negative effect on student

achievement: commercial workbooks, male teachers, ability grouping,

maintaining order, pupil-teacher ratio, and teacher education.

Material inputs versus organizational characteristics. The

statistically significant variables identified in the previous section

represent both material and non-material inputs that have direct cost

implications, and organizational characteristics and teacher practices

that have few if any cost implications.

Two inputs that have direct implications for increasing costs are

greater use of textbooks and lengthening the school year. Since each

is related to increased student learning, the greater costs may be

justified.

Cost savings are implied by two actions. First, by not purchasing

material or non-material inputs that are unrelated or negatively

related to student achievement gain: more qualified, formally

educated, experienced and male teachers; and commercial workbooks.

Second, by utilizing larger schools (Jimenez, 1984) and larger classes

(Levin, Glass & Meister, 1984) that entail lower costs. Since they

are also related to increased learning, a double benefit would ensue.

Several organizational characteristics are importantly related to

student learning gain. On the one hand, an enriched curriculum and

frequent teacher feedback regarding student performance both increase

student learning. On the other hand, ability groupings and time spent

on maintaining order both reduce student learning. Since

- 14 -

organizational characteristics and teacher practices have few cost

implications, they hold promise for school improvement under

conditions of austerity.

Rural and urban differences

Rural urban differences in Thailand are substantial; in 1980,

rural per-capita income was less than one-quarter of that in urban

Bangkok. The effects of schools on student achievement, therefore,

might be expected to be larger for students in rural schools than for

those in urban schools. To examine this question, the schools in this

sample were divided according to their rural/urban status, and the

previous analyses rerun. This section highlights differences

between rural and urban schools and differences in school effects for

urban and rural students.

Differences between urban and rural schools. An inspection of the

mean values for urban and rural school (Table 5) reveals a number of

differences. Students in urban schools are more likely to be in large

schools with more educated teachers. Their teachers are more likely

to be female, older and more experienced. Although more urban

students are in enriched classes, their teachers spend more time doing

routine administration, keeping order and assigning seat or boardwork.

The results of the analyses for urban and rural schools are

presented in Tables 6 and 7, respectively. The following discussion

is presented in four stages. First, school level effects on

achievement gain are reported (Tables 6 and 7, column 1). Then,

holding constant school-level characteristics, the effects of teacher

background characteristics are examined (Tables 6 and 7, column 2).

Next, holding constant school-level and teacher background

- 15 -

characteristics, the effects of classroom characteristics are examined

(Tables 6 and 7, column 3). Finally, holding constant school-level,

,teacher background and classroom characteristics, the effects of

teaching practices are examined (Tables 6 and 7 column 4).

Posttest on pretest. A greater percent of variance in posttest is

explained by pretest for urban students (49%) than for rural students

(44%), which suggests that schools may be contributing more to

student achievement for rural students than for urban ones. (Tables

6 and 7, column 1).

School characteristics. The effect of two school characteristics

are consistent for both rural and urban students; students in larger

schools learn more than students in smaller schools, and students in

schools with higher student-teacher ratios learn less than students in

schools with lower student-teacher ratios. That larger schools are

more effective than smaller ones may be due to economies of scale,

while the fact that schools with lower student-teacher ratios are more

effective than those with higher student-teacher ratios may be a

function of less overcrowding and greater teacher-student contact

time. One other statistically significant variable--time--operates

differently for urban and rural schools. In urban schools, the longer

the school year, the more children learn, but in rural schools, a

longer school year is related to less learning. Finally, there are

two school characteristics that operate in one, but not both,

settings. In urban schools, children in schools with more qualified

mathematics teachers learn less, but school-level teacher

qualifications are unrelated to learning gain in rural schools.

Similarly, children in rural schools that utilize ability groupings

learn less than those in rural schools that do not use ability

- 16 -

groupings, but ability grouping at the school level is unrelated to

learning in urban settings.

Teacher characteristics. Only one teacher characteristic is

related to student learning in urban schools; this is teacher sex.

Students of male teachers learn less than students of female teachers.

In rural settings, no teacher characteristics are related to student

learning.

Classroom characteristics. In both urban and rural settings,

students following an enriched curriculum learn more than students in

less enriched classes. In urban schools only, students in classes

that use textbooks frequently learn more than students in classes that

do not use textbooks frequently; and in rural schools only, students

learn more in larger classes. It is possible that teachers in urban

schools have access to better textbooks and better training in their

use, while in rural settings available textbooks may be of poorer

quality or teachers untrained in their use. Also, larger rural

schools may be better quality schools according to other, unmeasured

criteria.

Teacher practices. In urban settings, students learn less in

classes in which the teacher spends more time maintaining class order

and more in classes in which students spend more time at seat or

blackboard work. In rural setting, students learn less in classes in

which seatwork and workbook use is more frequent, and learn more in

classes in which students receive frequent individual feedback.

Total explained variance. In all, for both rural and urban

students, school variables explain about 7% of the variance in

achievement; there is no difference in the amount explained for urban

versus rural students.

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DISCUSSION

This paper has used a fixed-effects regression model to estimate

school effects on eighth grade mathematics achievement gain in

Thailand. Schools explained 34% of the variance in pretest

achievement score, and added 6% to the variance in posttest scores,

once pretest scores were taken into account. Two-thirds of the

overall school effects on achievement gain could be accounted for by

certain school and classroom characteristics.

However, differences in these effects for students in rural versus

urban schools were pronounced. Only three of the 18 variables

analyzed had consistent effects in both urban and rural schools. In

both settings, students in larger schools and those studying a more

enriched curriculum learned more over the course of the year, and

students in schools with a higher student-teacher ratio learned less.

Otherwise, in some cases, school and classroom characteristics

that were positively associated with student learning in urban schools

were negatively associated with student learning in rural schools, and

vice versa. In other cases, variables that had statistically

significant relationships with student achievement in one setting had

no effect in the other setting. Table 8 summarizes these differences.

One implication of these findings is that decisions regarding

effective school characteristics based on information from urban

schools may not generalize to rural schools. The direction of the

differences observed, however, do not necessarily conform to prior

expectations. First, I anticipated that material inputs, such as

textbooks, would have greater effects in rural areas; this was not

the case. Second, I expected that school characteristics would

- 18 -

explain more of the achievement gains in rural schools than in urban

schools; this also was not the case.

On the other hand, there were strong effects for non-material

inputs and teacher practices. Organizational characteristics, such as

ability grouping, were negatively related to achievement for rural

students. A more enriched curriculum was positively related to

achievement for both urban and rural students. These organizational

characteristics hold promise for further research and policy and may

provide direction for improving education in developing countries

without extensive increases in resources.

Table 1: Effectiveness of Selected School Inputs on Student Learning

Number of Positive Statistically Significant EffectsInput Studies

Number Percent

Instructional Materials 40 -- 29 73

Learning Time 27 19 70

Teacher Inservice Training 12 8 66

Teacher Years of Education 60 36 60

Teacher Experience 23 10 43

Teacher Salary 13 4 31

Smaller Class Size 21 5 23

Source: Avalos & Haddad, 1979; Fuller, 1985.

Table 2: Variable names, descriptions and simmary statistics

Variable name Description Mean S.D.

XROT Pretest mathematics achievement score 8.66 3.86

YROT Posttest mathematics achievement score 11.84 4.52

SENROLT Number of students in school 1186.93 438.56

SSTREAM Ability groupings for instruction 1.47 0.24

SDAYSYR Days in school year 195.12 4.78

SPUTEAR Pupil-teacher ratio in school 14.64 2.00

SQUALMT % of teachers in school qualified to teach math .52 0.17

TEDMATH Semesters of post-secondary mathematics 4.17 2.38

TSEX Teacher sex (1=female, Z=male) 1.35 0.23

TAGE Teacher age in years 28.60 4.16

TEXPTCH Years of teaching experience 6.75 3.14

TNSTUDS Number of students in target class 42.83 4.44

TMTHSUB Math curriculum (1-remedial, 2-normal, 3=enriched) 1.90 0.34

TXTBK Frequent use of textbook 1.58 0.24

CEFEED Frequent individual feedback 3.16 0.39

TWORKBK Use of published workbooks 1.90 0.39

TVISMAT Use of commercial visual materials 1.36 0.23

TADMINL Weekly minutes spent in routine administration 27.08 19.99

TORDERL Weekly minutes spent in maintaining class order 20.18 9.92

TSEATL Weekly minutes students spent at seat or blackboard 54.99 20.54

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Table 3: Pretest, school indicator and schoolindicator by pretest interaction effects on posttest Grade 8

mathematics achievement in Thailand, 1981-82.

Dependent IndependentVariables Variables R N

Pretest 98 school indicators ("dummy" vbls.) .34 4013

Posttest Pretest .48 3801

Posttest Pretest and 98 school indicators .54 3801

Posttest Pretest, 98 school indicators and98 school-pretest interactions .54 3801

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Table 4: School and classroom determinants of Grade 8 mathematicsachievement gain in Thailand, 1981-82

Alternative specifications

Variables (1) (2) (3) (4) (5)

XROT .82 .78*** .78*** .77*** .75***(59.04) (48.52) (44.45) (42.39) (38.98)

SENROLT (in 100's) .07*** .05*** .04* .08***(4.65) (2.60) (2.02) (3.66)

SSTREAM -.84*** -1.18*** -.63* -.69*(-3.43) (-4.02) (.2.03) (-2.09)

SDAYSYR .04*** -.01 -.002 -.02(4.33) (-.57) (-0.14) (-1.21)

SPUTEAR -.13*** -.14*** -.12** -.23***(-3.67) (-3.32) (-2.90) (-4.84)

SQUALMT -.54*** -.59*** -.71*** .71(-4.91) (5.34) (-6.29) (1.50)

TEDMATH -.06* -.06* -.08*(-2.00) (-2.23) (-2.43)

TSEX -1.05*** -.93** -.81*(-3.67) (-3.26) (-2.56)

TAGE .05* .04 -.005(1.97) (1.54) (-0.17)

TEXPTCH -.06 -.06 .01(-1.81) (-1.58) (0.29)

TNSTUDS .05** .04**(3.24) (2.64)

TMTHSUB .82*** 1.00***(3.83) (4.44)

TXTBK 1.10*** 1.55***(3.86) (4.68)

CEFEED .40*(2.10)*

TWORKBK -1.01***(-4.80)

TVISMAT .49(1.53)

TADMINL -.003(-0.65)

TORDERL - .04**

(3.25)TSEATL .005

(0.94)

C 4.71 .55 11.00 3.54 8.55Adj. R2 (.48) (.47) (.50) (.51) (.52)N 3801 3134 2446 2395 2262

Note: Numbers are unstandardixed OLS coefficients, with t-statisticsin parentheses.

*p < .05, **p < .01, ***p <.001

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Table 5: Means and standard:deviations of variables for urban andrural schools in Thailand, 1981-82

Urban Rural

Variable Mean S.D. Mean S.D.

YROT 13.13 4.54 10.74 4.42

XROT 9.85 3.92 7.66 3.74

SENROLT 1648.13 440.72 794.83 325.65

SSTREAM 1.44 .24 1.49 .25

SDAYSYR 194.06 5.03 196.02 4.47

SPUTEAR 15.11 2.27 14.25 1.68

SQUALMT .54 .15 .51 .19

TEDMATH 5.25 2.76 3.25 1.84

TSEX 1.29 .22 1.41 .25

TAGE 30.06 3.84 27.37 4.35

TEXPTCH 8.32 3.33 5.42 2.77

TNSTUDS 43.71 2.74 42.07 5.57

TMTHSUB 2.07 .32 1.76 .35

TXTBK 1.51 .24 1.64 .24

CEFEED 3.22 .37 3.12 .42

TWORKBK 1.98 .39 1.83 .39

TVISMAT 1.36 .23 1.37 .24

TADMINL 38.50 25.84 17.42 9.71

TORDERL 22.94 8.43 17.85 11.01

TSEATL 58.29 20.65 52.20 20.35

- 24 -

Table 6: School and classroom determinants of Grade 8 mathematicsachievement gain in urban schools in Thailand, 1981-82

Alternative specifications

Variables (1) (2) (3) (4) (5)

XROT .82*** .77*** .77** .74*** .69***(21.41) (36.33) (31.33) (29.44) (24.65)

SENROLT (in 100's) .05* .09** .11** .06(2.16) (3.23) (2.59) (.98)

SSTREAM -.26 .15 1.08 1.84(-.78) (.29) (1.66) (1.87)

SDAYSYR .06*** .06* .09*** .05(6.00) (2.54) (3.39) (1.27)

SPUTEAR -.12** -.26*** -.15* -.43***(-2.70) (-4.25) (-2.29) (-4.85)

SQUALMT -.58*** -. 68*** -.90*** 3.20*(-5.08) (-6.02) (-7.45) (2.45)

TEDMATH -.02 -.01 .09(-.60) (.24) (1.36)

TSEX -2.78*** -2.04*** -2.78**(-6.16) (-4.23) (-3.09)

TAGE .03 .03 -.01(.90) (.54) (-.14)

TEXPTCH -.08 -.03 -.10(-1.68) (-.52) (-1.48)

TNSTUDS -.05 .23*(-.65) (2.15)

TMTHSUB 1.46*** 2.09***(4.05) (3.91)

TXTBK 2.04*** 2.92***(4.49) (5.03)

CEFEED -.91(-1.42)

TWORKBK -.10(-.25)

TVISMAT -.11(-.16)

TADMINL -.001(-0.18)

TORDERL -. 07**(-2.88)

TSEATL .04***(4.44)

C 5.01 -3.71 -.48 -13.26 -13.74Adj. R2 .49 .48 .53 .55 .56N 2087 1719 1232 1232 1099

Note: Numbers are unstandardized OLS coefficients, with t-statisticsin parentheses.

*p < .05, **p < .01, ***p < .001

- 29 -

Walberg, H. (1984) Improving the productivity of America's schools.Educational Leadership, 41 (8), 19-30.

- 30 -

Appendix A: Protest and school indicator effects on posttestGrade 8 mathematics achievement in Thailand, 1981-82

Alternative SpecificationsVariables

Coeff. t-stats. Coeff. t- tats.

XROT .82 (59.04) .65 (39.77)S02 1.50 (1.22)S03 4.32 (3.55)S04 - -5.73 (-4.70)S05 -0.33 (-0.27)S06 0.34 (0.26)S07 -1.17 (-0.90)S08 7.07 (5.11)S09 -1.46 (-1.07)S10- -2.71 (-1.97)Sl -3.64 (2.66)S12 -1.70 (-1.24)S13 3.15 (2.30)S14 -1.49 (-1.09)S15 -0.72 (-0.52)S16 -2.80 (-2.05)S17 -3.00 (2.20)518 1.19 (0.87)519 1.56 (1.14)S20 2.57 (1.87)S21 -1.22 (-0.87)322 2.30 (1.66)S23 -2.40 (-1.74)S24 1.18 (0.83)S25 5.12 (3.62)326 -0.32 (-0.23)S27 2.50 (1.76)S28 3.77 (2.68)S29 -0.97 (-0.69)S30 -1.26 (-0.87)S31 0.18 (0.14)S32 -4.76 (-3.66)S33 -2.45 (-1.87)S34 -2.19 (-1.67)S35 -1.18 (-0.91)S36 4.71 (3.62)S37 -0.65 (-0.50)S38 4.60 (3.51)S39 0.17 (0.13)S40 3.39 (2.48)S41 -4.71 (-3.43)S42 -1.10 (-0.81)S43 -3.78 (-2.77)S44 -2.97 (-2.16)S45 0.48 (0.35)S46 1.65 (1.19)S47 -1.17 (-0.85)S48 -2.63 (-1.91)S49 3.46 (2.51)S50 -0.74 (-0.52)

- 31 -

Appendix A (continued)

S51 3.34 (2.42)S52 6.42 (4.61)S53 0.71 (0.51)S54 0.69 (0.50)S55 0.10 (0.07)S56 2.50 (1.82)S57 -2.45 (-1.76)S58 -3.79 (-2.76)S59 -3.84 (-2.80)S60 -3.20 (-2.33)S61 2.93 (2.13)S62 -0.82 (-0.59)S63 4.52 (3.28)S64 -0.20 (-0.14)S65 -2.20 (-1.60)S66 -0.16 (-0.11)S67 2.25 (1.64)S68 -2.22 (-1.62)s69 0.71 (0.52)570 0.10 (0.07)S71 -2.12 (-1.50)572 -0.79 (-0.61)S73 -1.91 (-1.49)S74 1.59 (1.23)S75 -2.19 (-1.69)S76 -3.36 (-2.61)S77 -2.86 (-2.23)S78 -2.28 (-1.77)S79 -0.92 (-0.66)S80 1.68 (1.20)581 -0.84 (-0.61)582 2.79 (2.02)S83 -3.19 (-2.31)S84 2.59 (1.87)S85 3.25 (2.31)S86 -2.76) (-2.22)S87 2.85 (2.30)S88 6.58 (5.24)S89 -2.29 (-1.86)S90 1.98 (1-59)S91 1.43 (1.16)S92 1.76 (1.43)S93 3.28 (2.66)S94 2.86 (2.32)S95 4.85 (3.92)S96 -3.44 (-2.79)S97 -1.45 (-1.18)S98 0.65 (0.52)S99 -1.98 (-1.61)

C 4.71 6.13R2 .48 .54N 3801 3801