Student Discipline and Highschool Performance

Student Discipline and High School PerformanceAuthor(s): David E. Myers, Ann M. Milne, Keith Baker and Alan GinsburgSource: Sociology of Education, Vol. 60, No. 1 (Jan., 1987), pp. 18-33Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/2112616 .

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STUDENT DISCIPLINE AND HIGH SCHOOL PERFORMANCE

DAVID E. MYERS KEITH BAKER

ANN M. MILNE ALAN GINSBURG DRC U.S. Department of Education

Sociology of Education 1987, Vol. 60 (January):18-33

This paper examines the relationship between student misbehavior and academic performance and the effects offamily structure and mother's employment on misbehavior and performance. Using panel data on high school sophomores from the High School and Beyond (HSB) survey, we estimate a number of linear panel models. The findings indicate that sophomores with low grades misbehave more as seniors than those with high grades. Academic achievement in the sophomore year has little effect on changes in misbehavior. Misbehavior, however, has negative effects on changes in grades and achievement test scores. Finally, living in a single-parent family and mother's employment negatively affect both achievement and behavior.

INTRODUCTION

For the last sixteen years, the Gallup education opinion polls have found that the public's major concern about schools is the lack of discipline (Gallup 1984). Recently, A Nation at Risk (Commission on Excellence in Education 1983) and other national reports have called attention to the implications of the decline in test scores for this nation's youth. It has been hypothesized that poor academic performance is a function of student misbehavior and, more generally, lack of discipline in the classroom. While educators have focused on the consequences of misbehavior, researchers in the area of juvenile delinquency have hypothesized that misbehavior is attributable to poor school performance.

During the same period, there have been a number of changes in family demographics that may be related to students' school behavior and success. Foremost among these trends are increases in the incidence of single-parent families and working mothers. Between 1970 and 1980, the proportion of children living in one-parent families increased from about 11 percent to nearly 19 percent (U.S. Bureau of the Census 1982). The U.S. Bureau of Labor Statistics (1983) reports that in the same decade, the labor force participation of

mothers with children under 18 increased from about 42 percent to more than 56 percent.

REVIEW OF RESEARCH LITERATURE

Each of these issues-the relationship between academic performance and misbehavior, and the impact of family structure and mother's employment on academic performance and misbehavior- has been addressed in the literature.

The Relationship between Academic Performance and Misbehavior

A number of theories have been developed in the juvenile delinquency literature to explain the causes of misbehavior and its relationship to achievement. Early theorists held that delinquency was a problem associated with low socioeconomic status. In an attempt to account for this relationship, Merton (1968) and, more specifically, Cloward and Ohlin (1960) proposed that the poor socialization of lower-class students or the academic deficiencies they brought to school would lead to school failure, which would subsequently lead to discipline problems. Cohen (1955) ex- panded this notion by theorizing that many students with these lower-class characteristics, when confronted with the middle-class values inherent in the schools, would become frustrated and fail.

However, Call (1965) and Polk and Halferty (1966) found little class difference in the back- grounds of delinquents and nondelinquents, and Stinchcombe (1964) suggested that it was not family status but status prospects (i.e., a student's educational and occupational outlook) that were important. Stinchcombe showed that misbehavior at all class levels was lower among college- oriented or high-achieving students. Thus, he predicted greater discipline problems among low- achieving middle-class boys, a hypothesis not

This paper was prepared under contract no. 300-83-021 1 with the U. S. Department of Education. An earlier version was presented at the 1985 meetings of the American Educational Research Association, Chicago. The authors thank Michael Finch and Sandra Hanson for providing comments on an earlier version, Jane Burnette for editorial support, and two anonymous reviewers for helping to clarify a number of points in this paper. The opinions expressed in this paper do not necessarily reflect the position or policy of the U.S. Department of Education or DRC. Address all correspondence to Dr. David E. Myers, DRC, 1828 L Street, NW, Fifth Floor, Washington, DC 20036.

18



STUDENT DISCIPLINE AND HIGH SCHOOL PERFORMANCE 19

borne out by his own data or by Polk's (1967) or Elliott's (1966).

Given the importance of expectations of achievement in the development of these theories, a natural extension was the focus on labeling theory (e.g., see the summary by Pink [1979]) and the hypothesis that schools, through various types of labeling, create the situation for failure. Labeling theory argues that students who are "successfully labeled as inferior and treated as inferior [and who internalize the concept] will in fact perform in an inferior fashion. Such students lack commitment to the school, see school (and its rules) as unimpor- tant to them, and are thus prone to rebellion and delinquency" (Pink 1979, p. 46). Pink, like those before him, posits that the direction of causality is from academic failure to misbehavior.

Unlike criminologists, who have focused on one direction of the relationship between school success and misbehavior, sociologists have pointed out that there is also a causal path from misbehavior to school performance (Coleman, Hoffer, and Kilgore 1982; Purkey and Smith 1983; DiPrete 1981; Baker 1985). These authors argue that good discipline is a prerequisite for learning, and their research has consistently shown that a good discipline climate is one of the few variables associated with academic achievement.

Thus, two hypotheses concerning the relationship between school success (performance) and misbehavior can be derived from the literature. The criminological literature suggests that poor school performance will result in more misbehavior. The educational literature suggests that misbehavior will result in poorer performance.

DiPrete (1981) tested these two hypotheses using cross-sectional data rather than the longitudi- nal data used here and found that the effect of misbehavior on grades was weaker than the effect of grades on misbehavior. He notes that the restriction to cross-sectional data "creates difficul- ties in determining the correct causal ordering of school outcomes" (1981, p. 154). While sugges- tive, his analyses suffer from the inability to deduce directionality from cross-sectional data. (In the analyses reported here, we used the cross- sectional data used by DiPrete as the first wave of our panel data.)

Family Situation, Misbehavior, and Academic Performance

Several studies (e.g., Coleman et al. 1966; Jencks et al. 1972) have demonstrated the effects of socioeconomic status on achievement. Less attention has been paid to those aspects of family structure that have changed dramatically in recent years-the number of parents in the home and mother's employment.

Single Parents. There are a number of reasons why the absence of a parent in the home affects

school outcomes. Single parents have high rates of poverty (Bogue 1985), have less time for the supervision and care of their children (Hetherington, Camara, and Featherman 1981), and may have emotional problems stemming from divorce and separation (Hetherington et al. 1981). Each of these factors can be expected to have negative implications for school outcomes. The earliest studies on single-parent families (e.g., see reviews by Herzog and Sudia 1973; Hetherington et al. 1981; Shinn 1978) found effects of father absence on delinquency in boys, although they did not always control socioeconomic status variables. In a recent study, DiPrete (1981) found higher rates of discipline problems among students from single- parent families. Hetherington et al. (1981) and Kelly and Wallerstein (1979) have suggested that children from single-parent families created by divorce have more discipline problems than children from single-parent families created by death. They attribute this difference to the emotional disturbance caused by separation and divorce. Milne et al. (1986) found that students from single-parent families have lower achievement and that much of the difference in achievement can be attributed to the lower incomes of these families.

Working Mothers. The effects of mother's employment are also related.to the lack of time for supervision and care, but they may be offset by increased family income and by mother's function as a role model, particularly for daughters. Gold (1963) found more delinquency among the sons of working mothers with white-collar husbands than among the sons of working mothers in other social classes. Etaugh (1974) reviewed a number of studies relating mother's employment to lack of behavioral adjustment in school but found that the effects differed by age and sex of the child, by social class, and by mother's satisfaction with her job. Hoffman (1979, 1980), in particular, notes the importance of using control and intervening variables when studying the adjustment of children who have working mothers.

Hoffman (1980) states that while mother's employment may have no effect on the achievement of girls (partly because of positive role modeling) and lower-class boys, it may have a negative effect on the achievement of middle-class boys. Milne et al. (1986) show that mother's employment has negative effects for white students from two-parent families but positive effects for black students from single-parent families. The potential beneficial effects of mother's employment on achievement in poor and black families is also noted by Hoffman (1980) and by Heyns (1982).

THE MODEL

The conceptual model used to guide the investigation of the relationship between student



20 MYERS ET AL.

misbehavior and academic performance is shown in Figure 1. The panel design permits us to explore the effects of student misbehavior on changes in two measures of academic performance: (1) self-reported grades and (2) reading and mathematics achievement test scores. Similarly, we can examine the extent to which academic performance influences changes in student misbehavior between the sophomore and senior years of high school. With this panel design, we are able to assess, for example, the impact of misbehavior in the sophomore year on changes in academic performance between the sophomore and senior years.

The simultaneous equation model, which as- sumes a simultaneous relationship between misbehavior and academic performance, is an altemative to the panel design. That is, we could postulate a reciprocal relationship between misbehavior and performance in which both are measured at the same point in time (e.g., when the students were seniors in high school). The panel model, on the other hand, postulates that misbehavior and academic performance in the sophomore year affect misbehavior and performance in the senior year. Thus, the simultaneous equation model and the panel model represent different formulations of the process.

The criminologist's theories imply that student grades but not achievement tests influence changes in student misbehavior. Students rarely know their

scores on standardized achievement tests; therefore, these scores may not affect students' perceptions of how well they are doing in school. On the other hand, students know their grades and use them to measure their success or failure in school relative to their classmates. Thus, it is expected that grades will have a direct effect on student misbehavior and that achievement scores will have little, if any, effect.

Students who perceive that they are failing in school can be expected to exhibit nonnormative behavior. For example, students with low performance may resort to alternative forms of behavior to attain specific goals. In addition, students not performing well in school may be labeled bad students by both teachers and classmates and, in tum, may act in a manner consistent with those expectations.

On the other hand, misbehavior in the sophomore year is expected to influence both grades and achievement scores. Students who misbehave (e.g., cut class) are likely to perform at a lower level than their classmates who regularly attend class and behave in prescribed ways. Further, a student's misbehavior may influence a teacher's subjective rating of that student and result in poor grades.

In addition to emphasizing the relationship between misbehavior and changes in academic performance, the model also states that the number of parents in the family, mother's employment,

SINGLE PARENT. SENIOR MISBEHAVIOR

MOTHER WORKS SENIOR GRADES

MOTHER'S EDUCATION SENIOR ACHIEVEMENT SCORES

NUMBER OF SIBLINGS

FAMILY INCOME

HIGH SCHOOL PROGRAM

SOPHOMORE EDUCATONAL ATTAINMENT EXPECTATIONS

SOPHOMORE MISBEHAVIOR

SOPHOMORE GRADES

SOPHOMORE ACHIEVEMENT SCORES

Figure 1. The conceptual model. (Models are estimated separately by sex.)




mother's educational attainment, family income, number of siblings, and high school curriculum have both direct and indirect effects on each of the endogenous variables in the model. We expect students from single-parent families and students whose mothers work to misbehave more than students from two-parent families and students whose mothers do not work because of the more limited potential for supervision in these families. Further, we expect mother's educational attainment and family income to have negative effects on misbehavior and positive effects on academic performance. These influences may be attributed to family socialization (Cloward and Ohlin 1960), to expectations about educational performance, and to the availability of material and intellectual resources in the home (Benson, Medrich, and Buckley 1980). A large number of siblings is expected to have a positive influence on misbehavior and a negative influence on academic performance. This effect may be attributed to reduced supervision by adult family members. Enrollment in a general or vocational program rather than an academic program is expected to increase student misbehavior (Schafer and Olexa 1971) and de- crease academic performance (Alexander and McDill 1976). Finally, the student's educational attainment expectations are expected to mediate the effects of the exogenous variables and to directly influence changes in misbehavior and academic performance. Those with high aspirations are expected to experience decreases in misbehavior and increases in academic performance.

We conducted separate analyses for white males, white females, black males, and black females. This allowed us to test for possible interactions between race and sex and the remaining variables in each of the models. DiPrete (1981) found that the processes relating family background and misbehavior differed for males and females, and Milne et al. (1986) observed strong interaction effects between race and other variables on academic achievement.

DATA AND METHODS

Data Data from the base year and first follow-up of

the HSB survey (National Center for Education Statistics 1983a, 1983b) were used in the analyses reported here. The base-year data, collected in the spring of 1980, were obtained from a multistage, stratified, cluster sample of about 30,000 sophomores in over 1,100 schools; 36 sophomores were randomly selected from each school. In the first follow-up, conducted in the spring of 1982, information was obtained from nearly all base-year respondents. For purposes of the analyses reported here, we excluded students who did not participate in the base-year survey or the follow-up survey, those who transferred from one school to another

school, and those who graduated early. In addition, we confined our analyses to white and black students. Constraining the sample in the above manner reduces the overall number of students from 30,000 to 19,000.

In both the base-year and follow-up surveys, students were asked about their family background, socioeconomic status, family composition, and aspirations concerning postsecondary schooling. For the analyses reported here, the background (exogenous) variables refer to student and family characterstics in 1980. Achievement tests in subject areas such as reading and mathematics were administered to all students in 1980 and to base-year sophomores again in 1982. Grades represent students' self-reports of the grades they had earned in high school so far, measured in the sophomore and senior years.

The measure of student misbehavior for each time period is a Guttman scale derived from three items, each coded 0 or 1. Students were asked (1) if they cut class, (2) if they were perceived by their classmates as troublemakers, and (3) if they had ever been in serious trouble with the law. Thus, with the exception of the second item, the misbehavior items capture cumulative behavior and not behavior at a specific point in time. Responses to the second question measure behavior in the sophomore and senior years. Initially, we considered adding other items to the scale but found that they did not contribute additional information. A positive value on the scale indicates misbehavior, and zero indicates no discipline problems. The following table presents summary statistics of the performance of the scales. The coefficients of reproducibility and scalability for misbehavior in the sophomore year and in the follow-up year suggest that these items scale reasonably well, particularly in the follow-up data.

Sophomores Seniors Coefficient of reproducibility .89 .94 Coefficient of scalability .51 .76

A reviewer noted that if the misbehavior scale is primarily a function of students cutting class, then this variable may be a weak indicator of misbehavior. A detailed examination of the items, however, shows that 25 percent of all students reported that they cut class, 25 percent reported that their classmates considered them troublemakers, and 4 percent reported that they had been in trouble with the law. Further examination of the scale shows that 59 percent of the sophomores in 1980 had no behavioral problems (as measured by the three items), 41 percent had at least some disciplinary problems (a score of 1 or more), 12 percent had a moderate or high score on the scale (2 or 3), and slightly less than 2 percent had a high score of 3 on the misbehavior scale. Thus, the



22 MYERS ET AL.

misbehavior scale is not dominated by class cutting; it captures a wide range of behaviors that at the extreme can be thought of as delinquent.

The remaining variables used in our analysis are briefly described in Table 1. Univariate statistics for each of the four subsamples are presented in Table 2.

Previous analyses of family background data

provided by students and parents have shown that students provide less reliable information than parents (Rosenthal et al. 1983). Thus, we expect that some of the estimates reported here will be attenuated.

Like many large social surveys, the HSB has its share of missing data. In response to missing data, researchers typically (1) discard observations with

Table 1. Variable Descriptions

Variable Description

SINGLE Coded 1 if only the mother and no extended family is present, 0 otherwise. Derived from questions BB036B, BB036C, BB036D, BB036E, BB036G, BB036J, and BB036K on the base-year survey.

MWPT amA MWFT MWFT coied I ii -stuene' mol'ner woikei ThY-time 'belore ithe studenr was m elementary school, while the student was in elementary school, and while the student was in high school; coded 0 otherwise. MWPT coded 1 if student's mother worked in at least one period but less than full-time during each of the three periods, 0 otherwise. Derived from BB037A, BB037B, and BB037C.

MEDUC Mother's educational attainment, coded 10 if mother did not complete high school, 12 if she graduated from high school, 13 if she attended a vocational school or college for less than two years, 14 if she attended a vocational school or college for two years or more but did not graduate from a four-year program, 16 if she graduated from college or attended graduate school. Derived from BB042.

NUMSIB Number of children in the household. Derived from BB096A, BB096B, BB096C, BB096D, and BB096E.

LNINC Logarithm of family income. Family income was coded 3500 if student reported family income of $6,999 or less, 9500 if income reported was $7,000 to $11,999, 14000 if income reported was $12,000 to $15,999, 18000 if income reported was $16,000 to $19,999, 22500 if income reported was $20,000 to $24,999, 31500 if income reported was $25,000 to $37,999, and 44500 if income reported was $38,000 or more. Derived from BB1O1.

EDUCEXP Plans to attend college, coded 1 if student did not plan to attend, 2 if student did not know, and 3 if student did plan to attend college. Derived from BBI 15.

YBDISC Guttman scale based on responses to three items: 1. Every once in a while I cut class. (true or false) 2. Do other sophomores in your school see you as a troublemaker? (very, somewhat,

not at all) 3. I have been in serious trouble with the law. (true or false) Derived from BB059E, YBO53F, and BB061A.

FYDISC Same as YBDISC, but derived from FY66F, FY74F, and FY76A on the follow-up survey.

YBREAD Sophomore reading achievement test score. Derived from YBREADFS.

FYREAD Derived from FYREADFS.

YBMATH Sum of two math test scores (items YBMTHIFS and YBMTH2FS).

FYMATH Sum of two math test scores in the follow-up survey (items FYMTHIFS and FYMTH2FS).

ACAD, GEN, and VOC ACAD coded 1 if student was enrolled in an academic or college-preparatory program, 0 otherwise. GEN coded 1 if student was in a general program, 0 otherwise. VOC coded 1 if student was in a vocational program, 0 otherwise. Derived from BB002.

SEX Coded 0 if male, 1 if female.

YBGRADE Coded 3.75 if student reported mostly A's, 3.5 if about half A's and half B's, 3.0 if mostly B's, 2.5 if about half B's and half C's, 2.0 if mostly C's, 1.5 if about half C's and half D's, 1.0 if mostly D's, and .5 if mostly below D. Derived from BB007.

FYGRADE Coded the same as YBGRADE, but derived from FY7 on the follow-up survey.

RACE Coded 1 if black, 0 if white.




Table 2. Means and Standard Deviations of Variables, by Sex and Race

White Females White Males Black Females Black Males

Variables Mean SD Mean SD Mean SD Mean SD

SINGLE .108 .311 .109 .311 .318 .466 .307 .461 MWPT .671 .470 .644 .479 .475 .500 .473 .499 MWFT .127 .333 .139 .346 .441 .497 .426 .495 MEDUC 12.624 1.795 12.767 1.809 12.235 1.751 12.438 1.797 NUMSIB 2.814 1.936 2.796 2.055 3.846 2.684 3.730 2.857 LNINC 9.809 .555 9.927 .555 9.403 .710 9.493 .714 GEN .449 .497 .462 .499 .389 .488 .400 .490 VOC .156 .363 .179 .383 .305 .461 .297 .457 EDUCEXP 2.551 .705 2.436 .786 2.625 .658 2.549 .701 YBDISC .467 .698 .692 .825 .377 .599 .589 .767 FYDISCa .492 .658 .776 .835 .382 .581 .562 .685 YBREAD 7.668 4.640 7.960 4.792 4.177 3.685 4.697 4.012 FYREADa 9.250 4.851 9.543 4.986 5.174 4.123 5.947 4.378 YBMATH 14.162 9.129 15.602 10.198 6.219 7.039 6.741 7.866 FYMATHa 16.539 9.888 18.723 10.866 7.972 7.945 9.480 9.039 YBGRADE 2.879 .707 2.645 .779 2.604 .688 2.414 .722 FYGRADEa 3.008 .620 2.768 .685 2.717 .635 2.522 .647 N (base year) 8,281 8,019 1,580 1,350 N (in school

at time of follow-up survey) 7,710 7,416 1,428 1,169 a Based on the sample of students enrolled in school at the time of the follow-up survey.

missing data (listwise deletion), (2) use pairwise covariance matrices when applicable, or (3) impute missing data. We have selected the last altemative. A description of the imputation procedure is provided in Wise and McLaughlin (1980). We chose this method over pairwise deletion because the iterative maximum likelihood procedures used here require complete individual-level data. We did not use listwise deletion because it resulted in a large reduction in the number of sample observations and poor estimates of selected univariate parameters.

Methods

To obtain estimates of the relationships depicted in Figure 1, we estimated a series of linear regression equations. These equations are assumed to represent the behavioral process in the population. The equations were specified for each individual, i, as follows:

Ytjii=XalBlj +Elji (=1, . 5;i=1, . . ,N) (1)

and

Y2ki takYlki + X2iB2k + E2ki (k= I, . . . 4; i= 1, . . ., N), (2)

where Yljf and Y2k, are the endogenous variables measured while a student was a sophomore and a senior, respectively; Xli is a vector of exogenous variables (including a constant); Blj is a conform-

able vector of parameters to be estimated; Elji is a random disturbance; X2j is a vector of predetermined variables (not including Ylki); B2k is a conformable vector of parameters to be estimated; Otk is a parameter that relates the lagged endogenous variable Ylk, to the current endogenous variable Y2ki; and E2ki is a random disturbance term.

Unfortunately, panel data for the sophomore cohort are incomplete on two outcome variables; that is, only those who were still in school during the first follow-up survey were asked about their in-school misbehavior and the grades they had earned so far in high school. Since leaving high school is not a random process, we are left with a nonrandom sample of students who were high school sophomores in 1980 and who were still enrolled in high school in 1982. All analyses involving dependent variables measured two years after the base-year survey are based on the sample of students who remained in the same school that they attended when they were sophomores. The sophomore equations are based on all students.'

'We could have restricted the sample for the sophomore equations to those students who were also in the senior sample. In so doing, however, we would have thrown away data that could have been used in the estimation process and, thus, would have biased our estimates, unless we had attempted to correct for sample selection biases, as we did in the senior equations. Corrections for sample selection biases are based on a number of assumptions and thus make it more likely that the results will be called into question. To obtain the



24 MYERS ET AL.

As shown by Heckman (1976, 1979), Berk and Ray (1982), and others, serious biases in the estimates of the parameters may arise when only a select subsample of respondents is used. To correct for selection bias in the senior equations, we estimated the substantive equations (see equations [1] and [2]) and an additional equation that relates the probability of staying in school to a set of exogenous and predetermined variables measuring the youth's characteristics and family background. From this "selection" equation, we obtained predicted values, which were then included as explanatory variables in the senior equations. We then used ordinary least squares (OLS) to estimate the parameters in the equations. A detailed illustration of this procedure is presented in Appendix A.

The only selection process examined here is that which determines whether a student drops out of high school. We did not include as part of the analysis of sample selection bias those students who transferred from one school to another or those who graduated early. Out of the total sample of students, about 2,600 dropped out of school, 1,700 transferred to another school, and roughly 600 graduated early.

RESULTS

The presentation of the results is divided into four sections. First, we present a series of descriptive statistics pertaining to misbehavior, achievement test scores, and grades. Second, we focus on the direct effects of family background on both the level of and the changes in misbehavior and academic performance. Third, we examine the cross-lagged relationships between misbehavior and academic performance. Finally, we describe the stability of students' misbehavior and academic performance between the initial survey (1980) and the follow-up survey (1982). (The estimates of the probit equations used to correct for self-selection biases are shown in Appendix B.)

Descriptive Analysis

In Table 2, we show the means and standard deviations for student misbehavior, achievement test scores, and grades. In general, we find modest increases in student misbehavior between the sophomore and senior years. Closer examination shows that self-reported sophomore misbehavior tends to be higher for white males than for the other groups of students and higher for black males than for both white and black females. This same pattern holds for student misbehavior measured in the follow-up survey.

White males tend to score somewhat higher on both the reading and math achievement tests than the three remaining groups of students. White females have the next highest scores, followed by black males and females.

The means for the final measure of academic performance-grades-do not parallel those for the achievement scores. Self-reported grades, unlike student misbehavior and achievement scores, are highest for white females, followed by white males, black females, and black males. Contrast- ing the findings for grades with the achievement test scores shows a difference by sex: Males scored higher on the achievement tests than females, but females had higher grade point averages than males.

Family Background, Misbehavior, and Academic Performance

In this section we first describe the influence of the exogenous variables on sophomore misbehavior and academic performance (Table 3). Next, we focus on the effects of family background on changes in misbehavior and academic performance (Table 4). It must be kept in mind that the equations relating the exogenous variables to sophomore misbehavior and academic performance were estimated from the total sample of students. The equations describing senior misbehavior and academic performance were estimated from the sample of students who remained in school between their sophomore and senior years.

White students from one-parent families have slightly higher levels of misbehavior than white students from two-parent families. Further, white sophomores from one-parent families tend to have low achievement scores and grades. Students whose mothers work tend to have somewhat higher levels of sophomore misbehavior and lower reading and math achievement scores, grades, and educational attainment expectations than students whose mothers do not work. Living in a single-parent family or having a mother who works has few significant effects for blacks.

Mother's educational attainment is positively associated with students' academic performance and educational attainment expectations. In nearly all subsamples, students from large families have high levels of misbehavior, low academic performance, and low educational attainment expectations. As expected, family income has a positive effect on academic performance and expectations to attend college. However, for white males and females, high family income is also related to high levels of misbehavior. For black students, no relationship between family income and misbehavior is observed.

When students enrolled in academic, general, and vocational programs are compared, we find that, net of the other exogenous variables in the

most defensible findings, we used the complete sample to estimate the sophomore equations.




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28 MYERS ET AL.

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model, students in general and vocational programs misbehave more than students in academic programs, have lower achievement scores and grades, and are less likely to expect to attend college.

We now turn to an analysis of changes in the educational and behavioral outcome measures.2 Living in a single-parent family is generally not related to changes in misbehavior or achievement test scores. That is, the negative effects of living in a single-parent family do not become increasingly negative between the sophomore and senior years of high school. However, living in a single-parent family has negative effects on changes in grades for white males and black females. This finding indicates that these students in single-parent families tend to experience less change in their grades than students in two-parent families. Further, white females in single-parent families misbehave more in their senior year than in their sophomore year and learn mathematics at a lower rate than white females in two-parent families.

Mother's employment tends to be related to small increases in misbehavior and lower academic performance for white students over the two years. Increases in mother's educational attainment are generally related to gains in achievement for white students but not to changes in grades or misbehavior. Living in a large family tends to be associated with smaller increases in reading achievement and grades. More specifically, in three out of four subsamples of students we find that, net of the other variables in the model, students with many siblings experience a relative decline in reading achievement over the two-year period when compared to those with few siblings. White males

and black females from large families tend to show declines in grades between their sophomore and senior years of high school.

The influence of family income on changes in misbehavior and academic performance are mixed. We observe positive effects for misbehavior. This indicates that students with high family income tend to increase their misbehavior between the sophomore and senior years of high school at a greater rate than those with low family income; this is particularly true of white males and females. Family income is positively related to changes only in math achievement for white females and males. Income has no effect on grades.

Finally, we note the effects of school program and educational attainment expectations on changes in misbehavior and academic performance. Enroll- ment in a general or vocational curriculum has a weak effect on changes in misbehavior. In only two out of eight instances do we find significant effects. In contrast, curriculum has fairly consistent effects on academic performance. That is, students in general and vocational programs generally experience lower rates of change in their achievement test scores and grades than students in academic programs. As expected, students with high educational attainment expectations experience increases in both reading and math achievement. However, educational attainment expectations generally have no impact on changes in misbehavior or grades.

Relationship Between Misbehavior and Academic Performance

In this section we review the findings pertaining to the relationship between the level of academic performance (or misbehavior) and changes in misbehavior (or academic performance). When we compare the results across the four samples, we find several striking patterns (see Table 4). First, the level of sophomore misbehavior is negatively related to changes in academic performance in eleven out of twelve equations. Students who have no discipline problems score between .05 and .35 standard deviation higher in reading achievement than students at the other extreme of the discipline scale, depending on which race/sex group is considered. Their scores on the math achievement tests are between .07 and .64 standard deviation higher, even when prior achievement is controlled. Misbehavior in the sophomore year has a somewhat larger effect on senior achievement for black students than for white students. The senior grades of students who report no discipline problems are approximately .20 to .80 standard deviation higher than the grades of those who report serious behavior problems.

The effects of reading and math achievement in the sophomore year on changes in misbehavior are mixed. For white females, high sophomore math

2 Drawing on the estimated form of equation (2), we show why the parameter estimates reflect the effects of exogenous and predetermined variables on changes in the current endogenous variable. When we subtract Ylki from each side of equation (2), we get

2ki YIki - (k Y Iki Yl ki + X2jB2k

or Y2ki -Y ki = (a(k 1) Yl ki

+ X2iB2k + E2ki-

When we take the partial derivative of the new dependent variable (Y2ki - Ylki) with respect to an element of X2j, say, X2j1 (i.e., 8[Y2ki - Ylki/cIX2Ti), we find that based on the marginal distribution, the effect of X2j1 equals B2kj (see, for example, Maddala 1984). That is, the effect of the jth independent variable on the rate of change in the dependent variable equals its associated parameter estimate (B2kj). A similar argument follows when difference equations are applied to situations in which a dummy independent variable is used instead of a continuous independent variable (e.g., single-parent status).



30 MYERS ET AL.

achievement test scores are related to low rates of change in misbehavior, but high reading achievement test scores are associated with increases in misbehavior. For blacks and white males, achievement test scores have no effect on changes in misbehavior. (DiPrete [1981] found the same relationship using the cross-sectional data.) Grades have consistent, negative effects on changes in misbehavior for all students except black males. For black males, the effect is negative but insignificant. The estimates show, for example, that students who had a D average as sophomores are about .26 standard deviation higher on the misbehavior scale as seniors than those who had a B average.

Stability of Students' Misbehavior and Academic Performance

To assess the stability of misbehavior and academic performance between the initial and follow-up surveys, we examined the regression coefficient for the lagged dependent variable in each of the senior equations (Table 4). More specifically, we tested the null hypothesis that the regression coefficient linking, for example, prior achievement to current achievement equals one. If this null hypothesis is rejected, then we can conclude that there are significant changes between the prior score and changes in achievement. The results show a significant change in misbehavior and academic performance between the sophomore and senior years of high school for all subsamples.

A more detailed examination of the estimates of stability indicate that white males had the most stable misbehavior scores and that black males had the least stable scores. White and black females had about equal stability. For reading and math achievement, we find little variation among samples in the stability coefficients within tests. On the other hand, there are considerable differ- ences in the estimates in the grade equations. Here we find that white males have the most stable self-reported grades and that both black males and females have the least stable grades. White females are intermediate between these two extremes.

Selection Effects

As a side note, we briefly discuss the effects of dropping out of school on each of the senior endogenous variables. Examination of the selection effects (i.e., the parameter estimates for the lambda variables in the senior equations) shows that in general, there are no strong selection effects in the senior misbehavior equations after the other independent variables in the equations are controlled. However, in all the achievement and grade equations we find significant positive effects. A positive parameter estimate for the sample selection correction variable shows that as the chances

of staying in school increase, there are, on average, greater gains in academic performance.

SUMMARY AND CONCLUSIONS

The purpose of this study was to examine the relationship between misbehavior and academic performance and the effects of family background on these two variables. For a sample of students who remained in high school for two years after their sophomore year, our analysis indicates that those who report low grade point averages experience greater increases in misbehavior between the base-year survey and the follow-up survey than those who report high grade point averages. The results for the achievement tests are mixed, but white females with low math achievement test scores tend to experience greater increases in misbehavior than those with high achievement test scores.

The strong impact of grades on misbehavior and the generally insignificant effect of achievement test scores suggest that it is not failure to learn that results in misbehavior; rather, failure must be perceived. Perception of low performance relative to classmates leads to misbehavior.

When we look at the flip-side of this relationship, we see that misbehavior in the sophomore year has negative effects on both learning and grades. Earlier in the paper, we specified two research hypotheses: (1) misbehavior causes poor academic performance, and (2) poor academic performance causes misbehavior. The results presented here support both of these contentions.

Students from single-parent families and with mothers who work have greater discipline problems and lower academic performance than similar students from two-parent families with mothers who do not work. Number of parents and mother's employment affect not only the level of misbehavior and academic performance but also the changes in these outcomes. That is, students from single- parent families with mothers who work tend to experience increases in misbehavior and decreases in performance. This is particularly true of white males and females.

Our findings suggest that academic performance and family situation play an important role in determining student misbehavior. The results suggest that failure in school as measured by grades and math achievement influence later misbehavior. We also found that students who tend to misbehave experience declines in school performance between their sophomore and senior years of high school. Finally, misbehavior and academic performance are influenced by family situation, thereby lending support to the notion that family socialization and supervision by parents must be considered when evaluating policies pertaining to in-school misbehavior.




APPENDIX A AN ILLUSTRATION OF THE PROCEDURE TO

CORRECT FOR SAMPLE SELECTION BIAS

Rather than describe the general procedure for correcting for sample selection bias, we focus on the hypothesized specification in the population for student misbehavior measured at the time of the follow-up survey:

FYDISCi = otYBDISCi + X2iB2 + E2i, (Al)

where X2i, B2, and E2i are the same as in equation (2). The population regression function for equation (Al) can be written as

E(FYDISCijX2i) = (xYBDISCi + X2iB2 (i = 1, . . . N), (A2)

where we assume that E(E2i) = 0. Now, suppose that we have data on student misbehavior only for those students who remained in school. Taking this information into account, we can write equation (Al) as

E(FYDISCi|YBDISCi, X2i, Di = 1) = otYBDISCj

+ X2iB2 (A3) + E(E2ij Di = 1),

where Di = 1 if the student stayed in school and Di = 0 if the student dropped out. When we compare equations (A2) and (A3), we see that the expected level of student misbehavior for the sample of in-school students at the time of the second survey does not equal the expected level of misbehavior in the population. One may be tempted to estimate the misbehavior equation with the selected sample and make inferences about the relationships for the subsample of selected students using the set-up in equation (A2). Such conditional inferences may be faulty. That is, in the selected sample, the variables on the right-hand side of equation (Al) are likely to be correlated with E2i, thus producing biased parameter estimates of ot and B2 if OLS is applied.

To derive estimates of ot and B2, we need to take into account the conditional expectation of the disturbance term in equation (A3). To accomplish this, we specify a selection process,

Di=1 if Zj> 0 Di= 0 otherwise, (A4)

and a selection equation,

Zi = X1iB1 + Uli, (A5)

where X1i is a vector of exogenous variables, B1 is a comformable vector of parameters to be estimated, Uli is a random disturbance, and Uli and E2i are assumed to be bivariate normally distributed. Given equations (A4) and (A5), we can write equation (A3) as

E(FYDISCi|YBDISCi, X2i, Z > 0) = otYBDISCi + X2iB2

+ E(E2ijU1i > -X1iB1). (A6)

As Heckman (1976) shows,

E(E2ij Uli> -X1iB1) = (ao2/2j2)12 A

Xi = J(0i)/[ 1-F(0i)], (A7)

and

0i = (-XliBi)/(or )1/2,

where Xi is the inverse Mills ratio. Consistent estimates of Bl/(ou1)"/2 and therefore Oi can be obtained by estimating a probit equation in which Di is the dependent variable. Once Oi is calculated, Xi can be computed and included in equation (A3). OLS can then be used to estimate the parameters in equation (A3). The standard errors from the senior equations are corrected to account for the use of estimated values of Xi as regressors (Heckman 1979; Greene 1981). The senior equations were estimated using the computer program LIMDEP (Greene n.d.).

Two vectors of exogenous covariates (X1i and X2i) are referred to in the selection and misbehavior equations. Ideally, these do not contain identical sets of independent variables. If they do, we must rely on the nonlinearity of the probit equation to secure identification. For the analysis reported here, it is difficult to imagine an independent variable that does not influence student misbehavior, for example, but does influence the chances of a student dropping out of high school. In light of this problem, we have opted to rely on the nonlinearity of the probit equation to conduct the analyses.



32 MYERS ET AL.

APPENDIX B: Probit Estimates for Selection Equation

White White Black Black Variables Females Males Females Males

SINGLE -.295** -.380** -.094 -.017 (.068) (.067) (.101) (.104)

MWPT -.145* -.107 .087 .082 (.065) (.063) (.143) (.135)

MWFT -.261** -.188* .143 .082 (.083) (.080) (.146) (.137)

MEDUC .099** .036* .031 .006 (.016) (.016) (.030) (.029)

NUMSIB -.052** -.040** -.060** -.003 (.011) (.010) (.017) (.016)

LNINC .102* .068 .025 -.023 (.043) (.044) (.069) (.070)

EDUCEXP .196** .196** .248** .312** (.032) (.032) (.065) (.064)

YBDISC -.283** -.261** -.400** -.315** (.030) (.028) (.071) (.057)

YBREAD -.002 .004 .027 .014 (.007) (.007) (.017) (.016)

YBMATH .021 * * .020** .015 .021 * (.004) (.004) (.009) (.009)

YBGRADE .302** .456** .146 .272** (.038) (.038) (.076) (.072)

GEN -.311** -.112 -.127 -.252 (.069) (.073) (.131) (.131)

VOC -.235** - .200* - .152 - .202 (.082) (.082) (.137) (.140)

Constant - 1.438** -.768 .032 .078 (.436) (.453) (.736) (.727)

Log likelihood -1,625.1 -1,603.1 -429.16 -448.06

NOTE: Standard errors are in parentheses. * p<.05.

** p<.01.

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Student Discipline and Highschool Performance