Investigating the Link Between Home–School Dissonance and Academic Cheating Among High School Students

Abstract

The current study examined the association between home–school dissonance and academic cheating among 344 high school juniors and seniors at two urban high schools. Students completed two subscales of the Patterns of Adaptive Learning Scale (PALS) and one subscale of the Academic Motivation Scale (AMS). Analyses revealed that home–school dissonance significantly predicted both amotivation and academic cheating. In addition, results revealed that amotivation was a significant mediator of the association between home–school dissonance and academic cheating. Limitations and future research directions are discussed.

Keywords

African American students urban education academic achievement culture subjects

Several reports have indicated that cheating is very common among middle-, high school-, and college-level students (Anderman, Griesinger, & Westerfield, 1998; Anderman & Midgley, 2004; Finn & Frone, 2004; Jensen, Arnett, Feldman, & Cauffman, 2002; Kerkvliet & Sigmund, 1999; McCabe, 1999; Murdock & Anderman, 2006). According to some reports, up to 90% of students have cheated at least once prior to completing secondary education (McCabe & Trevino, 1993, 1997), while others have shown that cheating is recurrent among some students (Athanasou & Olasehinde, 2002; Cizek, 1999; Evans & Craig, 1990; Evans, Craig, & Mietzel, 1993; Graham, Monday, O’Brien, & Steffen, 1994; McCabe, 2001; Whitley, 1998; Whitley, Nelson, & Jones, 1999).

Researchers have sought to more fully understand academic cheating by moving from investigating student demographic predictors to examining its psychological and social/contextual correlates (Anderman et al., 1998; Anderman & Midgley, 2004; Anderman & Murdock, 2007; Finn & Frone, 2004; Murdock & Anderman, 2006; Murdock, Hale, & Weber, 2001). A consistent finding amongst these studies is students’ overall feelings about and attitudes toward school and teachers are predictive of academic cheating behaviors (Anderman & Midgley, 2004; Calabrese & Cochran, 1990; Finn & Frone, 2004; Murdock et al., 2001; Taylor, Pogrebin, & Dodge, 2006). In particular, students’ disidentification with school and overall negative emotional perceptions of school and teachers are associated with increased academic cheating reports (Calabrese & Cochran, 1990; Finn & Frone, 2004; Murdock et al., 2001). Also, students’ perceptions of classroom performance goal structures are linked to reported cheating behaviors (Anderman et al., 1998; Murdock et al., 2001). In essence, it is likely that students who cheat—particularly those in the middle- and high-school levels—may do so as a consequence of being in a formal learning environment that (a) stresses performance or ability above deep cognitive processing and comprehension, and/or (b) makes students feel undervalued, mistreated, alienated, and disrespected (Anderman & Midgley, 2004; Evans & Craig, 1990).

The purpose of this study is to extend this line of research by examining students’ perceptions of home–school dissonance and their association with academic cheating reports. Home–school dissonance is defined as the perceived difference between the values and operations extant in students’ home or out-of-school environment and those salient throughout their formal schooling experiences (Arunkumar, Midgley, & Urdan, 1999; Kumar, 2006).The literature contains several anecdotal and empirical reports of the role that perceived home–school dissonance has on student performance and its psychological antecedents (Arunkumar et al., 1999; Gay, 2000; Kumar, 2006).

No study to date, however, has examined whether home–school dissonance is a significant predictor of academic cheating. Examining the association between home–school dissonance and academic cheating with a high school sample is particularly noteworthy, as academic cheating peaks during high school (Cizek, 1999; Davis, Grover, Becker, & McGregor, 1992; Evans & Craig, 1990; Jensen et al., 2002). In addition, this study will also explore the psychological process of academic cheating by examining whether students’ reports of amotivation mediate the relationship between home–school dissonance and academic cheating. A review of the literature on academic cheating, its sources, and correlates such as home–school dissonance is provided below.

Literature Review

Academic Cheating

Athanasou and Olasehinde (2002) describe academic cheating as students’ conscious involvement or participation in deception (i.e., lying, falsifying, misrepresenting, corruption, plagiarism, copying, or the unlawful assistance provided to someone else), typically for the purpose of performing well or giving the perception of performing well on an academic task. Cizek (1999) offers three classifications of cheating behaviors, which include (a) cheating by taking, giving, or receiving information from others (e.g., copying another student’s work with or without their permission), (b) cheating through the use of forbidden materials or information (e.g., plagiarism), or (c) cheating by circumventing the process of assessment (e.g., a student taking or having someone else take an examination for another).

Academic Cheating Explanations

Several theoretical frameworks in the sociological literature have sought to explain academic cheating behaviors (Michaels & Miethe, 1989; Vowell & Chen, 2004). In particular, deviance behaviors such as academic cheating have been linked to deterrence theory, rationale choice theory, and social bond theory. Deterrence theory finds that the probability of engaging in deviant behaviors such as academic cheating is linked to an individual’s personal and vicarious experiences with punishment and/or punishment avoidance (Sitren & Applegate, 2007; Stafford & Warr, 1993). Rationale choice theory argues that deviant behaviors such as academic cheating result from an individual’s rationalization of the cost and benefits of executing such behaviors (Sullivan, 2006). Social bond theory suggests that academic cheating is the result of eroding social bonds (e.g., attachment, identification, or commitment) to the social institution or the persons within it (Vowell & Chen, 2004). Additional sociological explanations of academic cheating include social strain theory (Agnew, 1992; Vowell & Chen, 2004) and differential association theory (Michaels & Miethe, 1989; Tibbets & Myers, 1999).

While Murdock and Anderman (2006) note that most of the research studies on academic cheating are not grounded in a central uniform theory, much of the psychological literature has linked the phenomenon to various motivational perspectives. Recognizing the need to situate research on academic cheating into a logical framework, Murdock and Anderman (also Anderman & Murdock, 2007) propose a conceptual model that underscores the motivational processes as well as the individual and contextual factors that precede the decision to engage in academic cheating. In particular, Murdock and Anderman’s conceptual model of academic cheating employs Bronfenbrenner’s (1979) ecological systems theory and several achievement motivation theories, including intrinsic motivation (Deci & Ryan, 1985) and achievement goal theories (Ames, 1992; Dweck & Leggett, 1988). Ecological systems theory identifies multiple ecologies (e.g., microsystem, mesosystem, exosystem, macrosystem, and chronosystem) in which development, most notably cognitive development, occurs. In their model, Murdock and Anderman introduce several micro-level factors, which include the immediate social and academic settings of the learner (e.g., home and school and those persons within each) that may directly or indirectly influence his or her decision to engage in academic cheating.

The second set of theories referred to in Murdock and Anderman’s model include intrinsic motivation and achievement goal theories. Each theory seeks to explain variance in achievement and achievement-producing behavior by examining students’ rationale for engaging in school work (Murdock & Anderman, 2006). In particular, intrinsic motivation and achievement goal theorists argue that engagement in academic cheating is typically not reflective of students with a relatively high intrinsic motivation and/or mastery goal orientation (i.e., learners with a heightened orientation toward understanding and deep cognitive processing). Rather, academic cheating is considered characteristic of students with high extrinsic motivation and performance goal orientations (i.e., learners more interested in positive external indicators of accomplishment; Murdock & Anderman, 2006). Thus, in their model, Murdock and Anderman suggest that a performance goal orientation as well as extrinsic motivation is directly linked to the propensity to engage in academic cheating behaviors. Also included in this conceptual model is the work of Wigfield and Eccles (2000) expectancy-value model. Specifically, Murdock and Anderman suggest that the expectation for success or failure, along with the perceived costs and values of the academic task a learner engages in, is linked to his or her decision to engage in academic cheating.

Indeed, the Murdock and Anderman’s (2006) conceptual model begins to depict the multifaceted process of academic cheating. However, the conceptual model offered by Murdock and Anderman does not exhaust the list of possible individual and contextual factors that may be predictive of academic cheating behaviors. That is, the conceptual model does identify some orientations (e.g., performance goal orientation) and attitudes (e.g., “negative view of self”) said to precede the behavioral propensity to engage in academic cheating. However, there are other psychological/individual as well as contextual factors that may be equally predictive of academic cheating while also fitting into the conceptual model of academic cheating. Given that students’ perceptions of their learning environments—particularly its values—are linked to academic cheating reports (Anderman & Maehr, 1994; Anderman & Midgley, 2004; Baird, 1980; Evans & Craig, 1990; Finn & Frone, 2004; Murdock et al., 2001), it is likely that perceptions of home–school dissonance may also explain some variance in academic cheating reports.

Home–School Dissonance

Though several educational researchers have offered anecdotal evidence to support the existence and effects of home–school dissonance (Gay, 2000; Ladson-Billings, 1994), much of the data offering empirical evidence of such effects have been found in the work of Kumar. Borrowing from the work of Phelan, Davidson, and Cao (1991), Kumar defines home–school dissonance as the perceived differences between the values and operations extant in students’ home or out-of-school environment and those salient throughout their formal schooling experiences (Arunkumar et al., 1999; Kumar, 2006). According to Kumar, students from all grade levels experience dissonance when the cultural values, beliefs, and norms of their home are incongruent with those found in the school. In particular, Arunkumar and colleagues note that “students from cultures outside the mainstream may experience a sense of dissonance when they encounter a devaluing of their beliefs and behaviors at schools that reflect the dominant White, middle-class ideology” (p. 442). Other education researchers agree that differences in home and school values and operations are linked to issues of culture (Gay, 2000; Ladson-Billings, 1994). Some work has corroborated this claim (Tyler, Boykin, Miller, & Hurley, 2006).

Throughout Kumar’s research, along with several other ethnographic accounts (Gay, 2000; Phelan et al., 1991), the effects of exposure to a dissimilar or dissonant learning environment have proven to be debilitating for many students, including White, middle-class students. For example, Arunkumar et al. found no significant differences in reports of home–school dissonance between African American and European American middle-grade students. However, they showed that students reporting high levels of home–school dissonance also reported lower levels of future hopefulness, academic efficacy, self-esteem, and grade point average (GPA). These students also reported higher levels of anger and self-deprecation (Arunkumar et al., 1999).

In a later study, Kumar (2006) employed home–school dissonance as a criterion variable. Specifically, Kumar used multilevel growth curve analysis in a study examining the associations between middle school students’ perceptions of classroom goal structures and teachers’ reported classroom practices and home–school dissonance. Analyses revealed that students’ perceptions of classroom performance goals were predictive of home–school dissonance. In addition, teachers’ reported mastery goal instructional practices were significantly related to decrease in home–school dissonance as students made the transition from elementary to middle school.

Taken together, these studies suggest that home–school dissonance can be viewed as both a predictor of various achievement outcomes and their psychological antecedents (i.e., academic efficacy) and a criterion variable predicted by several contextual factors (e.g., perceptions of classroom goal structure and teacher-based goal orientation instruction). Given the predictive nature of home–school dissonance in the aforementioned research (Arunkumar et al., 1999), it is expected that home–school dissonance reports will be predictive of academic cheating. That is, if students’ reports of home–school dissonance are related to lowered academic efficacy, self-esteem, hopefulness, and current performance indices such as GPA, then it is likely that—in a performance-oriented context such as the public school classroom—students would resort to academic cheating when exposed to home–school dissonance. Furthermore, students may engage in academic cheating to offset the lowered feelings of academic efficacy and achievement associated with home–school dissonance (Anderman & Murdock, 2007; Murdock & Anderman, 2006).

Since the literature has also suggested that academic cheating is most prevalent during the high school years, it is important to examine the link between home–school dissonance and academic cheating with high school students. Murdock and Anderman’s (2006) conceptual model links several psychological (e.g., self-efficacy) and contextual (e.g., classroom goal orientation) variables to academic cheating. It does not, however, fully capture those variables that may also be statistically associated with academic cheating. As Murdock and Anderman suggest, academic cheating is largely a motivational issue.

Academic Motivation

Academic motivation is situated in self-determination theory (Deci & Ryan, 1985, 2000), which postulates that human behavior is linked to various forms of motivation: intrinsic motivation, extrinsic motivation, and amotivation. Intrinsic motivation focuses on the satisfaction an individual experiences as a result of his or her engagement in particular behaviors or exhibition of particular behaviors. For Deci and Ryan (1985, 2000), there are three types of intrinsic motivation: motivation to know, motivation to accomplish things, and motivation to experience stimulation. Motivation to know involves performing an activity for the satisfaction that one experiences while learning, exploring, or trying something new. Motivation to accomplish involves engaging in an activity for the personal satisfaction of accomplishing a task or creating something. Motivation to experience stimulation involves engaging in an activity to experience sensory pleasure or excitement. Vallerand et al. (1993) found that high levels of intrinsic motivation were associated with academic persistence. More recently, Cokley and colleagues (Cokley, Bernard, Cunningham, & Motoike, 2001) found that intrinsic motivation was positively associated with academic self-concept.

Extrinsic motivation focuses on performing activities as a means to reach a particular goal. There are three types of extrinsic motivation: external regulation (behavior is regulated through external means such as rewards and constraints), introjected regulation (behavior is regulated by the expectations of others), and identified regulation (behavior that is internalized because of external factors). Vallerand et al. (1993) found that external and introjected regulations were unrelated to academic persistence. However, identified regulation was positively associated with persistence on academic tasks.

Amotivation is described as behaviors that do not facilitate the achievement of a specific goal and is considered the lowest level of motivation (Vallerand & Bissonnette, 1992). According to Cokley and colleagues (2001), individuals who are amotivated do not possess behavioral characteristics that reflect neither intrinsic or extrinsic motivation. Early research by Vallerand et al. (1993) showed that amotivation was inversely associated with academic persistence among college students.

Given this finding, it is expected that reports of amotivation will be positively associated with academic cheating reports. That is, unlike previous studies that have examined the type of motivation students have reported having prior to engaging in academic cheating behaviors (e.g., intrinsic or extrinsic, mastery vs. performance goal; Anderman & Murdock, 2007), the current study will examine whether amotivation is predictive of academic cheating reports. Based on the findings of Arunkumar et al. (1999) and the literature linking contextual factors to motivation (Anderman et al., 1998; Anderman & Murdock, 2007), it is also expected that amotivation will be predicted by home–school dissonance. Finally, it is anticipated that the association between home–school dissonance and academic cheating is mediated by lowered levels of achievement motivation (e.g., amotivation).

The current study will address these issues by examining the home–school dissonance, amotivation, and academic cheating reports of African American and European American high school juniors and seniors. High school juniors and seniors were chosen for participation as these student groups have been shown to engage in academic cheating behaviors significantly more than high school freshman and sophomores (Schab, 1991). Moreover, the literature examining the role of motivation in academic behaviors has utilized mostly middle-grade samples (Anderman & Murdock, 2007; Murdock & Anderman, 2006). The major research questions driving the current study are, “Does home–school dissonance significantly predict academic cheating among high school students?” and “Is this relationship mediated by amotivation”?

Method

Sample

A total of 344 high school students from two randomly selected high schools in the Southeastern region of the country participated in the current study. The majority of the students at both high schools were African American (60% and 84%, respectively). As much as 74% of the sample participants were on free and/or reduced lunch, 63% were female, 64% were juniors, and 44% were 17 years of age. The average GPA of all students was 2.98.

Instruments

The Academic Motivation Scale: College Version (AMS: College Version; Vallerand & Bissonnette, 1992) is a 28-item self-report measure used to assess students’ intrinsic, extrinsic, and amotivation. The AMS has seven different subscales, each of which corresponds to a different form of motivation. Each subscale contains four items. Scale responses for the AMS: College Version are recorded using a Likert-type scale ranging from 1 (does not correspond at all) to 7 (corresponds exactly). Vallerand and Bissonnette reported Cronbach’s αs ranging from .83 to .86 for the subscales and test–retest reliability estimates over a 1-month period ranging from .71 to .83. Though the factor structure of the AMS was initially derived from a sample of Canadian undergraduates, Cokley and colleagues (2001) found evidence to support the seven-factor structure of the AMS with an American sample of undergraduates. The Amotivation subscale of the AMS was used in the current study. Sample items for the Amotivation subscale include, “Honestly, I don’t know; I really feel that I am wasting my time in school.” Alpha reliability for the Amotivation subscale in the current study was .90.

Patterns of Adaptive Learning Scales

The Patterns of Adaptive Learning Scale (PALS; Midgley et al., 2000) was developed to examine the relationship between student motivation, affect, and behavior and the learning environment. The scale is comprised of items that assess (a) Personal Achievement Goal Orientation; (b) Perceptions of Teacher’s Goals; (c) Academic-related Perceptions, Beliefs, Attitudes, and Strategies; (d) Perceptions of Parents and Home Life and other subscales. Items on the PALS are scored on a 5-point Likert-type scale from 1 (not at all true) to 5 (very true). The PALS have been administered to ethnically diverse sample groups at the elementary-, middle-, and high-school levels in low- to middle-class households.

The Cheating Behavior subscale (3 items) was used in the current study to assess high school students’ reports of cheating at school. The α reliability coefficient for the Cheating Behavior subscale in the current study was .86. A sample item from the Cheating Behavior subscale was, “I sometimes copy answers from other students during tests.” Also, the Dissonance Between Home and School subscale (five items) was used in the current study. The α reliability coefficient for this subscale was .88. A sample item from the Dissonance Between Home and School subscale was, “I feel troubled because my home life and my school life are like two different worlds.”

Procedures

Institutional Review Board (IRB) approval was obtained from the institution hosting the research. Approval for research was also granted by the associate superintendent for research for the public school system serving students at the two high schools. Subsequently, an initial meeting was arranged with high school administrative personnel to introduce the study and to coordinate data collection. Written informed consent was obtained from participants aged 18 and older. For students below age 18, both written informed consent from the student’s parent or legal guardian, in addition to written assent, were obtained prior to survey completion. The survey packet was administered to participants during a single classroom session, and students were given 45 minutes to complete the survey protocol.

Results

Data Analysis Plan

Several preliminary analyses were performed before the analyses germane to the initial research questions were executed. To begin, a MANCOVA will be computed to determine whether scores on academic cheating, amotivation, and home–school dissonance vary as a function of ethnicity, gender, and/or class rank. GPA served as the covariate, thereby controlling for student achievement effects. GPA is self-reported in the current study. In addition, a bivariate correlation matrix will be computed to determine whether significant associations emerged among home–school dissonance, amotivation, and academic cheating. Along with these correlation analyses, a series of linear regression models will be computed to determine whether (a) home–school dissonance was predictive of amotivation, (b) amotivation was predictive of academic cheating, and (c) home–school dissonance was predictive of academic cheating. According to several researchers (Baron & Kenny, 1986; MacKinnon, Fairchild, & Fritz, 2007), these analyses are aligned with the criteria necessary to examine mediation effects. Finally, a SOBEL test of mediation (Preacher & Hayes, 2004) will be performed to determine whether amotivation mediates the relationship between home–school dissonance and academic cheating.

MANCOVA

A MANCOVA was computed to determine whether scores in home–school dissonance, amotivation, and academic cheating vary as a function of ethnicity, gender, and class rank, with GPA as a covariate. No significant F statistics emerged for the ethnicity, gender, or class rank variables, F(3, 272) = 0.66, p = .58, h² = .01; F(3, 272) = 0.75, p = .52, h² = .01; and F(3, 272) = 0.54, p = .78, h² = .01. Interactions between the variables were also statistically insignificant.

Bivariate Correlations

A correlation matrix was computed to determine the bivariate associations among the variables included in the MANCOVA. Consistent with the MANCOVA results, the associations between the ethnicity and class rank demographic variables and the outcome variables of interest did not reach statistical significance. Although the gender variable was significantly correlated with both amotivation (r = –.13, p = .02) and home–school dissonance (r = –.13, p = .02), given the sample size, the strength of the associations was relatively weak. Here, female high school students reported lower amotivation scores and lower home–school dissonance scores than did their male counterparts. In addition, home–school dissonance was significantly correlated with amotivation (r = .29, p = .01) and academic cheating (r = .21, p = .01). Also, amotivation was significantly associated with academic cheating reports (r = .27, p = .01). Table 1 presents the descriptive statistics, including dependent variable means and bivariate correlations.

Table 1.

Descriptive and Correlation Matrix.

	Student race	Gender	Grade	Home–school dissonance	AM	AC	α	Mean (SD)	AA	EA	M	F	J	S
Student race	1	−.12*	−.13*	.06	.06	.06	—	—	—	—	—	—	—	—
Student gender		1	−.00	−.11	−.13*	−.06	—	—	—	—	—	—	—	—
Student grade			1	−.06	−.10	−.09	—	—	—	—	—	—	—	—
Home–school dissonance				1	.29**	.20**	.86	2.28 (0.90)	2.30	2.49	2.42	2.38	2.28	2.41
Amotivation (AM)					1	.27**	.90	3.47 (0.61)	3.52	3.41	3.62	3.29	3.53	3.44
Academic cheating (AC)						1	.88	2.69 (1.15)	2.65	3.01	2.81	2.88	2.75	2.75

Note: AA = African American student mean; EA = European American student mean; M = male student mean; F = female student mean; J = junior (high school) student mean; S = senior (high school) student mean.

p ≤ .05. **p ≤. 01.

Multiple Regression Analyses

Regression analyses were computed to determine the predictive ability of home–school dissonance and amotivation on academic cheating. Given that student demographic characteristics were not significantly associated with home–school dissonance, amotivation, or academic cheating, the regression models only examined home–school dissonance and amotivation as predictors of academic cheating. The first regression model contained home–school dissonance as the predictor of academic cheating and emerged statistically significant, F(1, 311) = 12.01, p = .001. Home–school dissonance accounted for 4% of the variance in academic cheating. The standardized β coefficient for home–school dissonance was .19 (t = 3.47, p = .001). Here, a one-unit increase in home–school dissonance was related to a .19 increase in academic cheating reports.

The second regression model contained both home–school dissonance and amotivation as predictors of academic cheating. It also emerged statistically significant, F(1, 311) = 14.64, p = .001. Here, both home–school dissonance and amotivation accounted for 9% of the variance in academic cheating, with amotivation accounting uniquely for 5%. The standardized β coefficients for home–school dissonance and amotivation in the second model were .12 (t = 2.19, p = .02) and .23 (t = 4.08, p = .01), respectively. Here, a one-unit increase in home–school dissonance was linked to a .12-point increase in academic cheating reports. Similarly, a one-unit increase in amotivation was linked to a .23-point increase in academic cheating reports.

Path Analysis

The stipulations reported by Baron and Kenny (1986) to test a mediation model were examined in order to perform a path analysis with the three variables. Though some research has presented alternative methods to examine the presence of mediation effects (Frazier, Tix, & Barron, 2004; MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002; Mallinckrodt, Abraham, Wei, & Russell, 2006; Shrout & Bolger, 2002), the Sobel analysis has been regarded as a sufficient procedure to examine mediation effects (Preacher & Hayes, 2004). In particular, to determine whether amotivation mediates the association between home–school dissonance and academic cheating, there must be significant predictions of (a) home–school dissonance on amotivation, (b) amotivation on academic cheating, and (c) home–school dissonance on academic cheating. Given that the previous regression analyses had already revealed significant associations between home–school dissonance and academic cheating and amotivation and academic cheating, an association between home–school dissonance and amotivation must emerge statistically significant in order to test for mediation effects. A simple regression model was computed with home–school dissonance as the predictor variable and amotivation as the criterion variable. Results revealed that home–school dissonance was a significant predictor of amotivation, F(1, 312) = 28.56, p = .001 (t = 5.35, p = .001), thus, allowing for a test of the mediating effects of amotivation on home–school dissonance and academic cheating to be conducted. The standardized β coefficient for this association was .29.

Having met the requirements to test mediation, a path analysis was conducted. It was expected that the association between home–school dissonance and academic cheating would be lowered when reports of amotivation were included in analysis. That is, we expected amotivation to partially mediate the association between home–school dissonance and academic cheating. Partial mediation occurs when the β coefficient describing the association between the initial predictor of interest and the criterion variable is significantly reduced, but not to zero (MacKinnon et al., 2007).

A Sobel (1982) analysis was used to detect partial mediation. Sobel analysis reveals whether there is an indirect effect resulting from the mediation. That is, a Sobel analysis tests the null hypothesis suggesting that the indirect effect—when the relationship between the initial predictor variable of interest and the criterion variable is mediated by the identified mediator variable—is zero. In a Sobel analysis, a z statistic is used to determine whether “the difference between the path coefficient excluding the mediator and the path coefficient including the mediator is significantly different from zero” (National Institute of Child Health and Human Development [NICHD], 2003, p. 588). Recent research has advanced criteria to determine the sample size needed to detect significant mediation effects at a power of .8 (Cohen, 1988; Fritz & MacKinnon, 2007) Specifically, when the path coefficient from the initial predictor to the proposed mediator and the path coefficient from the mediator to the criterion variable are larger than .26, at least 196 participants are necessary to detect mediation with large power. With .29 and .23 as the β coefficients for the associations between home–school dissonance and amotivation and amotivation and academic cheating, the current sample of 344 was sufficient to detect significant mediation effects (Fritz & MacKinnon, 2007).

The Sobel analysis revealed that the relationship between home–school dissonance and academic cheating was significantly reduced by amotivation scores (mediation z statistic = 3.25, p = .001; β = .19, p = .001; β = .12, p = .02; with amotivation as mediator). Figures 1 and 2 illustrate the mediation analysis and its findings.

Figure 1.

Simple path from home-school dissonance to academic cheating.

Figure 2.

Simple path from home-school dissonance to academic cheating mediated by amotivation.

Discussion

The current study had several foci. One was to determine whether reports of home–school dissonance, amotivation, and academic cheating were linked to demographic variables reported by high school juniors and seniors. The current research on academic cheating suggests that the characteristics of students who engage in academic cheating behaviors are varied and not necessarily or solely linked to demographic factors such as gender or class rank (Evans & Craig, 1990; Miller & Murdock, 2007). Similarly, the literature has shown no significant differences in home–school dissonance as a function of race or gender (Arunkumar et al., 1999).

In the current study, no significant differences emerged in home–school dissonance, amotivation, or academic cheating scores as a function of student race, gender, or class rank. Similar findings of no statistically significant association between race, gender, or class rank and academic cheating behaviors and home–school dissonance have been reported (Anderman et al., 1998). Significant correlations emerged between gender and amotivation and gender and home–school dissonance. Though the strength of these associations were relatively weak, such findings are consistent with the literature. For example, Vallerand et al. found that female college students reported significantly higher intrinsic and extrinsic motivation scores than male students. Given that female students typically score higher than males on both intrinsic and extrinsic forms of motivation (Cokley et al., 2001; Vallerand et al., 1993), their lower amotivation scores are aligned with previous literature.

Another focus of the current study was to determine whether home–school dissonance was predictive of academic cheating. Also to be determined was whether the relationship between home–school dissonance and academic cheating was mediated by amotivation. The rationale for examining the predictive nature of home–school dissonance on academic cheating was based on some research that has shown that students’ perceptions of home–school dissonance is oftentimes linked to maladaptive academic outcomes (e.g., GPA) and their psychological antecedents (e.g., low self-esteem) (Arunkumar et al., 1999; Kumar, 2006). Similarly, in an attempt to examine the process of academic cheating for high school students, it was believed that amotivation scores would mediate the relationship between home–school dissonance and academic cheating. That is, home–school dissonance negatively affects students, particularly their motivation to excel in school and, as a result, will be linked—through amotivation—to academic cheating reports.

Regression analyses determined significant associations between each of the variables of interest. Particularly, home–school dissonance predicted amotivation and academic cheating and amotivation was also predictive of academic cheating. With these significant associations, a test of mediation was run to determine whether amotivation was a significant mediator of the relationship between home–school dissonance and academic cheating. A Sobel analysis determined that amotivation reduced the size of the association between home–school dissonance and academic cheating, thus indicating that amotivation was a significant mediator. That is, the difference between the path coefficient (between home–school dissonance and academic cheating) excluding the mediator (e.g., amotivation) and the path coefficient including the mediator was significantly different from zero.

The results discussed here continue in the line of research purporting the adverse academic experiences of students exposed to home–school dissonance (Arunkumar et al., 1999; Kumar, 2006). In particular, inclusion of a high school sample (a sample reported to have a significantly high incidence of cheating), along with an examination of the association between home–school dissonance and academic cheating, provides the literature with a better understanding of the sources of academic cheating for this student population. To begin, findings suggest that there is little significant difference between home–school dissonance, amotivation, or academic cheating reports among African American and European American students, male and female students, and junior and senior high school students. Furthermore, the findings in the current study suggest that home–school dissonance reports are linked to high school students feeling amotivated in school and academic cheating reports. Moreover, amotivation was shown to mediate the relationship between home–school dissonance and academic cheating reports.

While these results may inform educators and education researchers of additional factors that significantly predict academic cheating among high school students, several limitations persist. To begin, the nature of this study was correlational, and thus, causality cannot be implied throughout the findings. In addition, academic cheating was self-reported and it is likely that students may have attempted to underestimate the degree to which they engage in these behaviors (Anderman et al., 1998; Anderman & Murdock, 2007; Athanasou & Olasehinde, 2002; Murdock & Anderman, 2006). Finally, while the home–school dissonance measure has been used in previous research to detect students’ experiences with the phenomenon, there is no indication of what these perceived differences actually are between home and school. Though much of the literature has suggested that the student-perceived differences between home and school are situated within distinct cultural values (Gay, 2000), the PALS Home–school Dissonance subscale does not determine whether these differences are cultural in nature. Future studies should develop scales measuring the cultural nature of perceived home–school dissonance reports.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This research was supported by a grant provided to the 1st and 2nd authors by the Office of the President, University of Kentucky.

Author Biographies

Lynda Brown-Wright, PhD is Professor and Associate Provost of Faculty Affairs at Georgia State University.

Kenneth M. Tyler, PhD is Associate Professor of Educational Psychology at the University of Kentucky.

Danelle Stevens-Watkins, PhD is Assistant Professor of Clinical Psychology at Spalding University.

Deneia Thomas, PhD is Associate Professor of Educational Psychology at Eastern Kentucky University.

Shambra Mulder, PhD is Assistant Professor of Psychology at Kentucky State University.

Travonia Hughes, PhD is a Research Associate at Johns Hopkins University.

Ruby Stevens-Morgan, PhD is Assistant Director, Office of Institutional Research, Prairie View A&M University.

Clarissa Roan-Belle is a Doctoral Candidate in School Psychology at the University of Kentucky.

Nadia Gadson, PhD is Director of Academic Affairs, Southern A&M University.

La Toya Smith is a Doctoral Candidate in Counseling Psychology at the University of Kentucky.

References

Agnew

(1992). Foundation of a general strain theory. Criminology, 30, 47-87.

Ames

(1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84, 261-271.

Anderman

E. M.

Griesinger

Westerfield

(1998). Motivation and cheating during early adolescent. Journal of Educational Psychology, 90, 84-93.

Anderman

E. M.

Maehr

M. L.

(1994). Motivation and schooling in the middle grades. Review of Educational Research, 64, 287-309.

Anderman

E. R.

Midgley

(2004). Changes in self-reported academic cheating across the transition from middle school to high school. Contemporary Educational Psychology, 29, 499-517.

Anderman

E. M.

Murdock

T. B.

(2007). The psychology of academic cheating. San Diego, CA: Elsevier.

Arunkumar

Midgley

Urdan

(1999). Perceiving high or low home-school dissonance: Longitudinal effects on adolescent emotional and academic well-being. Journal of Research on Adolescence, 9, 441-466.

Athanasou

J. A.

Olasehinde

(2002). Male and female differences in self-report cheating. Practical Assessment, Research, & Evaluation, 8(5). Retrieved from http://PAREonline.net/getvn.asp?v=8&n=5

Baird

J. S.

(1980). Current trends in college cheating. Psychology in the Schools, 17, 515-522.

10.

Baron

R. M.

Kenny

D. A.

(1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.

11.

Bronfenbrenner

(1979). The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard University Press.

12.

Calabrese

R. L.

Cochran

J. T.

(1990). The relationship of alienation to cheating among a simple of American adolescents. Journal of Research and Development in Education, 23, 65-72.

13.

Cizek

G. J.

(1999). Cheating on tests: How to do it, detect it, and prevent it. Mahwah, NJ: Lawrence Erlbaum.

14.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

15.

Cokley

K. O.

Bernard

Cunningham

Motoike

(2001). A psychometric investigation of the Academic Motivation Scale using a United States sample. Measurement and Evaluation in Counseling and Development, 34, 109-119.

16.

Davis

S. F.

Grover

C. A.

Becker

A. H.

McGregor

L. N.

(1992). Academic dishonesty: Prevalence, determinants, techniques, and punishments. Teaching of Psychology, 19, 16-20.

17.

Deci

Ryan

R. M.

(1985). Intrinsic motivation and self-determination in human behavior. New York, NY: Plenum.

18.

Deci

Ryan

R. M.

(2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227-268.

19.

Dweck

C. S.

Leggett

E. L.

(1988). A social-cognitive approach to motivation and personality. Psychological Review, 95, 256-273.

20.

Evans

E. D.

Craig

(1990). Adolescent cognitions for academic cheating as a function of grade level and achievement status. Journal of Adolescent Research, 5, 325-345.

21.

Evans

E. D.

Craig

Mietzel

(1993). Adolescents’ cognitions and attributions for academic cheating: A cross-national study. The Journal of Psychology, 127, 585-602.

22.

Finn

K. V.

Frone

M. R.

(2004). Academic performance and cheating: Moderating role of school identification and self-efficacy. The Journal of Educational Research, 97(3), 116-122.

23.

Frazier

P. A.

Tix

A. P.

Barron

K. E.

(2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51, 115-134.

24.

Fritz

M. S.

MacKinnon

D. P.

(2007). Required sample size to detect the mediated effect. Psychological Science, 18, 233-239.

25.

Gay

(2000). Culturally responsive teaching. New York, NY: Teachers College Press.

26.

Graham

M. A.

Monday

O’Brien

Steffen

(1994). Cheating at small colleges: An examination of student and faculty attitudes and behaviors. Journal of College Student Development, 35, 255-260.

27.

Jensen

L. A.

Arnett

J. J.

Feldman

S. S.

Cauffman

(2002). It’s wrong, but everybody does it: Academic dishonesty among high school and college students. Contemporary Educational Psychology, 27, 209-228.

28.

Kerkvliet

Sigmund

C. L.

(1999). Can we control cheating in the classroom? Journal of Economic Education, 30, 331-343.

29.

Kumar

(2006). Students’ experiences of home-school dissonance: The role of school academic culture and perceptions of classroom goal structures. Contemporary Educational Psychology, 31, 253-279.

30.

Ladson-Billings

(1994). The dreamkeepers: Successful teachers of African American children. San Francisco, CA: Jossey-Bass.

31.

MacKinnon

D. P.

Fairchild

A. J.

Fritz

M. S.

(2007). Mediation analysis. Annual Review of Psychology, 58, 593-614.

32.

MacKinnon

D. P.

Lockwood

C. M.

Hoffman

J. M.

West

S. G.

Sheets

(2002). A comparison of methods to test mediation and other intervening variables effects. Psychological Methods, 7, 83-104.

33.

Mallinckrodt

Abraham

W. T.

Wei

Russell

D. W.

(2006). Advances in testing the statistical significance of mediation effects. Journal of Counseling Psychology, 53, 372-378.

34.

McCabe

D. L.

(1999). Academic dishonesty among high school students. Adolescence, 34, 681-687.

35.

McCabe

D. L.

(2001). Student cheating in American high school. Retrieved from http://www.academicintegrity.org

36.

McCabe

D. L.

Trevino

L. K.

(1993). Academia dishonesty: Honor codes and other contextual influences. Journal of Higher Education, 64, 522-538.

37.

McCabe

D. L.

Trevino

L. K.

(1997). Individual and contextual influences on academia dishonesty: A multicampus investigation. Research in Higher Education, 38, 379-396.

38.

Michaels

J. W.

Miethe

T. D.

(1989). Applying theories of deviance to academic cheating. Social Science Quarterly, 70, 870-885.

39.

Midgley

Maehr

M. L.

Hruda

L. Z.

Anderman

Freeman

K. E.

Urdan

(2000). Manual for the Patterns of Adaptive Learning Scales. Ann Arbor: University of Michigan.

40.

Miller

A. D.

Murdock

T. B.

(2007). Modeling latent true scores to determine the utility of aggregate student perceptions as classroom indicators in HLM: The case of classroom goal structures. Contemporary Educational Psychology, 32, 83-104.

41.

Murdock

T. B.

Anderman

E. M.

(2006). Motivational perspectives on student cheating: Toward an integrated model of academic dishonesty. Educational Psychologist, 41(3), 129-145.

42.

Murdock

T. B.

Hale

N. M.

Weber

M. J.

(2001). Predictors of cheating among early adolescents: Academic and social motivations. Contemporary Educational Psychology, 26, 96-115.

43.

National Institute of Child Health and Human Development. (2003). Do children’s attention processes mediate the link between family predictors and school readiness? Developmental Psychology, 39, 581-593.

44.

Phelan

Davidson

A. L.

Cao

H. T.

(1991). Students’ multiple worlds: Negotiating the boundaries of family, peer, and school cultures. Anthropology and Education Quarterly, 22, 223-249.

45.

Preacher

K. J.

Hayes

A. F.

(2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717-731.

46.

Schab

(1991). Schooling without learning: Thirty years of cheating in high school. Adolescence, 26, 839-847.

47.

Shrout

P. E.

Bolger

(2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422-445.

48.

Sitren

A. H.

Applegate

B. K.

(2007). Testing the deterrent effects of personal and vicarious experience with punishment and punishment avoidance. Deviant Behavior, 28(1), 29-55

49.

Sobel

M. E.

(1982). Asymptotic confidence intervals for indirect effects in structural equation models. In Leinhart

(Ed.), Sociological methodology (pp. 290-312). San Francisco, CA: Jossey-Bass.

50.

Stafford

M. C.

Warr

(1993). A reconceptualization of general and specific deterrence. Journal of Research in Crime and Delinquency, 30(2), 123-135.

51.

Sullivan

(2006). Students as rational decision-makers: The question of beliefs and attitudes. London Review of Education, 4, 271-290.

52.

Taylor

Pogrebin

Dodge

(2006). Advanced placement-advanced pressures: Academic dishonesty among elite high school students. Educational Studies, 33, 403-422.

53.

Tibbets

S. G.

Myers

D. L.

(1999). Low self-control, rational choice, and student test cheating. American Journal of Criminal Justice, 23, 179-200.

54.

Tyler

K. M.

Boykin

A. W.

Miller

O. A.

Hurley

E. A.

(2006). Cultural values in the home and school experiences of low-income African American students. Social Psychology of Education, 9, 363-380.

55.

Vallerand

R. J.

Bissonnette

(1992). Intrinsic, extrinsic, and amotivational styles as predictors of behavior: A prospective study. Journal of Personality, 60, 599-620.

56.

Vallerand

R. J.

Pelletier

L. G.

Blais

M. R.

Briere

N. M.

Senecal

Vallieres

E. F.

(1993). On the assessment of intrinsic, extrinsic, and amotivation in education: Evidence on the concurrent and construct validity of the Academic Motivation Scale. Educational and Psychological Measurement, 53, 159-172.

57.

Vowell

P. R.

Chen

(2004). Predicting academic misconduct: A comparative test of four sociological explanations. Sociological Inquiry, 74, 226-249.

58.

Whitley

B. E.

(1998). Factors associated with cheating among college students: A review. Research in Higher Education, 39, 235-274.

59.

Whitley

B. E.

Nelson

A. B.

Jones

C. J.

(1999). Gender differences in cheating attitudes and classroom cheating behavior: A meta-analysis. Sex Roles, 41, 657-680.

60.

Wigfield

Eccles

J. S.

(2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68-81.