Investigating the Association Between Home-School Dissonance and Disruptive Classroom Behaviors for Urban Middle School Students

Abstract

Disruptive classroom behaviors are a major schooling dilemma in urban schools. While several contextual and motivational factors have been statistically associated with disruptive classroom behaviors, one overlooked factor has been home-school dissonance. The current study examined the relationship between 260 middle school students’ reports of perceived home-school dissonance, several motivational antecedents of academic performance, and disruptive classroom behaviors. Six hundred sixty middle school students completed six subscales of the Patterns of Adaptive Learning Scales (PALS), including the Home-School Dissonance subscale, Mastery Goal, Performance Approach, and Performance Avoidance Goal Orientations, and the Disruptive Classroom Behavior subscales. Home-school dissonance scores were significantly associated with lower mastery goal orientation and lower academic efficacy scores. Home-school dissonance scores were also significantly associated with higher disruptive classroom behavior scores and higher performance approach and performance avoidance goal orientation scores. In addition, structural equation modeling with multiple mediators showed that mastery goal orientation and performance approach goal orientation mediated the relationship between home-school dissonance and disruptive classroom behavior.

Keywords

home-school dissonance disruptive classroom behavior middle school students structural equation modeling

Disruptive classroom behaviors are considered the most prevalent behavioral problems reported during early adolescence (Bidell & Deacon, 2010; Little, 2005; Murphy, Theodore, Aloiso, Alric-Edwards, & Hughes, 2007; Myers & Pianta, 2008). Disruptive classroom behaviors have been classified as externalizing behaviors, where students exhibit hostile and aggressive behaviors (Finn, Fish, & Scott, 2008). Some examples of disruptive classroom behaviors include refusing to cooperate in class, interrupting peers and classroom teachers, making distracting noises, unexcused class tardiness, fighting, bullying, and disrespecting teachers (Finn et al., 2008; Stewart, 2003).

There are several contextual, interpersonal, and student-based factors associated with disruptive classroom behavior (Thomas, Bierman, Thompson, Powers, & The Conduct Problems Prevention Research Group, 2008). The contextual factors include class size, parent and community-based socioeconomic status, classroom seating arrangements and goal structures, school discipline policies, and teachers’ classroom management strategies (DiLalla & Mullineaux, 2008; Fleming et al., 2008; Guardino & Fullerton, 2010; Way, 2011). Some interpersonal sources of disruptive classroom behavior include the student-teacher relationship, teachers’ job satisfaction, peer relationships, and parental involvement (Gu, Lai, & Ye, 2011; Powers, Bierman, & The Conduct Problems Prevention Research Group, 2013). Student-based factors linked to disruptive classroom behavior include students’ emotional disturbance, diminished self-concept, and perceived academic competence (Bauermeister, Zimmerman, Barnett, & Caldwell, 2007; Bidell & Deacon, 2010; Thomas et al., 2008). Indeed, the literature is replete with examples of several contextual and person-centered factors that are associated with disruptive classroom behaviors. The goal of the current study is to examine home-school dissonance as an additional factor possibly associated with disruptive classroom behaviors.

Home-School Dissonance

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, Midgley, & Urdan, 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, “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).

For several educational researchers, the salience of home-school dissonance or home-school cultural discontinuity is high in urban, low-income schools attended by predominantly ethnic minority students (Gay, 2010; Tyler, Dillihunt, et al., 2008; Tyler, Uqdah, et al., 2008). In schools predominated by ethnic minority students, research literature reveals a propensity toward the maintenance of specific cultural value-based behaviors, activities, and curricula that often times does not reflect the integrity-based socialization experiences that this population of students is exposed to outside school (Gay, 2010; Lee, 2001; Tyler, Dillihunt, et al., 2008). Some research has corroborated the claim that many students of color, particularly African American students from low-income backgrounds, are socialized toward specific cultural values that are not salient and in most cases, discontinued throughout their classroom learning experiences (Boykin, Tyler, Watkins-Lewis, & Kizzie, 2005; Tyler, Boykin, Miller, & Hurley, 2006; Tyler, Dillihunt, et al., 2008). These same authors agree that the cultural discontinuity experiences they face may be associated with poor schooling experiences and outcomes, including but not limited to academic failure/difficulty and/or heightened disciplinary action (e.g., Neal, McCray, & Webb-Johnson, 2001).

In fact, although not entirely synonymous with home-school cultural discontinuity, as this construct explicitly identifies certain cultural values extant in both home and formal schooling contexts, the effects of home-school dissonance have proven to be perilous for many middle school students. For example, Arunkumar et al. (1999) showed that middle school 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) used multilevel growth curve analysis to examine the associations between middle school students’ perceptions of classroom goal structures, teachers’ reported classroom practices, and home-school dissonance. Findings revealed that middle school students’ perceptions of classroom performance goals were associated with higher home-school dissonance reports. In addition, teachers’ reported mastery goal instructional practices were significantly related to decreases in home-school dissonance (Kumar, 2006).

With these reported findings on home-school dissonance and given some research that has critically discussed the adverse impact of cultural discontinuity on the schooling experiences of low-income students, particularly students of color, it is expected that home-school dissonance reports in the current study will be positively associated with disruptive classroom behavior reports. That is, if middle school students exposed to home-school dissonance have been shown to report lower academic efficacy, self-esteem, hopefulness, and GPA (Arunkumar et al., 1999; Kumar, 2006), then it is likely that they would resort to disruptive classroom behaviors as well, perhaps in protest of their exposure to home-school dissonance (i.e., learning conditions that do not foster a full acceptance of the students’ identities and/or aspects of their home environments).

Also, some research has shown that home-school dissonance scores are associated with lower scores on varying motivational factor reports (e.g., achievement goal orientations and academic efficacy), which typically precede school-based behavioral outcomes (e.g., academic cheating; Arunkumar et al., 1999; Kumar, 2006). Thus, it is likely that the home-school dissonance produces an indirect effect on disruptive classroom behavior through the motivational factors of achievement goals, as it has been suggested that (a) achievement goal orientations in particular and motivational factors (e.g., academic efficacy) in general depend, in part, on situational/contextual characteristics (e.g., home-school dissonance; Ames, 1992; Anderman, Urdan, & Roeser, 2003) and (b) achievement goal orientations and academic efficacy are associated with achievement and classroom behavior outcomes (Anderman et al., 2003; Midgley et al., 1998)

The purpose of the current study is to extend the line of research on disruptive classroom behaviors by examining urban students’ perceptions of home-school dissonance and their association with disruptive classroom behavior reports. The contribution of the current study to the larger literature is the investigation of the association between home-school dissonance—which is perceived by students—and their reports of their own disruptive classroom behaviors. As stated above, some research has evidenced an association between home-school dissonance and various psychological antecedents of academic performance, along with indices of average academic performance (i.e., GPA). In addition, Brown-Wright and colleagues (2011; Brown-Wright et al., 2013) have shown that home-school dissonance has been associated with higher academic cheating and disruptive classroom behavior reports among high school students. However, reports of home-school dissonance and their association with middle grade students’ reports of disruptive classroom behaviors have yet to be examined. Therefore, the current study seeks to determine whether perceptions of home-school dissonance—as reported by a sample of middle grade students attending urban public schools—are statistically associated with self-reported disruptive classroom behaviors. The major research question driving this study asks, “Does the reported degree of dissonance between home and school have any association with middle school students’ disruptive classroom behaviors?” In addition, this study will also explore the psychological process involved in the exhibition of disruptive classroom behaviors. Specifically, the study seeks to determine whether middle school students’ reports of home-school dissonance have an indirect effect on disruptive classroom behavior reports through reported achievement goal orientations (i.e., mastery goal, performance approach, and performance avoidance orientations) and academic efficacy. The current study will examine the hypothesis that home-school dissonance will be associated with higher disruptive classroom behaviors. In addition, home-school dissonance is expected to be associated with lower mastery goal orientation and academic efficacy and higher performance goal approach and performance goal avoidance orientations.

Method

Sample

Eight hundred thirty-seven (N = 837) middle school students from two randomly selected urban middle schools located in low-income communities in the Southeastern region of the United States participated in a study that focused on cultural attitudes and beliefs and learning. Data examined in the current study were gathered from 260 middle grade participants (N = 260). All the participants for this current study were drawn from one of the aforementioned middle schools. Nearly 41% of the analytic sample was African American, while 31% of the sample was Caucasian, 21% was Latino, and 6% was Asian American. The sample was almost split evenly for gender; 56% of the participants were female and 44% were male. In addition, 31% of the sample emerged from the sixth grade, nearly 35% from the seventh grade, and 34% from the eighth grade. The age range for middle school participants in this sample was 10 years to 15 years with 12 years of age being the largest percentage (31.4%). Average age of middle school participants was 12.39 (SD = 1.06). Of the students who were not given the survey, 42% were African American, 41% were Caucasian, 4.5% were Latino, and 5% were Asian American. In addition, 51% of these students were male and 49% were female.

Instruments

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. Items on the PALS are scored on a 5-point Likert-type scale from 1 (not at all true) to 5 (very true). Responses to all PALS subscales are self-reported by students in the current study, although some studies have investigated aspects of the PALS with adult populations (i.e., classroom teachers; Kumar, 2006). The PALS have been administered to ethnically diverse samples in elementary, middle, and high schools. In addition, several manuscripts have discussed the construct validation of the Achievement Goal Orientation subscales on the PALS (i.e., Mastery, Performance Approach, and Performance Avoidance) with mixed results for each goal orientation (see Anderman et al., 2003; Midgley et al., 1998; Midgley et al., 2000). Below is a brief summary section detailing each PALS subscale used in the current study. Items for all PALS subscales used in the current study are located in Table 1.

Table 1.

PALS Item Descriptive Statistics.

Item		$\bar{X}$	SD	Skewness (CR)	Kurtosis (CR)
Home-School Dissonance
x1	I feel troubled because my home life and my school life are like two different worlds	2.52	1.41	0.45 (2.95)	−1.08 (−3.55)
x2	I feel uncomfortable when my parents come to school, because they are different from the parents of many of my classmates.	2.38	1.39	0.55 (3.60)	−0.96 (−3.14)
x3	I feel upset because my teacher and my parents have different ideas about what I should learn in school.	2.32	1.37	0.62 (4.06)	−0.87 (−2.87)
x4	I am not comfortable talking to many of my classmates because my family is different from theirs.	1.94	1.19	1.06 (6.93)	0.09 (0.31)
x5	I don’t like to have my parents come to school because their ideas are very different from my teachers’ ideas	2.68	1.38	0.31 (2.04)	−1.02 (−3.35)
Disruptive Classroom Behavior
y1	I sometimes get into trouble with my teacher during most of my classes.	2.50	1.35	0.52 (3.43)	−0.86 (−2.81)
y2	I sometimes behave in a way during most of my classes that annoys my teacher.	2.46	1.29	0.53 (3.48)	−0.75 (−2.47)
y3	I sometimes annoy my teacher during most of my classes	2.40	1.34	0.60 (3.91)	−0.79 (−2.59)
y4	I sometimes don’t follow my teacher’s directions during most of my classes.	2.21	1.20	0.71 (4.67)	−0.36 (−1.19)
y5	I sometimes disturb the lesson that is going on in most of my classes.	2.51	1.24	0.52 (3.39)	−0.65 (−2.11)
Mastery Goal Orientation
m1	It’s important to me that I learn a lot of new concepts this year.	3.92	1.14	−0.91 (−5.97)	0.15 (.494)
m2	One of my goals is to master a lot of new skills this year.	3.82	1.13	−0.64 (−4.23)	−0.25 (−0.83)
m3	One of the goals in most of my classes is to learn as much as I can.	4.10	1.12	−1.11 (−7.31)	0.38 (1.23)
m4	It’s important to me that I improve my skills this year.	4.06	1.06	−0.93 (−6.10)	−0.03 (−0.09)
m5	It’s important to me that I thoroughly understand my class work.	3.86	1.12	−0.67 (−4.40)	−0.36 (−1.17)
Performance Avoidance Goal Orientation
m6	It’s important to me that I don’t look stupid in most of my classes.	3.43	1.30	−0.37 (−2.43)	−0.91 (−2.97)
m7	One of the goals in most of my classes is to avoid looking like I have trouble doing the work.	2.87	1.29	0.10 (0.63)	−0.96 (−3.14)
m8	It’s important to me that teachers in most of my classes don’t think that I know less than others in class.	2.94	1.32	−0.01 (−0.06)	−1.06 (−3.48)
m9	One of my goals is to keep others from thinking I’m not smart in most of my classes.	2.87	1.47	0.10 (0.66)	−1.31 (−4.30)
Performance Approach Goal Orientation
m10	One of my goals is to look smart in comparison to the other students in my class.	3.09	1.44	−0.05 (−0.31)	−1.24 (−4.07)
m11	It’s important to me that I look smart compared to others in my classes.	2.78	1.35	0.21 (1.34)	−1.08 (−3.54)
m12	One of my goals is to show others that I’m good at my class work.	3.44	1.25	−0.34 (−2.21)	−0.89 (−2.93)
m13	It’s important to me that other students in most of my classes think I am good at my class work.	3.13	1.29	−0.06 (−0.37)	−.1.02 (−3.34)
m14	One of my goals is to show others that class work is easy for me.	2.97	1.28	0.06 (0.38)	−0.94 (−3.09)
Academic Efficacy
m15	I’m certain I can master the skills taught in class this year.	3.73	1.13	−0.50 (−3.30)	−0.58 (−1.89)
m16	I’m certain I can figure out how to do the most difficult class work.	3.59	1.20	−0.45 (−2.97)	−0.70 (−2.31)
m17	I can do almost all the work in class if I don’t give up.	3.95	1.16	−0.84 (−5.49)	−0.27 (−.89)
m18	Even if the work is hard, I can learn it.	3.98	1.14	−0.89 (−5.86)	−0.18 (−0.59)
m19	I can do even the hardest work in most of my classes if I try.	3.84	1.19	−0.78 (−5.15)	−0.27 (−0.87)

Note. PALS = Patterns of Adaptive Learning Scale; CR = critical ratio.

Disruptive classroom behavior

The Disruptive Classroom Behavior subscale (three items, α = .86) is the criterion variable. A sample item from the Disruptive Classroom Behavior subscale was, “I sometimes annoy my teacher during class.”

Mastery goal orientation

The Mastery Goal Orientation subscale (five items, α = .85) is used as one of the meditational variables in the current study. A sample item from this subscale is, “One of my goals in class is to learn as much as I can.”

Performance-approach goal orientation

Another subscale serving as a motivational factor believed to mediate the association between home-school dissonance and disruptive classroom behavior is the Performance Approach Goal Orientation subscale (five items, α = .89). A sample item from this subscale is, “One of my goals is to show others that I’m good at my class work.”

Performance avoidance goal orientation

The Performance Avoidance Goal Orientation subscale (four items, α = .74) is also used as one of the meditational variables in the current study exploring the relationship between home-school dissonance and disruptive classroom behavior. A sample item from this subscale is, “One of my goals in class is to avoid looking like I have trouble doing the work.”

Academic efficacy

Finally, the Academic Efficacy subscale (five items, α = .78) from the PALS is explored in the current study as a possible meditational variable in the current study. A sample from the Academic Efficacy subscale includes, “Even if the work is hard, I can learn it.”

Home-school dissonance

The Home-School Dissonance subscale (five items, α = .88) was the principal independent variable in the current study. A sample item from the Home-School Dissonance subscale items includes, “I feel troubled because my home life and my school life are like two different worlds.” Construct validity information for the Home-School Dissonance subscale is found in Arunkumar et al. (1999) and Kumar (2006). Specifically, the items, which are published in Arunkumar et al.’s (1999) research, were reported to form a single factor explaining 43% of the variance. Kumar (2006) also noted that additional construct validity for the Home-School Dissonance subscale emerged from interviews with 49 students who scored high on the scale during initial piloting. According to Kumar (2006), all students interviewed indicated “. . . that they experienced dissonance in school either because of a clash in values, beliefs, and behaviors, between home and school, or because they felt very different from most of their peers in school” (p. 260). Please note that the current study used the Home-School Dissonance scale provided by Midgley and associates, which is only five items instead of the six utilized by Arunkumar et al. (1999) and Kumar (2006). There was no construct validity information provided for this version of the Home-School Dissonance subscale in the PALS Manual (Midgley et al., 2000).

Procedures

Institutional review board (IRB) approval was obtained from the institution hosting the research. The associate superintendent for research for the public school system identified and granted approval for research at the two urban middle schools. Subsequently, an initial meeting was arranged with each school’s administrative personnel to introduce the study and schedule data collection. Written informed consent was obtained from participants’ parents while student assent was obtained from all participants prior to survey completion. Participation rate was 31% (837 = total number of surveys administered after receipt of parental consent/260 = total number of surveys submitted). The paper and pencil survey packet was administered to participants during a single classroom session in 1 day, and students were given 45 minutes to complete the survey protocol. Two graduate research assistants, along with the principal investigator, monitored data collection, ensured confidentiality prior to survey completion, and were present to answer any participant inquiries about the survey items in particular or the research in general. Student participants were also encouraged to raise their hand at any point and ask a member of the research team if there was a question they could not understand. Student participants each received a US$5 department store gift card for survey completion.

Data Analytic Plan

Univariate analyses were conducted using SPSS v21 (SPSS, Inc., 2012) to examine normality. Examination of skew and kurtosis statistics, histograms (with normality curves), normality Q-Q plots, and boxplots indicated that analytic sample data were somewhat asymmetrically distributed. While skewness and kurtosis statistics fell within a range of ±1 for most items, indicating a lack of significant non-normality (Kline, 1998), critical ratio (CR) values associated with the skewness and kurtosis statistics fall outside the ±2 range for some items (indicating statistically significant degree of non-normality). Each item’s skewness and kurtosis, and their associated CRs are presented in Table 2. In addition, multivariate normality was assessed using Mardia’s joint multivariate index. A Mardia’s coefficient of 156.27, with an accompanying CR value of 29.59, was computed for the parallel multiple mediation model tested in this study. It has been suggested that CR values greater than five indicate data with non-normal distributions (Byrne, 2010). It is likely that the items collectively do not have a joint multivariate normal distribution.

Table 2.

Factor Loadings.

	Mediated model			Non-mediated model
	Std. β	SE	95% CI	Std. β	SE	95% CI
Home-School Dissonance (F1)
x1	.66***	.04	[.58, .74]	.66***	.05	[.57, .75]
x2	.71***	.04	[.63, .79]	.72***	.04	[.63, .80]
x3	.73***	.03	[.66, .80]	.74***	.04	[.67, .82]
x4	.55***	.05	[.44, .65]	.53***	.06	[.41, .65]
x5	.62***	.04	[.54, .71]	.63***	.05	[.53, .73]
Disruptive Classroom Behavior (F6)
y1	.67***	.04	[.59, .75]	.66***	.05	[.57, .75]
y2	.78***	.03	[.73, .84]	.78***	.03	[.72, .85]
y3	.73***	.03	[.66, .79]	.72***	.04	[.64, .80]
y4	.76***	.03	[.71, .82]	.75***	.03	[.69, .82]
y5	.67***	.04	[.59, .74]	.66***	.05	[.54, .76]
Mastery Goal Orientation (F2)
m1	.74***	.03	[.67, .81]
m2	.60***	.04	[.51, .68]
m3	.77***	.03	[.71, .83]
m4	.63***	.03	[.56, .70]
m5	.48***	.05	[.38, .58]
Performance Avoidance (F3)
m6	.60***	.05	[.51, .69]
m7	.57***	.05	[.48, .66]
m8	.71***	.04	[.62, .79]
m9	.48***	.06	[.37, .59]
Performance Approach (F4)
m10	.75***	.03	[.68, .82]
m11	.72***	.03	[.66, .79]
m12	.60***	.04	[.51, .68]
m13	.61***	.03	[.54, .68]
m14	.67***	.03	[.60, .73]
Academic Efficacy (F5)
m15	.69***	.03	[.63, .75]
m16	.72***	.03	[.66, .77]
m17	.71***	.03	[.64, .78]
m18	.64***	.05	[.55, .73]
m19	.66***	.04	[.58, .74]
Latent Factors
F6 on F1	.46***	.07	[.33, .60]	.59***	.06	[.48, .71]
F6 on F2	−.33***	.05	[−.44, −.23]
F6 on F3	−.01ns	.07	[−.15, .13]
F6 on F4	.18**	.07	[.06, .31]
F6 on F5	−.02ns	.07	[−.15, .11]
F2 on F1	−.16*	.07	[−.31, −.02]
F3 on F1	.34***	.07	[.20, .49]
F4 on F1	.35***	.06	[.23, .47]
F5 on F1	−.20***	.06	[−.33, −.08]

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

A MANOVA was computed to determine whether scores on disruptive classroom behaviors, mastery, performance approach, performance avoidance goal orientations and academic efficacy, and home-school dissonance vary as a function of ethnicity, gender, and/or class rank. Statistically significant MANOVA results were followed by univariate ANOVAs. In addition, a bivariate correlation matrix was computed to determine whether significant associations emerged among home-school dissonance, the motivational variables, and disruptive classroom behaviors.

Using Mplus v 7.4 (Muthén & Muthén, 1998-2015), two latent variable models were constructed. The first model examined the direct relationship between home-school dissonance and disruptive classroom behavior (without the presence of any mediator variables). The second model, a parallel multiple mediator structural equation model (SEM) was analyzed to better understand the direct and indirect relationships between home-school dissonance and disruptive classroom behavior in the presence of four mediating variables—mastery goal orientation, performance avoidance goal orientation, performance approach goal orientation, and academic efficacy. Parallel mediation analysis allows for the exploration of complex models in which the strength of direct and indirect effects of multiple mediators can be simultaneously compared, affording researchers the opportunity to test competing theoretical perspectives and further clarify paths between independent and dependent variables of interest (Hayes, 2013; Lau & Cheung, 2012; Preacher & Hayes, 2008). In addition, in the SEM environment, modeling all variables as latent factors, with individual survey items serving as factor indicators, allows for the control of measurement error, which reduces the negative influence random measurement error can have on parameter estimation (i.e., biased parameter estimates and lower statistical power to detect mediation effects; Cheung & Lau, 2008; Hayes, 2013). Two latent variables were created with five indicators each to represent home-school dissonance (Items x1-x5) and disruptive classroom behavior (Items y1-y5). A description of items is reported in Table 1. The three achievement goal orientations and academic efficacy were all added to the previous model also as latent factors to create the parallel mediation model—mastery goal orientation (Items m1-m5), performance avoidance goal orientation (Items m6-m9), performance approach goal orientation (Items m10-m14), and academic efficacy (Items m15-m19; see Figure 1).

Figure 1.

Direct and multiple mediation models.

The models were estimated using the MLMV estimator (Mean and Variance Adjusted Maximum Likelihood) available within Mplus, which provides parameter estimates that are robust to non-normality, and it functions well with small sample sizes (Asparouhov, 2005; Muthén & Muthén, 1998-2015). Symmetric confidence intervals (90%, 95%, 99%) were also constructed around each estimated effect. Model fit statistics were generated to assess the fit between the covariance structure as specified by the model and the covariance structure as observed in the data. Sample data fit to the model was assessed by chi-square (CMIN; χ²), and normed chi-square (CMIN/df; where CMIN is χ² and df is degrees freedom) values. Chi-square values with significance levels of p > .05 indicate that the model is a good fit to the data, whereas significance levels of p < .05 indicate poor model fit. In addition, the normed chi-square (CMIN/df) can give an indication of model fit with small sample sizes (because χ² is sensitive to sample size). Values between 2.0 and 5.0 have been recommended as reflecting reasonable model fit, with values less than 2.0 indicating acceptable fit (Bollen, 1989). Additional indices of model fit included absolute fit, as assessed by the goodness of fit index (GFI) and the adjusted goodness of fit index (AGFI). These fit indices reflect how well the model fits the sample data, with values close to 1.00 indicating good fit (Byrne, 2010). Also, the comparative fit index (CFI) is a measure of incremental fit and reflects the comparison of the hypothesized model with the null model. Values closer to 1 indicate the hypothesized model fits the data well. The root mean square error of approximation (RMSEA) was used as it gives an indication of how well the model would fit the population covariance matrix. Values less than .05 are considered to indicate good fit (Byrne, 2010).

As discussed above, mediation analysis with multiple mediators allows for the identification and investigation of specific mediating effects that describe the unique mediating abilities of each variable (controlling for other mediators present in the model). This can be helpful when teasing apart effects of mediating variables that are correlated with each other, and when trying to minimize parameter estimation bias that stems from the omission of additional relevant mediators (Preacher & Hayes, 2008). Therefore, additional parameters were defined and requested within Mplus to estimate specific indirect effects and to compare the strength of specific indirect effects identified in the model (Cheung & Lau, 2008; Lau & Cheung, 2012).

Results

Prior to conducting the planned analyses, the analytic data set was assessed for missing data points. Thirteen percent of the cases (34 cases) in the analytic sample contained missing data points (which translated into 1.1% missingness of all data points overall). The 34 cases that contained missing data points were not significantly different from the remaining cases with complete data. Missing data imputation via expectation-maximization (EM) or multiple imputation is used to help raise statistical power for hypothesis testing in analyses using small sample sizes with missing data points (Cheema, 2014). Therefore, in the preliminary analyses (assumptions checking, univariate/bivariate analyses, MANOVAs, and ANOVAs) carried out in SPSS v21 (SPSS, Inc., 2012), missing data points were imputed using EM. For the main analysis (SEM), missing data were imputed in Mplus v7.4 (Muthén & Muthén, 1998-2015) using a multiple imputation method (based on Bayesian analysis, see Rubin, 1987; Schafer, 1997). Ten data sets were generated, and parameter estimates for requested analyses were computed using averaged values across the generated data sets.

Tables 1 and 3 present the descriptive statistics and bivariate correlations for the study variables. Statistically significant F statistics emerged for the ethnicity, F(18, 693) = 2.91, p < .01, η² = .07, and gender, F(6, 250) = 3.48, p < .01, η² = .08, variables. Using a univariate ANOVA for the ethnicity variable, significant F statistics emerged only for home-school dissonance, F(3, 254) = 3.08, p = .03, η² = .04, and disruptive classroom behavior, F(3, 254) = 7.67, p = .00, η² = .08, variables. African American middle school students, on average, reported higher scores on home-school dissonance (2.50 vs. 2.22) and disruptive classroom behavior (2.70 vs. 2.12) than their White counterparts. Regarding gender, significant univariate ANOVAs yielded significant F statistics for the home-school dissonance, F(1, 257) = 6.21, p = .01, η² = .02; mastery goal orientation, F(1, 257) = 7.33, p = .007, η² = .03; performance avoidance, F(1, 257) = 6.31, p = .03, η² = .02; and academic efficacy, F(1, 257) = 4.96, p = .03, η² = .02 variables. Specifically, analyses revealed that male middle school students had higher scores for home-school dissonance (2.54 vs. 2.23) and performance avoidance goal orientation (3.20 vs. 2.89) than their female counterparts; while female middle school students had higher scores for mastery goal orientation (4.07 vs. 3.80) and academic efficacy (3.93 vs. 3.68) than their male counterparts. The Cohen’s d statistic for each mean difference, however, yielded small to medium effects (see Table 4). That is, mean differences for gender and ethnicity main effects were between small and medium effect sizes, specifically for the disruptive classroom behavior means for African American and White middle grade students (as determined by Cohen’s d criteria for small [.20], medium [.50], and large [.80] effect sizes; Cohen, 1992; Thalheimer & Cook, 2002).

Table 3.

Means, Standard Deviations, Alpha Coefficients, and Zero-Order Correlations of Study Variables.

Variables	$\bar{X}$	SD	α	1	2	3	4	5	6
1. Home-School Dissonance	2.37	0.99	.79	—
2. Disruptive Classroom Behavior	2.42	1.01	.84	.50**	—
3. Academic Efficacy	3.82	0.88	.82	−.18**	−.23**	—
4. Mastery Goal	3.95	0.81	.78	−.15**	−.32**	.64**	—
5. Performance Approach	3.07	0.99	.81	.26**	.20**	.29**	.32**	—
6. Performance Avoidance	3.03	0.96	.68	.24**	.18**	.21**	.23**	.69**	—

p < .01.

Table 4.

Effect Sizes of Significant Mean Differences.

Groups	Cohen’s d
Groups	Home-school dissonance	Disruptive classroom behavior	Mastery goal orientation	Performance avoidance orientation	Academic efficacy
African American	.29	.59	—	—	—
Caucasian	.29	.59	—	—	—
Female	.31	—	.33	.31	.27
Male	.31	—	.33	.31	.27

In addition to these mean differences, bivariate correlation analyses revealed significant associations between home-school dissonance and (a) each motivational variable serving as a mediator in the current study and (b) disruptive classroom behavior, which served as the major dependent variable of the study. Specifically, home-school dissonance scores were positively associated with disruptive classroom behavior scores (r = .50, p < .01), performance approach goal orientation scores (r = .26, p < .01), and performance avoidance goal orientation scores (r = .24, p < .01). Home-school dissonance scores were also negatively associated with mastery goal orientation scores (r = −.15, p < .01) and academic efficacy scores (r = −.18, p < .01). In addition, disruptive classroom behavior scores were negatively associated with mastery goal orientation scores (r = −.32, p < .01) and academic efficacy scores (r = −.23, p < .01) and positively associated with performance approach goal orientation scores (r = .20, p < .01) and performance avoidance goal orientation scores (r = .18, p < .01).

The results of the mediation analysis are presented in Tables 5 and 6. The first model examined the direct relationship between home-school dissonance and disruptive classroom behavior without the presence of any mediator variables. The overall fit of this model was good as indicated by χ²(34, 260) = 34.61, p > .05, and the normed chi-square, χ²/df = 1.02. Review of additional fit indices also indicates that the model was a good fit to the data: GFI (0.96), AGFI (0.93), CFI (0.99), and RMSEA (0.01). The total standardized direct effect between home-school dissonance and disruptive classroom behavior was significant (.62, p < .001). That is, disruptive classroom behavior can be expected to increase .62 standard deviations for an increase in home-school dissonance of one full standard deviation in our sample of middle grade students.

Table 5.

Fit Indices for Mediation Models.

Model	χ² (df)	χ²/df	GFI	AGFI	CFI	RMSEA
Non-mediated model	34.61 (34)	1.02	.96	.93	.99	.01
Mediated model	597.96 (368)	1.62	.82	.79	.80	.05

Note. χ²/df = normed chi-square; GFI = goodness of fit index; AGFI = adjusted goodness of fit index; CFI = comparative fit index; RMSEA = root mean square error of approximation.

Table 6.

Structural Equation Modeling Results: Total, Direct, and Indirect Effects.

	Std. β (SE)	95% CI	Sig. (p value)
Non-mediation model
Total effect (Home-School Dissonance on Disruptive Classroom Behavior)	.59 (.06)	[.48, .71]	.000
Multiple mediation model
Total effect (Home-School Dissonance on Disruptive Classroom Behavior)	.58 (.06)	[.45, .69]	.001
Direct effect (Home-School Dissonance on Disruptive Classroom Behavior)	.47 (.07)	[.33, .60]	.000
Total indirect effect (Home-School Dissonance on Disruptive Classroom Behavior via all four mediators)	.12 (.04)	[.03, .20]	.008
Specific indirect effect (through mastery goal)	.05 (.02)	[.00, .10]	.027
Specific indirect effect (through performance approach)	.07 (.02)	[.01, .10]	.011
New/additional parameters
DM1 (direct effect − specific indirect effect through mastery)	.40 (.08)	[.24, .56]	.000
DM2 (direct effect − specific indirect effect through performance approach)	.39 (.09)	[.21, .56]	.000
DM3 (difference between mastery and performance approach indirect effects)	−.01 (.04)	[−.08, .06]	ns

Note. DM1 = (b61 − b21 × b62); DM2 = (b61 − b41 × b64); DM3 = (b21 × b62 − b41 × b64). CI = confidence interval.

Although the chi-square statistic testing fit for the multiple mediator model was significant, χ²(368, 260) = 597.96, p < .05, the normed chi-square χ²/df = 1.62 indicates an acceptable fit for this model. Review of additional fit indices also indicates that the model was an acceptable fit to the data: GFI (0.82), AGFI (0.79), CFI (0.80), and RMSEA (0.05). The standardized direct effect between home-school dissonance and disruptive classroom behavior was significant (.47, p < .001). Hence, an increase in disruptive classroom behavior (.47 standard deviations) is associated with an increase in home-school dissonance of one full standard deviation in our sample of middle grade students. The standardized total indirect effect between home-school dissonance and disruptive classroom behavior was positive and significant (.12, p = .008), indicating that disruptive classroom behavior is influenced by home-school dissonance through the set of mediators considered in the model (i.e., achievement goal orientations and academic efficacy). An increase in home-school dissonance of a full standard deviation is associated with an increase in disruptive classroom behavior of .12 standard deviations, through its influence on mastery goal, performance approach, performance avoidance orientations, and academic efficacy.

However, an examination of specific indirect effects in the model shows that only the mediating paths through mastery goal orientation (.05, p = .027) and performance approach orientation (.07, p = .011) were significant. Thus, controlling for the effects of the other mediators, an increase in home-school dissonance is associated with an increase in disruptive classroom behavior through influence exerted on mastery goal orientation. More specifically, a decrease in mastery goal orientation is associated with an increase in disruptive classroom behavior (when partialing out the effects of home-school dissonance; see Table 2 for factor loadings). Similarly, controlling for the effects of all other mediators contained in the model, an increase in home-school dissonance is associated with an increase in disruptive classroom behavior through influence exerted on performance approach goal orientation. Partialing out the effects of home-school dissonance, an increase in performance approach goal orientation is associated with an increase in disruptive classroom behavior.

The strengths of the two aforementioned significant meditational paths were compared with each other. The significant mediation effects were also compared with the direct effect estimated in the model (see Lau & Cheung, 2012). The difference between the two specific mediation effects (i.e., mastery goal orientation, performance approach orientation) was not statistically significant, suggesting that the specific mediation effects through mastery goal orientation and through performance approach orientation are not significantly different from one another. Comparing these specific indirect effects with the direct effect demonstrated that the direct effect is significantly larger than either of the statistically significant specific indirect effects found in the model. Last, there were no significant effects of either race or gender on any of the mediator (i.e., goal orientations and academic efficacy) and outcome (i.e., disruptive classroom behavior) variables.

Discussion

The purpose of this study was to examine the association between reports of home-school dissonance and reports of disruptive classroom behavior among urban middle school students. To better understand this association, several motivational variables were included to examine indirect effects. The means for home-school dissonance and disruptive classroom behavior were below the scale midpoints, while means for the motivational variables (i.e., achievement goal orientations and academic efficacy) were at or above the scale midpoint. The current study investigated whether home-school dissonance was statistically associated with achievement-oriented/efficacy variables that theoretically precede school-based behaviors such as classroom disruption. Overall, findings indicate that middle school student perceptions of home-school dissonance and practices of disruptive classroom behavior are (a) statistically associated, (b) relatively low, and (c) better understood through examination of mastery goal orientation and performance approach goal orientation.

Specifically, in examining the indirect effects, SEM analyses indicated that mastery goal orientation and performance approach goal orientation were the only variables to mediate the relationship between home-school dissonance and disruptive classroom behavior. The strengths of both specific indirect effects were comparable with each other, but both were of lesser strength than the direct effect associated with home-school dissonance on reports of disruptive classroom behavior (as reflected in the model). Students experiencing home-school dissonance may become less motivated to engage in mastery-oriented learning behaviors. Feeling thusly, students might choose to engage in disruptive classroom behaviors as a behavioral reaction, which may be a manifestation of the dissonance they perceive (i.e., acting out as a result of feeling isolated or marginalized in class). At the same time, students may be reacting to the shift in classroom goal structures that occurs during the transition from the elementary classroom to the middle school classroom where students become more cognizant of an emphasis on academic performance and grades, while also developmentally becoming more capable of using comparative information (i.e., grades) in their own estimations of personal competence and in socially comparative ways among their peers (Bong, 2009). Linnenbrink-Garcia and colleagues contend that, at the classroom level, students’ perceptions of a performance structure are typically associated with less adaptive behaviors, and that such emphasis on performance is not helpful to learners (Brophy, 2005; Linnenbrink-Garcia et al., 2012). Further study using multiple mediator analysis could help identify additional potential mediators that would provide a richer description of the salient motivational variables that may shed light on the unique psychological antecedents that jointly mediate the relationship between home-school dissonance and disruptive classroom behavior.

The implication of these findings for teachers of middle school students can be couched in cultural relevant instruction previously discussed at the beginning of this article. Specifically, given that the direct association between home-school dissonance and disruptive classroom behavior was significant, albeit with relatively low means, it is likely that middle school teachers are utilizing aspects of middle school students’ home and out-of-school experiences and culture during their instruction, which may keep such urban students moderately engaged and classroom disruption to a minimum. Moreover, as students begin to explore who they are during early adolescence, they will likely pay more attention to issues that address their preferences and their budding self-definitions. Thus, acknowledging, respecting, and incorporating aspects of the students’ identities throughout curriculum and teacher-student interaction can facilitate not only their identity exploration in general, but also their ideas about the type of student they see themselves as (e.g., mastery-oriented, academically efficacious). The latter then will inform the types of classroom behaviors the students display.

Study limitations include aspects of the research design including the (a) cross-sectional nature of the data, which limits causal explanations and therefore, external validity of the findings beyond a middle school sample in the Southeastern region of the United States and (b) the self-reporting of disruptive classroom behaviors and home-school dissonance, which were not paired with instruments assessing social desirability and therefore, must be interpreted with caution. Along these lines, future studies should utilize more specific measures to assess possible distinctions in middle school students’ home and school experiences as well as more nuanced measurement of disruptive classroom behavior (Kulinna, Cothran, & Regualos, 2003), especially because many educators now contend with classroom disruption that includes technology (i.e., texting, smartphone usage; Thomas, O’Bannon, & Bolton, 2013). Such studies can provide greater insights into what specific factors are different between home and school and how such differences are associated with a variety of disruptive classroom behaviors. One way to assess this would be to use multiple informant research methods (i.e., surveys with parent, teacher, and student views on home-school dissonance and disruptive classroom behavior).

In addition, future studies can benefit from the development of instruments that capture a better theoretically sound conceptualization of goal orientation. As Hulleman, Schrager, Bodmann, and Harackiewicz (2010) argued, the two most popular instruments used in research about goal constructs (i.e., PALS and Achievement Goals Questionnaire) measure the same achievement goals using language that captures different aspects of said goals (i.e., focus on appearance/self-presentation language vs. focus on normative goal language, respectively). This could result in differences in how goal constructs correlate with achievement outcomes (Hulleman et al., 2010). Finally, future studies should look to maximize the data collected from study participants by offering more effective participation incentives along with expanding the survey research format to include electronic, along with paper-and-pencil, completion. Additional time for survey completion along with greater follow-up opportunities initiated by both school staff and research team members would augment the number of surveys completed and returned.

The current study sought to examine motivational factors that may mediate the association between home-school dissonance and disruptive classroom behavior. Although identified study limitations (e.g., missing data, cross-sectional research design, construct/factor validity information for PALS subscales) restrict the results of the current study, the data show that the degree to which middle school students experience home-school dissonance is associated with the degree to which these students will engage in disruptive classroom behaviors.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Kenneth M. Tyler is an associate professor of educational psychology in the College of Education at the University of Kentucky. He has broad research interests including culture and cognitive development, home-school dissonance and cultural discontinuity, race and racism, identity development, adolescent learning and socialization processes, and African American student achievement.

Jennifer L. Burris is a doctoral student in the Department of Educational, School, and Counseling Psychology at the University of Kentucky. Her research interests include the identity development and academic achievement of African American students, and applying factor analysis and IRT analysis techniques to evaluate and improve measurements for use with multicultural populations.

Sean T. Coleman is an assistant professor in the Department of Educational Studies and Leadership at Bowie State University. He has a research concentration on comprehensive schooling and school reform that address 21st-century learning and pedagogy demands in general and the minority academic achievement gap in particular.

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