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Effects of Part-Time Work on School Achievement During HighSchoolKusum Singh a , Mido Chang a & Sandra Dika ba Virginia Polytechnic Institute & State Universityb University of Puerto RicoPublished online: 07 Aug 2010.

To cite this article: Kusum Singh , Mido Chang & Sandra Dika (2007) Effects of Part-Time Work on School Achievement During HighSchool, The Journal of Educational Research, 101:1, 12-23, DOI: 10.3200/JOER.101.1.12-23

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12

art-time employment is a common activity among youth and is perceived by many adolescents as an integral part of their identity. Approximately 80%

of high school students work some time during high school, and about 40% of them work at any given time. According to the Current Population Survey (Bureau of Labor Sta-tistics, 2005), 37% of students between the ages of 16 and 19 work during the school months. Those numbers have stayed somewhat stable over the last few years (except for a slight decline in the last 2 years) after a sharp rise in the 1980s. Despite differences in work patterns, the phenom-enon of employment among school-aged youth cuts across social class, gender, and ethnicity.

Students spend anywhere from a few hours to over 40 hr per week in part-time jobs. The estimates of number of adolescents who work at any given time vary because the reported numbers change as young people move in and out of work during the school year. However, a large number of students are employed during high school years and try to manage work and school obligations simultaneously. Students work a substantial number of hours on weekdays after school. Any activity that is so widespread and takes a relatively significant amount of time is likely to have an effect on various aspects of adolescents’ lives.

The extensive practice of part-time work among high school students has led to a growing literature on the phenomenon of paid work during adolescence in the last 2 decades. Prevalent thinking among parents and educators seemed to be that work is good for youth. Traditionally, parents assumed that students’ involvement in work was valuable and had no negative effects on other aspects of their lives. In the last 2 decades, however, researchers have begun to examine critically the effects of employment on the educational and psychosocial development of youth. Although the body of knowledge on part-time work has grown substantially in the last decade, findings about the multiple effects of work on the school lives of students, including how work affects achievement, are inconsistent and debatable. Many researchers have studied the effects of part-time employment on the academic achievement as measured by grades or standardized test scores (e.g., Marsh, 1991; Quirk, Keith, & Quirk, 2002; Singh, 1998; Singh & Ozturk, 2000) and have found small-to-moderate negative effects on school outcomes, controlling for prior achievement. Some researchers have found either no effect or positive effect of work on school (Leventhal, Graber, & Brooks-Gunn, 2001; Mortimer 2003).

In general, researchers have found small to moderate negative effects of work on achievement measures such as grades and test scores (Marsh & Kleitman, 2005). Despite substantial literature on work and school out-comes, only a few researchers have examined part-time work and school success, controlling for school engage-ment. Thus, when those researchers found that students who are involved in part-time work achieve at a lower level than students who do not work, one can argue that these students were less engaged in school before they started working. It is important that one disentangles the relationship of work and school success by control-ling for other related factors.

Address correspondence to Kusum Singh, Virginia Polytechnic Institute & State University, Eductional Leadership & Policy Studies, 315 E. Egg-leston Hall (0302), Blacksburg, VA 24061. (E-mail:[email protected])

Copyright © 2007 Heldref Publications

Effects of Part-Time Work on School Achievement During High School

KUSUM SINGH SANDRA DIKAMIDO CHANG University of Puerto Rico Virginia Polytechnic Institute & State University

ABSTRACT The authors explored the effects of part-time work on school achievement during high school. To esti-mate the true effects of part-time work on school grades, the authors included family background, students’ educational aspirations, and school engagement as controls. Although a substantial literature exists on the relationship of part-time work and school achievement, the findings are inconsistent. The authors hypothesized that work intensity, as measured by number of work hours per week during the school year, would produce a negative effect on achievement. Results indicate that work hours do have a negative relationship with students’ self-reported grades after controlling for family background, educational aspirations, and school-engagement. The authors also discuss practical implications of the findings.

Keywords: adolescence, part-time work, school achievement

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Because school-engagement factors such as motivation, effort, and interest in learning are precursors to school achievement (Audas & Willms, 2001; Fredricks, Bluemen-feld, & Paris, 2004), it is important to control for their effects to understand the true relationship of part-time work to school achievement. If these school-success factors are not taken into consideration, the effect of part-time work on school performance will likely be overestimated. We examined the effect of part-time work on grades, controlling for prior and concurrent factors that lead to achievement. The concurrent factors are academic engage-ment, effort, and motivation, in addition to background factors; that is, family socioeconomic status, as well as parents’ and students’ educational aspirations.

The primary question that we investigated was: What is the effect of part-time work as measured by the number of work hours per week during the school year on grades? To investigate the true effect of work on school achieve-ment, we controlled for other factors related to school success, such as family background, educational aspira-tions, school motivation, engagement in academic work, and academic effort. The main criticism reported in some of the earlier studies has been that researchers have not controlled for other important predictors of academic success or they have controlled for family factors but not school-based factors that influence success, such as aca-demic engagement and motivation. On the basis of earlier studies and zero-sum theory framework (Marsh, 1991) that time is limited and when increasing time is spent on work, less time is spent on academic work, affecting school performance, the study hypothesized a negative effect of work on grades. Thus, we controlled for (a) fam-ily background variables, (b) parents’ education, occupa-tion, and aspirations; (c) students’ educational aspirations (a strong predictor of individuals’ educational outcomes); and (d) school-engagement factors, such as engagement in academic work, academic effort, and lack of motivation or apathy toward school.

Often researchers have argued that assessing the effect of part-time work on school outcomes is difficult because the students who are disengaged and academically apathetic are more likely to choose to work long hours. Thus, to deter-mine the true effect of part-time work on school grades, we ran a series of hierarchical regression models, controlling for different sets of variables: family, individual, and school-engagement variables in successive steps. Furthermore, we investigated differences among students on school engage-ment and school performance according to their working or nonworking status, and how much they worked. We con-ducted statistical analyses with a t test and analysis of vari-ance (ANOVA) to explore the differences between working and nonworking students on achievement-related variables (educational aspirations, lack of motivation, academic effort, academic engagement, and self-reported grades) and among working students on the basis of number of hours worked per week.

In view of the national concern over the low achieve-ment of secondary students in the United States, it is critical that researchers examine the relationship of work intensity and school achievement. We proceed in three main sections: (a) literature review of the antecedents and consequences of part-time work, (b) Method, and (c) Results and Discussion. We conclude with the Summary, Conclusions, and Implications of this work.

Part-Time Work: Antecedents and Consequences

Although a growing body of literature exists on the effects of employment on social and psychological devel-opment of youth (e.g., drug use, alcohol use, personal and social decisions, peers), we focus on studies that examine the relationship of part-time work with educational out-comes. In recent studies, researchers have examined the relationship of part-time work to school achievement and other school-related outcomes. Using data from the National Education Longitudinal Study:1988 (NELS:88), Singh (1998) found a small negative effect of work hours on standardized achievement test scores and a larger nega-tive effect on grades. Singh and Ozturk (2000) explored the relationship between work intensity during high school and total coursework completed in science and mathemat-ics. They found a direct negative effect of intensity of part-time work on number of courses taken in mathematics and science and an indirect effect on mathematics and science achievement through course-taking patterns.

In a recent study based on the first follow-up of NELS:88 data, Quirk, Keith, and Quirk (2002) found an overall negative effect of employment on high school grade-point average (GPA), controlling for the effects of family back-ground, previous achievement, gender, and ethnicity. The researchers found a significant decline in achievement scores when students worked more than 10 hr a week. Also, Marsh and Kleitman (2005) found that working dur-ing high school had negative effects on 15 of 23 Grade 12 and postsecondary outcomes, such as achievement, course work selection, educational and occupational aspirations, and college attendance, controlling for background and prior variables. Their results, based on NELS:88, a nation-ally representative data set, were consistent across ethnic-ity, gender, SES, ability levels, and type of work. Roisman (2002) reported gender differences for the effect of work on grades. In this study, boys’ school performance was more adversely affected by part-time work.

In several cross-sectional studies, researchers found that work intensity was negatively correlated to atten-tion in class, effort in school, and attendance (Steinberg & Dornbusch, 1991; Steinberg, Greenberger, Garduque, & McAuliffe, 1982), whereas studies in which part-time work was related to time spent on homework and GPA had somewhat mixed results. Some researchers have reported no effects of work on school performance, whereas others have found small-to-moderate negative effects of intensity

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of work behavior on educational achievement. Steinberg, Fegley, and Dornbusch (1993) conducted a longitudinal study with a sample of approximately 1,800 high school sophomores and juniors to examine the relationship of part-time work with several different indexes of school engagement and performance. They reported less time spent on homework, more frequent class cutting, lower educational expectations, and more disengagement from school for students who work long hours.

Findings on school engagement, on the whole, sug-gest that students with part-time jobs were less engaged in school before they were employed, but that part-time employment, especially that requiring longer than 20 hr each week, far worsened the problem of school disengage-ment. Other researchers found similar negative effects of work on school outcomes. Marsh (1991), in a longitudinal study using the High School & Beyond dataset, found a negative effect of intensity of part-time work on 17 of 22 school-outcome variables, after controlling for background and prior-achievement variables. Although research on the negative effects of part-time work on school-engagement variables were consistent, the results on GPA were mixed (Chaplin & Hannaway, 1996; D’Amico, 1984; Mortimer, Finch, Ryu, Shanahan, & Call, 1993; Steinberg & Dorn-busch, 1991; Steinberg et al., 1982).

In contrast to studies in which researchers found nega-tive effects of students working during the school year, a few researchers reported no effects of working on academic performance. The latter researchers found generally that the effect of work on GPA was negligible and that the effect of work intensity seemed to vary by grade level and high schools (Barone, 1993). In a cross-sectional study of 348 high school seniors, the GPA of 12th-grade students decreased slightly as their hours of work increased (Wor-ley, 1995). Winkler, Dewalt, Rhyne-Winkler, and Dewalt (1993), in a cross-sectional study of 130 juniors and 110 seniors, found that hours worked per week were not related to GPA for seniors but were related to GPA for juniors.

In a study based on a small sample of high school students, researchers reported that part-time work was not a signifi-cant predictor of semester GPA, although the general trend of the data indicated a negative correlation between hours worked and grades (Steinberg et al., 1982). One explanation for a lack of relationship between GPA and part-time work has been that working students tend to take less challenging and fewer courses to maintain their GPAs, and that teachers also lower their expectations and grading standards for these students (Greenberger & Steinberg, 1981; Mortimer et al., 1993; Singh & Ozturk, 2000; Steinberg et al., 1993). Thus, because of the inconsistent findings, researchers need to consider other sources of variation in school success.

In general, researchers who have explored the relation-ship of part-time employment to educational outcomes have found mixed results—from no effects to small, nega-tive, and sometimes positive effects. Reasons for the incon-sistencies in findings are related to differences in research

designs, methods, measures, and various subpopulations of students. Some researchers have argued also that differ-ences between working and nonworking students may be attributable to pre-work differences in attitudes, values, and behaviors; that is, students who work long hours are, for example, less interested in school than are their peers before they start working (Steinberg et al., 1993). Bach-man and Schulenberg (1993) suggested that work intensity might be the result, rather than the cause, of low academic achievement. In the present research, we explore the relationship of work and school performance, controlling for different sets of variables at successive steps. Family background variables are strong and consistent predictors of school success, as are students’ educational aspirations. Educational aspirations reflect home and parent influences and are crystallized before high school. School engage-ment, effort, and motivation are the most proximal vari-ables that are likely to affect grades. By controlling for those important predictors of academic achievement, we can determine the true effect of part-time work on school engagement and grades. Thus, we build on prior research and extend the empirical knowledge on the effect of part-time work on educational outcomes of youth.

Method

Sample

We collected the data for this study as part of a larger study; students in the sample were high school students in Grades 9–12 (n = 1,547). In 2002, we collected the data with a survey questionnaire on the school and work experiences of students in six high schools in southwest Virginia. The majority of students (52.2% girls and 47.8% boys) were Cau-casian American (91.1%), 4.7% were African American, and only 1.2% and 2.4% were Hispanic and Asian Ameri-can, respectively. Most students were in Grades 9 (33.4%) and 10 (28%); 24.4% were in Grade 11, and 15% were in Grade 12. A majority of students were in the college track (49.8%) rather than in general education (42.8%). Less than 2% were in special education. A large majority (74.2%) of students reported that they had worked during the school year, whereas 36.5% of the students worked at the time of data collection. Those figures are similar to the national statistics on part-time work among high school students. We had difficulty assessing the number of working students at one time because students moved in and out of jobs. Of those students who reported working, about 40% worked between 5 to 20 hr; only 25% reported never working.

Measures

Students completed the School and Social Experienc-es Questionnaire (SSEQ), a 45-question survey that we designed. We used the items and scales listed in the follow-ing paragraphs for this study.

14 The Journal of Educational Research

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Part-time work. We measured participation in part-time work by three items: (a) “Have you ever worked for pay, not counting the work around the house?” (b) “How many hours do/did you work each week on your current or most recent job during this school year?” (c) “How many of those hours are on the weekend (Saturday or Sunday)?” The items did not constitute a scale, and we used them only to understand the work behavior. The number of hours worked in a week during the school year was the primary independent variable. We measured work hours in 10 categories: none, 1–5, 6–10, 11–15, 16–20, 21–25, 26–30, 31–35, 36–40, and over 40.

Family background variables. We measured family back-ground by each parent’s education and the average of both parents’ education, as well as mother’s and father’s occupa-tions. We recoded the occupations of mothers and fathers to Duncan’s Socio-economic Indicator (SEI) of occupa-tional status (Stevens & Cho, 1985). In addition, we mea-sured parents’ educational aspirations for their students and averaged them for both parents. We used those items on family background separately in the regression analyses.

Educational aspirations was a single-item measure with five response categories: 1 (less than high school) to 5 (gradu-ate or professional school). The item asked the respondent how far he or she intended to pursue education.

School-related items. We assessed three school-engagement items: lack of motivation (α = .70, academic apathy), academ-ic engagement (α = .80), and academic effort (α = .70). (Table 1 shows item wordings, means, standard deviations, and scale reliability estimates.) We measured academic engagement by seven items that reflected psychological investment and enjoyment of school, and academic effort by four items about student effort in schoolwork. Both measures employed 4-point rating scales, ranging from 1 (strongly disagree) to 4 (strongly agree); we averaged the items for each scale (range 1–4). Six items concerning the frequency of disengaged and apathetic behaviors measured lack of motivation (e.g., number of times students skipped classes or came to class without homework). By using a 5-point scale ranging from 1 (none) to 5 (more than 10 times), we determined that three of the items focused on the number of times that the behavior had occurred in the past 4 weeks. The other three items assessed students’ preparedness for class by asking how many times they came to class without books, and so forth. We scored those items on a 4-point scale, ranging from 1 (never) to 4 (usually). We standardized, then averaged the six items to create a standardized mean score for lack of motivation. (See Table 1 for items, composites, and reliability estimates.)

We included self-reported grades as the outcome vari-able. Schiel and Noble (1991) have indicated that self-reported grades are sufficiently accurate to use as measures of the educational development of groups of students.

Model and Analysis

The basic model that guided this research was based on previous research and theory. Part-time employment

during high school is a widespread phenomenon; many students devote considerable time to part-time jobs. Thus, we hypothesized that work hours affect school performance. The analyses were hierarchical—under-taken by adding different sets of variables in each suc-cessive step. There was a logical order of adding new sets of variables based on temporality. Earlier influences such as parents’ education, mother’s and father’s occu-pational status, and parents’ educational aspirations for the students were the first model because these factors preceded other influences. In the second model, we added educational aspirations because students’ educa-tional aspirations are formed before high school and are correlated strongly with parent influences. In the third model, we added three school-engagement variables, the most proximal and current predictors of school success, reflecting involvement in school work. In the fourth and final model, we added work intensity (number of hours worked per week during school). We wanted to determine the effect of work intensity on grades, after controlling for earlier sets of variables.

We conducted descriptive analyses to assess the (a) aver-age number of hours worked during the school year, (b) percentage of students who worked during the school year, and (c) percentage of students in different categories of work from a few hours to over 40 hr. We used ANOVA to compare students who were currently working, students not currently working, and those not working on the individual and school factors. To further understand the differences in school factors on the basis of work intensity, we divided working students into four categories according to the num-ber of hours worked per week: (a) less than or equal to 10 hr (low work intensity), (b) between 11 to 20 hr (moderate work intensity), (c) between 21 to 30 hr (high work inten-sity), and over 31 hr (very high work intensity). We used ANOVA to observe mean differences in school variables among the groups and examined correlations for signifi-cant relationships between work hours and other variables. Finally, we conducted regression analyses to estimate the effect of part-time work hours on self-reported grades. We checked assumptions of ANOVA and regression prior to the analyses. Although the distribution of the variables was abnormal, we did not consider violation of this assumption serious in view of the robustness of ANOVA and regression for minor violation of normality. We found the more seri-ous assumption of the homogeneity of variance acceptable in the case of all variables except grades and motivation. Thus, we checked ANOVA results for equal and unequal variances and found them to be robust.

Results and Discussion

Table 2 shows means, standard deviations, and correla-tions among the variables. Approximately 26% (n = 373) of the 1,547 participants reported that they never worked for pay. About 37% were currently employed (n = 564), and a


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TABLE 1. Composites and Items

Composite Item wording and codes M SD

Parents’ education How far in school did your mother go? 4.63 2.56 (mean of mother’s and How far in school did your father go? 4.56 2.63 father’s education), α = .76

Parents’ educational How far does your mother expect you to aspirations (mean of go in school? 4.11 0.91 mother’s and father’s How far does your father expect you to aspiration), α = .92 go in school? 4.08 0.94

Mother’s occupation Which of the categories below comes closest to describing your mother’s current job? 8.63 3.64

Father’s occupation Which of the categories below comes closest to describing your father’s current job? 6.86 4.18

Student’s educational How far in school do you expect to go? aspirations (1 = less than high school to 5 = graduate or professional school) 4.20 0.94

Academic engagement I enjoy school because learning things (α = .80) will help in the future. 2.79 0.76 Academic success is important for success in life. 3.31 0.80 I look forward to school because I like subjects studied. 2.43 0.83 Participation in classes is fun. 2.70 1.54 My teachers encourage me to learn. 2.95 0.76 I often study things that interest me. 2.92 0.81 My school experiences are generally positive. 2.80 0.77 (1 = strongly disagree to 4 = strongly agree)

Academic effort I feel I am responsible for my learning. 3.36 2.26 (α = .70) I always try hard, no matter how difficult the work. 2.83 0.82 When I fail, that makes me try that much harder. 2.87 0.83 I always try to do my best in school. 2.98 0.82 (1 = strongly disagree to 4 = strongly agree)

Lack of motivation Number of times late for school in past (α = .70) 4 weeks 1.55 0.84 Number of times missed school in past 4 weeks 1.75 0.90 Number of times skipped classes in past 4 weeks 1.24 0.73 (1 = none to 5 = more than 10 times) How often come to class without pencil or paper 1.75 0.87 How often come to class without books 1.68 0.85 How often come to class without homework done 2.19 1.04 (1 = never to 4 = usually)

Work intensity Hours worked per week on current/most recent job 3.81 2.40

Grades What kind of grades did you get this year? 3.97 0.91 (1 = mostly below Ds to 5 = mostly As)


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similar percentage (37%) had worked at some time (but not currently) during the school year (n = 583). The data support the national figures that about 70% to 75% of high school stu-dents work at one time or another, and about 35% to 40% of high school students work at any given time (Bureau of Labor Statistics, 2005). Of those students who reported working, 36% worked less than 10 hr per week; 35% worked 10 to 20 hr per week. However, approximately 30% of the employed stu-dents worked over 20 hr a week; 18% worked 20 to 30 hr and 12% worked longer than 30 hr a week. Those figures illustrate the prevalence of employment among youth.

Relationship of Work and School Achievement

We computed the correlations between work intensity and school variables to examine the magnitude of relation-ship between work intensity and other variables (see Table 2). Significant negative correlations occurred between work intensity and all school engagement and performance variables. Although the correlations were moderate, rang-ing from r = –0.17 between work intensity and engagement to r = –0.23 between work and grades, the trend is unmis-takable. It is evident that a negative association exists between work hours and school factors that lead to higher achievement. Students who work longer hours are likely to display less engagement, less motivation, and less effort to learn. They are also more likely to have lower educational aspirations and lower grades. Significant correlations indi-cate association, and one cannot infer a cause–effect rela-tionship on the basis of correlations.

Differences Among Working and Nonworking Students

We conducted further analyses to compare students who reported currently working, those who had worked but were

not currently working, and those who had never worked for pay. We used ANOVA to compare the groups on all school variables (see Table 3). After checking the Levene statistics to test the homogeneity of variance assumption, we ran a robust test (Welch test) to investigate group differences for self-reported grades and lack of motivation because the homogeneity of variance assumption was violated for these variables. The tests for the two variables produced signifi-cant results similar to the ANOVA test results.

Our results show some noteworthy patterns. Students who had never worked scored significantly higher on academic engagement and academic effort and signifi-cantly lower on lack of motivation than did the other two groups of working students. There was no significant difference regarding educational aspirations between students who never worked and those who were current-ly working, but those who had worked in the past scored significantly lower on educational aspirations than both of these groups. Notable differences also occurred on self-reported grades between working and nonworking students. Those who had never worked had signifi-cantly higher self-reported grades than did the other two groups. We computed eta-squared values (η2) as indexes of effects (see Table 3). According to Cohen (1988), η2 = .01 is considered a small effect. Education tends to have smaller effects than those found in sociology, eco-nomics, and so forth (Tabachnick & Fidell, 2007). The η2 values showed small but significant effects of work-ing on school factors and grades. The results emphasize the general trend that working students have lower school engagement and school performance. According to the data, nonworking students tended to have higher engagement and performance in school than did work-ing students. Figures 1 and 2 illustrate differences based on work status.

TABLE 2. Descriptive Statistics and Correlations of Study Variables

Variable M SD 1 2 3 4 5 6 7 8 9 10

1. Parents’ education 4.380 2.329 — 2. Parents’ educational aspirations 4.108 0.871 .279** — 3. Mother’s occupational status 8.470 3.739 .351** .175** — 4. Father’s occupational status 6.354 3.874 .117** .007 .029 — 5. Student’s educational aspirations 4.178 0.883 .251** .702** .170** .040 — 6. Academic engagement 2.855 0.522 .085* .279** .029 .010 .322** — 7. Academic effort 3.004 0.702 .091* .201** –.001 .033 .288** .564** — 8. Lack of motivation 1.675 0.517 –.113** –.183** –.059 .024 –.202** –.352** –.358** — 9. Work hours 3.867 2.299 –.109** –.129** –.033 .009 –.108** –.173** –.121** .101** — 10. Grades 4.020 0.883 .249** .425** .176** –.047 .467** .377** .380** –.409** –.228** —

*p < .05. **p < .01.


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Differences Among Working Students Based on Work Hours

We conducted separate analyses to examine differences among working students on the basis of work hours (Table 4). We used ANOVA to examine differences on school factors among low intensity (n = 325), moderate intensity (n = 316), high intensity (n = 160), and very high intensity

(n = 96) students, followed by post-hoc comparisons. There were significant differences among students who worked different numbers of hours (see Table 4). For example, stu-dents who worked less than 10 hr had significantly higher educational aspirations than did the high-intensity and very high- intensity groups (M = 4.24 versus 4.04 and 3.96, respectively). The two low-intensity work groups were not

TABLE 3. Group Means for School Factors Based on Work Status

Never Currently Not currently employed employed employed EffectVariable (n = 373) (n = 564) (n = 583) sizea

Educational aspirations 4.191 4.225 4.062b 0.005Academic engagement 2.924b 2.842 2.818 0.006Academic effort 3.120b 3.009 2.963 0.008Lack of motivation 1.555b 1.695 1.707 0.014Self-reported current grades 4.164b 4.006 3.923 0.011

aEta-squared values (η2) as indexes of effect. bIndicates significant difference from both of the other groups.

FIGURE 1. Differences in grades and educational aspirations.

4.191

4.225

4.164

4.006

4.062

3.923

4.25

4.20

4.15

4.10

4.05

4.00

3.95

3.90

Mea

n

Never Employed Currently Employed Not Currently Employed

Self-Reported Grades Educational Aspirations


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different from one another in terms of educational aspira-tions. Low work-intensity students also reported higher academic engagement and grades than did all other groups. The results suggest that low-intensity workers are more engaged in school, get better grades, report higher aspira-tions and effort, and display lower apathy than do their counterparts who work longer hours. We conducted post-hoc comparisons with Tukey’s HSD method and controlled for the Type I error rate. Table 4 shows details on all sig-nificant differences.

Effect of Work on Grades

The most important question that we addressed is the effect of part-time work on school achievement. To answer that question, first we regressed students’ self-reported grades on family background variables. Parents’ education, edu-cational aspirations and mother’s and father’s educational occupations together explained 20.8% variance in grades. Second, we added educational aspirations of the students to

the model. The ß =.319 for educational aspiration was sig-nificant, and the addition of educational aspirations resulted in a significant change in R2. The total R2 for the model increased to .259. Third, we added three school variables, engagement in learning, academic effort, and lack of moti-vation (academic apathy), to the model to account for the effect of these variables on school achievement. The vari-ables were significant and explained an additional 12.8% variance, increasing the total R2 to .387. Finally, we added the variable work hours (a measure of work intensity) to assess the effects of working. Controlling for all prior factors, working part-time had a significant negative effect. The standardized regression coefficient for work intensity was –.122 and added 1.4% to the explained variance, bringing the total R2 for the final model to .401. One should interpret the results, based on small but significant negative effects, with caution. Although the effect size is not large and the contribution of the part-time work to the explained variance in school grades is small, this effect is unique and additional after other important factors (e.g. family effects, educational

FIGURE 2. Differences in school engagement factors.

3.25

3.00

2.75

2.50

2.25

2.00

1.75

1.50

Mea

n

Never Employed Currently Employed Not Currently Employed

Academic Engagement Academic Effort Lack of Motivation

3.120

3.0092.963

2.924

2.842 2.818

1.555

1.695 1.707


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aspirations and other school engagement) have been con-sidered. Therefore, when students are involved in part-time work, they are more likely to have lower grades, controlling for other important predictors of school success.

Overall, the results of the study emphasize the net nega-tive effect of working on grades. Students of similar family backgrounds, educational aspirations, and similar levels of school engagement are likely to do worse than their peers if they are also involved in part-time work. Few research-ers have included a comprehensive set of school engage-ment factors and family background variables in models of part-time work. The findings of this study highlight the deleterious effects of long hours of work on school-related outcomes and further confirm similar results of earlier stud-ies (Greenberger & Steinberg, 1986; Marsh, 1991; Mor-timer et al., 1993; Steinberg & Dornbusch, 1991; Singh & Ozturk, 2000; Marsh & Kleitman, 2005). Evidently, many variables other than part-time employment affect school performance, but working for long hours does lower school achievement, controlling for other family and school fac-tors (see Table 5 for all regression results).

Summary, Conclusions, and Implications

We examined the effect of part-time work on school achievement. The results clearly suggest that serious involvement in part-time work has a negative effect on school grades, even after taking into consideration the effects of family, along with students’ educational aspira-tions, engagement in learning, effort, and school moti-vation. Our results are similar to previous studies that related part-time work to educational or psychosocial out-come variables. Although the sample came from southwest Virginia high schools, one can cautiously generalize the results to similar high school populations. Earlier find-ings have suggested that students who choose to work long hours have previously had problems with their aca-

demic work and acquiring a part-time job worsened the situation (Bachman & Schulenberg, 1993; Steinberg & Dornbusch, 1991). Marsh and Kleitman (2005) found that working during high school resulted in a negative effect on many student outcomes such as achievement, course selection, educational and occupational aspirations, and college attendance. Roisman (2002) reported that for boys, working more than 20 hr per week is associated with self-reported poor grades and behavior problems in school.

We show that despite small effect sizes, there are sig-nificant differences in school engagement and school out-comes among working and nonworking students. Our study is based on cross-sectional data, which does not permit cause-and-effect inferences. Thus, one cannot infer that work causes low achievement. There is no doubt that prework differences exist between students who choose to work versus those who choose not to work. Students who are less interested in school and school-related tasks may choose to work long hours. Yet, there is no doubt that substantial commitment to work would further erode aca-demic achievement. Earlier studies based on longitudinal data (Marsh & Kleitman, 2005; Quirk, Keith, & Quirk, 2002; Singh 1998, Singh & Ozturk, 2000) that controlled for previous school achievement still found a decline in school performance associated with long hours of work. Many students do engage in long hours of work during high school. According to our data, approximately 30% of work-ing students worked over 20 hr a week.

There are practical implications of the findings of this study. Because students with similar family backgrounds, educational aspirations, and similar levels of engagement are likely to perform worse in school if they work, the results of this study have important implications for par-ents, teachers, counselors, and educational policymakers. Many students who may be able to pursue attending col-lege likely weaken that possibility by working long hours and failing to take interest in learning during high school

TABLE 4. Group Means for School Factors Based on Work Intensity

Work intensity

Low Moderate High Very highVariable (6–10 hr)a (11–20 hr)b (21–30 hr)c (> 30 hr)d Significant differences

Educational aspirations 4.24 4.10 4.04 3.96 Low > high Low > very highAcademic engagement 2.94 2.81 2.76 2.68 Low > moderate, high, very high Moderate > very highAcademic effort 3.02 3.00 2.95 2.78 Low, moderate, high > very highLack of motivation 1.65 1.71 1.76 2.06 Low < high, very high Moderate < very high Grades 4.28 3.91 3.79 3.57 Low > moderate, high, very high Moderate > very high

an = 325. bn = 3–6. cn = 160. dn = 96.


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years. Specifically, when students work long hours, they are more likely to perform at a lower level and be less engaged in school than their peers.

The relatively low achievement of U.S. secondary school students, compared with students in other industrialized countries, has been a matter of national concern. Part-time work during the school year is almost unique to the United States. In view of the concern about the academic achieve-ment of secondary school students, part-time work and its ramifications for academic decisions should be considered carefully. Often the decision to work is based on almost no information about school-related considerations. Parents are often unaware of the consequences that working during the school year may have for school performance. It is not unreasonable for one to infer that parents are guided by the common wisdom that work prepares young persons for the transition to the adult world of work, but the fact that early participation in the workforce also limits future opportuni-ties is often overlooked.

Although a growing body of literature exists on the relationship of part-time work with a wide range of edu-cational and psychosocial outcomes, there is need for fur-ther research on part-time work, school engagement, and school achievement. We also underscore the need for more

research, especially on various groups of students, social contexts, and types of work. It is critical that researchers explore the effect of work on defined subpopulations of students such as urban, suburban, or rural students; at-risk students; college-bound versus vocational students; minori-ties; and female students. The effect of work varies by intensity of work and social contexts. More focused analy-ses by group, intensity, and type of work will likely produce more precise and specific results than previous studies and lead to the development of better guidelines for counselors, parents, and students.

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TABLE 5. Regression Coefficients

Current grade

Model 1 Model 2 Model 3 Model 4

B SE β B SE β B SE β B SE β

Step 1 Parents’ education .048** .015** .128** .043** .015** .113** .035* .013* .092* .031* .013* .082*

Parents’ educational aspirations .384** .038** .379** .163** .051** .160** .137** .046** .135** .128** .046** .126**

Mother’s occupational status .016 .009 .067 .013 .009 .056 .016* .008* .068* .016* .008* .069*

Father’s occupational status –.015 .008 –.066 –.017* .008* –.076* –.016* .007* –.069* –.015* .007* –.067*

Step 2 Student’s educational aspirations .319** .05** .319** .220** .046** .219** .221** .046** .221**

Step 3 Academic engagement .158* .068* .093* .132 .068 .078 Academic effort .183** .050** .146** .180** .050** .143**

Lack of motivation –.409** .060** –.239** –.403** .060** –.236**

Step 4 Work hours –.047** .012** –.122**

Total R2 .208 .259 .387 .401Total adjusted R2 .202 .253 .378 .392R2 change .208** .051** .128** .014**

F change 39.506 41.429 41.643 14.109df1 4 1 3 1df2 603 602 599 598

Note. df = degrees of freedom.*p < .05. **p < .01.


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