Research_Theory_Design_and_Methods__Walden_University_2016

Research Theory, Design, and Methods Walden University

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• Is the research question(s) a logical extension of the purpose of the study?

• Does the research question(s) reflect the best question to address the problem?

• Does the research question(s) align with the design of the study?

• Does the research question(s) align with the method identified for collecting data?

If the study is qualitative, does the research question(s) do as follows?

• Relate the central question to the qualitative approach

• Begin with What or How (not Why)

• Focus on a single phenomenon

• Use exploratory verbs

• Use nondirectional language

• Use an open-ended format

• Specify the participants and research site

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• Do the descriptive questions seek to describe responses to major variables?

• Do the inferential questions seek to compare groups or relate variables?

• Do the inferential questions follow from a theory?

• Are the variables positioned consistently from independent/predictor to dependent/outcome in the inferential questions?

• Is a null and/or alternative hypothesis provided as a predictive statement?

Research Theory, Design, and Methods Walden University

• Is the hypothesis consistent with its respective research question?

• Does the question(s) and/or hypothesis specify the participants and research site?

If the study is mixed methods, do the research questions and/or hypotheses do the following?

• Include the characteristics of a good qualitative research question (as listed above)

• Include the characteristics of a good quantitative research and/or hypothesis (as listed above)

• Indicate how the researcher will mix or integrate the two approaches of the study

• Specify the participants and research site

• Convey the overall intent of the study that calls for a mixed methods approach

Research Questions and Hypotheses Checklist

Violence and Victims, Volume 30, Number 2, 2015

Interpersonal Youth Violence Perpetration and Victimization in a Diverse Asian American and Pacific

Islander Adolescent Sample

Earl S. Hishinuma, PhD Janice Y. Chang, PsyD

Deborah A. Goebert, DrPH Susana Helm, PhD

Department of Psychiatry, John A. Burns School of Medicine, University of Hawai‘i at Mānoa

Iwalani R. N. Else, PhD The College of St. Scholastica, Duluth, Minnesota

Jeanelle J. Sugimoto-Matsuda, DrPH Department of Psychiatry, John A. Burns School of Medicine,

University of Hawai‘i at Mānoa

This study was the first to examine ethnic, sex, and ethnicity-by-sex differences for under- researched, Asian American and Pacific Islander, adolescent groups on youth violence outcomes other than cyberbullying. This effort included the less researched, emotional violence, and included socioeconomic status (SES) measures as covariates. The sample size from 2 high schools in spring 2007 was 881, using an epidemiologic survey design. The pattern of results was higher rates of violence victimization for ethnic groups, with lower representation in the 2 schools’ population, and ethnic groups that more recently moved or immigrated to Hawai‘i. For emotional victimization, girls of European American and “other” ethnicities self-reported higher rates than boys. Several implications (e.g., need for ethnically and gender-based approaches) and further research (e.g., ethnocultural identity) are discussed.

Keywords: interpersonal youth violence; Asian Americans; Pacific Islanders; perpetration; victimization

Y outh violence encompasses a wide range of behaviors, from teasing and name- calling to homicide. Homicide is the second leading cause of death for youth ages 15–24 years old in the United States (Centers for Disease Control and Prevention

[CDC], 2009). Yet, homicide represents only a small fraction of youth violence. Based on CDC’s 1999–2009 Youth Risk Behavior Survey (YRBS), approximately 34% of

226 Hishinuma et al.

high school students reported that they were in a physical fight in the past year, and 13% reported that they were in a physical fight on school property in the past year (Sugimoto- Matsuda, Hishinuma, & Chang, 2013). In addition, studies consistently have shown the strong relationship between violence perpetration and victimization (e.g., Ozer & McDonald, 2006) as well as the negative long-term effects into adulthood of mere expo- sure to violence and engagement in violence during childhood (Loeber & Dishion, 1983; Patterson, Crosby, & Vuchinich, 1992; Tharp-Taylor, Haviland, & D’Amico, 2009). The human toll and financial costs associated with violence in the United States are substantial (CDC, 2012; Else, Goebert, Bell, Carlton, & Fukuda, 2009; Miller, Cohen, & Wiersema, 1996; Miller, Fisher, & Cohen, 2001; Sieger, Rojas-Vilches, McKinney, & Renk, 2004; Tharp-Taylor et al., 2009). The human toll includes problems, such as physical and psy- chological pain, and adverse effects on families, communities, and society. Costs include resources related to medical and mental health care, the justice system, intervention pro- grams, and property-value decreases.

Interpersonal violence is a heterogeneous construct. One obvious distinction at the individual level is between those who perpetrate violence and those who are the victims of violence. Another dimension of violence is violence type—for example, physical versus emotional violence. Physical violence can include hitting, pushing, or shoving another person, whereas emotional violence can include social exclusion, teasing, name-calling, spreading rumors and gossip, or cyberbullying with the intent to cause harm to another person (Crick, 1997; David-Ferdon & Hertz, 2007).

ETHNIC DIFFERENCES

The association between youth violence and ethnicity has been a critically important one (Guerra & Smith, 2006; Mark & Nishigaya, 2009), especially in light of the changing ethnic demographics in the United States, whereby the projection is that the United States will not have a majority group by the Year 2043 (Frey, 2008; Yen, 2012). Previous U.S. national studies have found ethnic differences in victimization and perpetration. In general, African American, Native American, and Hispanic American youth tended to be at high- est risk, whereas European American, Asian American, and combined Asian American/ Pacific Islander adolescents tended to have the lowest violence perpetration and/or vic- timization risk (National Survey of Adolescents [Kilpatrick, Saunders, & Smith, 2003]; National Crime Victimization Survey [Lauritsen, 2003]; Snyder & Sickmund, 2006; U.S. Department of Health and Human Services, 2000; Wordes & Nunez, 2002).

Studying Asian American/Pacific Islanders (AAPIs) has become increasingly important given that this collective group has been one of the fastest growing populations in the United States for the past few decades. From Census 2000 to Census 2010, the number of Asian Americans (part or mixed) increased 46%, and the number of Pacific Islanders (part or mixed) increased 40%, compared to the number of European Americans increas- ing only 6% (U.S. Census Bureau, 2010b, 2012). In addition, the number of AAPI children increased by 31%, whereas the number of European American children decreased by 10% (O’Hare, 2011).

Although AAPIs have been shown to be at low risk for violence (Grunbaum, Lowry, Kann, & Pateman, 2000; U.S. Department of Health and Human Services, 2000; Harrell, 2009), aggregating data from these diverse groups have obscured our understanding of group dif- ferences (Lai, 2009). Efforts in the past 15 years have begun to disaggregate data on AAPIs,

Asian and Pacific Islander Youth Violence 227

providing a deeper understanding into how AAPI groups are different from one another and may have different needs in terms of violence prevention and intervention. For example, nationally, Native Hawaiian and other Pacific Islander youth have reported higher rates of violence than Asian American and European American adolescents (based on CDC’s 1999–2009 YRBS; Sugimoto-Matsuda et al., 2013).

In addition to disaggregating Asian Americans from Pacific Islanders (including Native Hawaiians), further disaggregation within the Asian American population and within the Pacific Islander population is needed to determine other ethnic differences (Mark & Nishigaya, 2009; Mark, Revilla, Tsutsumoto, & Mayeda, 2005). For example, a study in California found higher rates of serious violence among Southeast Asian youth as com- pared to Chinese American adolescents (Le & Wallen, 2006).

Like California, the State of Hawai‘i is an important setting to study ethnic differences in youth violence. Hawai‘i’s ethnically diverse population is reflective of the Pacific region and allows for cross-cultural comparisons: 57% full or part-Asian Americans, 42% full or part- European Americans, and 26% full or part-Native Hawaiians/Pacific Islanders (U.S. Census Bureau, 2010a). Researchers in Hawai‘i found that Samoan high school students had higher self-reported rates of violence than Native Hawaiian, Filipino American, and Japanese American students (Mayeda, Hishinuma, Nishimura, Garcia-Santiago, & Mark, 2006). In another study in which Hawai‘i teachers rated students, Native Hawaiian and European American students tended to engage in significantly more violent and other externalizing behaviors when compared to Asian American students (Loo & Rapport, 1998).

Hawai‘i, with the only statewide public school system in the United States, is also a place of research interest given long-standing efforts addressing school violence, includ- ing recent anti-bullying legislation directed at public schools (Vorsino, 2013). For the first time since introducing legislation regarding bullying prevention, House Bill (HB) 688 was passed and signed into law by Governor Neil Abercrombie in July 2011. HB 688 required the Hawai‘i Department of Education to heighten its collective response to bully- ing and cyberbullying as well as monitor school-level programs. As a result, in September 2011, the Hawai‘i Department of Education unveiled “Peaceful Schools,” a campaign to address not only bullying and cyberbullying but also safety and well-being as a whole. The campaign included more training for educators, heightened efforts to identify and assist youth involved in bullying, and increased prevention to stop bullying before it starts (Vorsino, 2011).

SEX DIFFERENCES

Significant sex differences between boys and girls have been found for violence perpetra- tion and victimization. Rather consistently, boys reported higher rates of victimization and physical violence perpetration than girls, whereas girls tended to report sexual vic- timization and relational violence perpetration more frequently than boys (Crick, 1997; Eagly & Steffen, 1986; National Longitudinal Study of Adolescent Health [Shaffer & Ruback, 2002]). Similarly in Hawai‘i, boys self-reported higher rates of violence than girls (Mayeda et al., 2006). However, a slightly different trend in sex differences has been shown both nationally and in Hawai‘i with respect to teen dating violence. With few exceptions, girls tended to report higher rates of victimization and perpetration than boys for most dating violence types (Archer, 2000; Baker & Helm, 2011; O’Leary, Smith Slep, Avery-Leaf, & Cascardi, 2008).

228 Hishinuma et al.

ETHNICITY-BY-SEX INTERACTION

Few studies on youth violence have been published on sex differences as a function of eth- nicity regarding AAPIs. Goodkind, Wallace, Shook, Bachman, and O’Malley (2009) found the highest rates of self-reported fighting by African Americans, followed by Hispanics, European Americans, and then by Asian Americans and Native Americans. Among boys, however, the highest rates of fighting were self-reported by Hispanics and African Americans, followed by Native Americans, European Americans, and Asian Americans. Therefore, for both girls and boys, lower self-reported rates were for Asian Americans. Sugimoto-Matsuda et al. (2013) found boys self-reported carrying weapons more than girls in the following descending order: European American, American Indian/Alaska Native, Hispanic, Asian American, mixed non-Hispanic, Native Hawaiian/Pacific Islander, African American, and mixed Hispanic youth. Goebert, Else, Matsu, Chung-Do, and Chang (2011) found Filipino American and Native Hawaiian girls reported being cyber-controlled via the web more often than their male counterparts, whereas Samoan and European American boys reported being cyber-controlled via the web more than their female counterparts. These disparate results were likely reflective of the type of youth violence examined.

SOCIOECONOMIC STATUS

An important confounding, and potentially causal, variable involves socioeconomic status (SES). In general, the lower the SES (e.g., income, occupation, education), the higher the youth violence rates (e.g., National Institute of Justice, 2007; K. Williams, Rivera, Neighbours, & Reznik, 2007). When examining free or reduced-cost lunch as a proxy for SES with an Asian American and Pacific Islander youth sample, Goebert and colleagues (2012) found that 45% received free or reduced-cost lunch, and there was a significant interaction between lunch status and ethnicity with more Native Hawaiian and Samoan students qualifying for “free or reduced-cost” lunch status than European American or Japanese American students. In addition, Singh and Ghandour (2012) found that SES, as measured by living below the poverty line and having parents with less than a high school education, was related to higher odds that children had severe behavioral problems. Higher rates of youth violence have also been associated with SES measures such as low parental education and income, decreased economic opportunities, high levels of transiency, and lack of social capital (K. Williams et al., 2007). Therefore, it is important to control statisti- cally for SES in determining differences among ethnic groups and gender.

PURPOSES

Our understanding of AAPI youth violence epidemiology has been increasing. However, gaps in the literature remain. For example, because of the nature of sampling or data col- lection procedures, previous studies (a) were not able to disaggregate within the Asian American or Pacific Islander sample (e.g., Sugimoto-Matsuda et al., 2013); (b) examined only cyber-related violence (Goebert et al., 2011); or (c) did not include relevant ethnic groups (Mayeda et al., 2006, excluded European American youth within diverse ethnic populations). When studying AAPI youth violence, there are two reasons for including other ethnic groups, including European Americans. First, European American youth can

Asian and Pacific Islander Youth Violence 229

serve as an important comparison group, given the vast majority of research has been conducted with this ethnic group. Second, although Asian Americans constitute more than 50% of Hawai‘i’s population, Hawai‘i does not have a majority ethnic group when examining disaggregated ethnicity (e.g., Japanese, Filipino, Chinese, Korean). Therefore, European Americans can be conceptualized as a minority group in Hawai‘i, with tradi- tional risk factors associated with minority group status (i.e., minority effort; Halpern, 1993) with some empirical support for this notion (Hishinuma et al., 2005). In addition, previous studies (e.g., Mayeda et al., 2006; Sugimoto-Matsuda et al., 2013) did not include SES in the model to determine whether any significant unique variance was associated with ethnicity after SES was considered. Finally, previous research (e.g., Mayeda et al., 2006; Sugimoto-Matsuda et al., 2013) generally focused on violence perpetration as opposed to victimization and emotional violence.

Therefore, the purposes of this study were as follows:

1. To determine whether there are differences across ethnic groups in Hawai‘i, including among the AAPI groups (i.e., European American, Filipino American, Native Hawaiian, Japanese American, Samoan, and other) for six different forms of youth violence (i.e., social exclusion perpetration, teasing perpetration, physical perpetration, emotional victimization, physical victimization, and overall), with the hypothesis that there will be generally higher rates for Pacific Islanders (Native Hawaiians, Samoans) and higher rates of victimization for those with low ethnic populations for the schools in question.

2. To determine whether there are differences by sex for the six youth violence measures, with the hypothesis that boys will have higher rates than girls for at least the physical violence indicators, and girls will have higher rates than boys for emotional violence measures.

3. To determine whether there are ethnicity-by-sex interactions for the six youth violence measures. 4. To determine whether the SES measures, used as covariates, alter the results involving ethnic-

ity and sex, with the hypothesis that SES will decrease the number of statistically significant findings given SES’s association with ethnicity.

METHOD

Selection of Schools

Data collection took place at two public high schools on the island of O‘ahu—the most populated island in the State of Hawai‘i (see also Goebert et al., 2011). The selected schools are located in communities populated by the ethnocultural groups of interest (Native Hawaiians, Filipino Americans, and Samoans). One of the schools comprised a large proportion of Native Hawaiian students as well as Japanese American and European American youth. The other school is from a more ethnically diverse community where more than half of this school’s student population is Filipino American. The school also serves other Pacific Islander youth, including Samoan, Marshallese, Chuukese, Tongan, and Native Hawaiian students.

Sample Description

The sample consisted of 881 high schools students (see Table 1). Ethnicity was based on self-reported ethnic identity (see “Measures” section). There were considerably more Filipino Americans, followed by Native Hawaiians and those of “Other” ethnicities, with

230 Hishinuma et al.

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232 Hishinuma et al.

the lowest frequencies for Japanese Americans, European Americans, and Samoans. There were more girls than boys; more students in the lower grade levels (i.e., greater proportion of 9th, 10th, and 11th graders as compared to 12th graders); more students who did not receive free or reduced-cost lunch than did; and more students whose main wage earners’ educational achievement were at the high school graduate, some college, or college gradu- ate levels than below these levels.

The interaction effects between ethnicity and sex, and between ethnicity and grade level were not statistically significant. There was a statistical significant interaction between ethnicity and lunch status (p , .0001; see Table 1), whereby the proportion on free or reduced-cost lunch from highest to lowest based on ethnicity was as follows: Samoans, Native Hawaiians, Filipino Americans, Others, European Americans, and Japanese Americans. There was also a statistically significant interaction between ethnicity and main wage earners’ educational achievement (p , .0001; see the following sections for description), where Native Hawaiians had generally lower attainment.

Measures

Demographic Variables. Ethnicity was determined based on the question, “Which of the following do you most strongly identify with? (bubble only one),” with the following choices: Hawaiian, Samoan, Marshallese, Chinese, Japanese/Okinawan, Black/African American, Portuguese, Filipino, Puerto Rican, Hispanic, Korean, Tongan, Chuukese, Don’t know, and Other. Students responding to the “other” category were recoded (e.g., if the student wrote in “German,” then the student was coded as being “European American”). Given the purposes of this study and the need to have sufficient sample sizes for each ethnic category, the ethnic groups were collapsed into six ethnic classifications: European American, Filipino American, Native Hawaiian, Japanese American, Samoan, and Other (see Table 1 for n sizes for each). Native Hawaiians consisted of students who were either full or part-Hawaiian because the large majority of Native Hawaiian adolescents are of mixed ancestry and such a classification system is commonly used in Hawai‘i. Japanese American youths consisted of students who were of Japanese or Okinawan heritage. The “Other” category was composed of all other students (see Table 1, Footnote c).

The following questions were used to determine sex, grade level, lunch status, and main wage earners’ educational achievement, respectively: “What is your sex?” “What is your grade level in school right now?” “Do you get free or reduced cost lunch?” and “The main wage earner is the person who makes the most money to support your family. What is the highest level of schooling for this person?” Table 1 provides the response choices and coding. The latter two variables served as measures of SES.

Interpersonal Youth Violence. Both perpetration and victimization were measured for physical and emotional-relational violence. Physical and emotional violence items mea- sured behaviors that are encountered on a regular basis in high schools, such as hitting, pushing and shoving, intimidation, and threats of physical violence. Relational violence (e.g., teasing and social exclusion) items were adapted from the Relational Aggression and Prosocial Behaviors Scale (Werner & Crick, 1999) to reflect self-reported statements about both violence victimization and violence perpetration. Students rated 18 items based on the instructions, “In the last 30 days, how many times have you . . . ” with the following rating choices: 0  never, 1  once, 2  2–3 times, and 3  4 or more times. The responses were coded to indicate whether any violence occurred (i.e., 0  no, 1  yes) to study whether a particular type of violence occurred versus the frequency of such occurrences.

Asian and Pacific Islander Youth Violence 233

To determine the more robust underlying psychological constructs of the 18 items, fac- tor analyses were used. Given the dichotomy between perpetration versus victimization and physical violence versus nonphysical violence, a four-factor solution was suggested (i.e., physical violent perpetration, physical violent victimization, nonphysical violent perpe- tration, and nonphysical violent victimization). In addition, the emotional violence perpetra- tion construct consisted of nine variables that appeared to measure two separate subconstructs, and therefore, preliminary cross-validation exploratory factor analyses were first conducted on only these nine variables. The cross-validation entailed performing the factor analyses on two separate, random halves of the dataset. The result was two forms of emotional violence perpetration (i.e., social exclusion vs. teasing). Exploratory factor analyses were not per- formed on the physical violence items because there did not appear to be such a dichotomy as with the emotional violence items. A confirmatory factor analysis was then conducted on the five-factor solution (Table 2). A reasonably good fit was obtained: root mean square error of approximation (RMSEA)  .054 (90% confidence interval  .048–.059) and comparative fit index (CFI)  .957. Because of the smaller n sizes of some of the ethnic groups, a multi- group structural equation modeling analysis was not conducted. The overall Cronbach’s alpha was .85 for all 18 items and ranged from .63 to .75 for the five factors (see Table 2). These Cronbach’s alpha values indicated adequate internal consistency, taking into account the dichotomous measurement scale of the variables and the relatively small number of items per factor. The Cronbach’s alpha (.85) for all 18 items may have been higher than any individual factor because the greater the number of items, the higher will be the Cronbach’s alpha. The composite score for each of the five domains was derived by computing the mean of the items in question. To provide equal weight to each of the five factors, the overall composite score was computed based on the mean of the five factor composite means.

Procedures

In spring 2007, the sampling strategy involved recruiting students from two public high schools on O‘ahu. To decrease selection bias, the students were recruited via their core English or science class, which were required courses that all students must pass, as opposed to elective courses. Similar procedures were used at both schools, although they were tailored to accommodate the structure of each school. Both parental permission and youth assent were required for participation. Teachers assisted in the distribution of the parental permission forms. Upon verification of written parental permission and youth assent, students were administered the survey in a group format. At one school, trained research staff read the survey to several classes in the cafeteria, and in the other school, researchers read the survey to students in their individual classrooms. A psychiatrist was “on call” should there have been any adverse reactions to being exposed to the content of the questions. The administration duration was approximately one to one-and-one-half hours. Students who did not want to participate were provided an activity to complete or they continued their regularly scheduled school coursework. Depending on the school, par- ticipants received either (a) a movie ticket and a class party for those classes whose parent- permission return rate was more than 70%, or (b) a $25 money order. The overall estimated participation rate was 33% (Goebert et al., 2011) with the ethnic breakdown of the sample (see Table 1) approximating that of the population for both schools combined (i.e., 48.1% Filipino American, 21.0% full or part-Native Hawaiian, 13.2% “Other,” 8.0% Samoan, 5.3% Japanese American, 4.2% European American; Department of Education, State of Hawai‘i, 2007). The University of Hawai‘i Institutional Review Board approved all study procedures.

234 Hishinuma et al.

TABLE 2. Measurement Model (Confirmatory Factor Analysis) of Victimization and Perpetration Factors (N  876)

Perpetration Victimization

Item # Item Description

Social Exclusion Teasing Physical Emotional Physical

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5

1 Not let another student be in your group anymore because you were mad at them?

.65

2 Told another student you wouldn’t like them unless they did what you wanted them to do?

.75

3 Tried to keep others from liking another student by saying mean things about him/her?

.78

4 Left another student out on purpose when it was time to do an activity?

.68

5 Spread rumors or gossip to create drama?

.77

6 Made up rumors because you were mad at the person?

.80

7 Said things about another student to make other students laugh?

.76

8 Teased other students? .91

9 Called other students names?

.90

10 Said you would hit a student?

.84

11 Pushed, shoved, or hit a student from your school?

.82

12 Been teased by a student?

.75

(Continued)

Asian and Pacific Islander Youth Violence 235

TABLE 2. Measurement Model (Confirmatory Factor Analysis) of Victimization and Perpetration Factors (N  876) (Continued)

Perpetration Victimization

Social Exclusion Teasing Physical Emotional Physical

Item # Item Description Factor 1 Factor 2 Factor 3 Factor 4 Factor 5

13 Been called a bad name by a student?

.81

14 Been left out on purpose by a student?

.71

15 Had something made up about you by a student?

.80

16 Had a student make sexual comments, jokes, or gestures about you?

.64

17 Been pushed, shoved, or hit by a student?

.85

18 Been told you were going to be hit by a student?

.82

Cronbach alpha (a  .85 for all 18 items)

.74 .75 .64 .72 .63

N size 864 865 866 857 871

Root mean square error of approximation (RMSEA)  .054 RMSEA 90% confidence interval  .048–.059 Comparative fit index (CFI)  .957

Data Analyses

SAS Version 9.2 statistical package was used to conduct most analyses, and Mplus Version 6.12 was used to perform the factor analysis. Means, standard deviations, and sample sizes were computed by ethnicity, sex, and ethnicity by sex for each of the five youth violence factors and the overall youth violence mean. To decrease the probability of Type I errors, multivariate analyses of variance (MANOVAs) were separately conducted with the independent variable main effects of (a) ethnicity and (b) sex, and the depen- dent variables of the five youth violence factors. Given the statistically significant results for the two main effects, analyses of variance (ANOVAs) were subsequently performed on the (a) overall model (independent variables  ethnicity, sex, and ethnicity by sex); (b) ethnicity; and (c) sex, with the dependent measure being each of the five youth violence factors and overall youth violence mean. To determine the role of SES, MANOVAs were conducted with the independent variable main effects of (a) lunch status, (b) main wage

236 Hishinuma et al.

earners’ educational achievement, and (c) combined SES measures, with the dependent measures being the five youth violence factors. ANOVAs were subsequently performed on the same three ethnicity, sex, and ethnicity-by-sex models with the two SES measures added to the model as covariates. Student Newman-Keuls subsequent tests were employed for applicable pairwise comparisons to decrease Type I errors.

RESULTS

Mean Differences

Table 3 presents the means, standard deviations, and n sizes by ethnicity, by sex, and by ethnicity and sex for each of the six violence measures.

Multivariate Analyses of Variance and Analyses of Variance

MANOVAs first were conducted to determine whether there were overall main effects for ethnicity and sex across the five violence factors. Both ethnicity (Wilks’s Lambda, F[25, 3200]  2.3, p  .0003) and sex (Wilks’s Lambda, F[5, 856]  14.4, p , .0001) were statistically significant. Table 3 presents the results of the ANOVAs.

Overall Model. The overall model included ethnicity and sex as main effects, and ethnicity by sex as the interaction effect. The overall model was statistically significant (p , .05) for four of the five factors (all but Factor 2 [teasing perpetration]) and for the overall violence mean, with the variances accounted for ranging from .018 to .045.

Ethnicity. Although ethnicity was statistically significant for only two of the five factors (i.e., Factor 1 [social exclusion perpetration] and Factor 5 [physical victimization]), subsequent pairwise t-test comparisons suggested ethnic group differences for all but Factor 2 (teasing perpetration; see Table 3). However, to decrease Type I errors, Student- Newman-Keuls subsequent tests were conducted resulting in statistically significant pair- wise comparisons for only Factor 5 (physical victimization), where European American and Samoan youth had higher self-reported physical victimization than Native Hawaiian and Japanese American adolescents.

Sex. Sex was statistically significant for three of the five factors, with boys having higher self-reported rates for the physical forms of violence as compared to girls: Factors 1 (social exclusion perpetration; girls . boys), 3 (physical perpetration; boys . girls), and 5 (physical victimization; boys . girls).

Ethnicity-by-Sex Interaction. The ethnicity-by-sex interaction effect was statistically significant for Factor 4 (emotional victimization). Figure 1 depicts Factor 4’s interaction between ethnicity and sex. To compare boys versus girls, t tests were performed for each of the ethnic groups. Two statistically significant (p , .05) results were found: (a) European American girls self-reported higher rates of emotional victimization than their male coun- terparts (p  .0202, R2  .153); and (b) girls of “other” ethnic groups self-reported higher rates of emotional victimization than their male counterparts (p  .0159, R2  .044).

Socioeconomic Status

MANOVAs were first conducted to determine whether each of the two SES measures (i.e., lunch status, main wage earners’ educational achievement), and in combination, were associated overall with the five violence factors. No statistically significant results were found (i.e., free/reduced-cost lunch, F[5, 849]  1.4, p  .2414; main wage earners’

Asian and Pacific Islander Youth Violence 237

T A

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238 Hishinuma et al.

T A

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Asian and Pacific Islander Youth Violence 239

F ac

to r

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m ea

su re

s as

c ov

ar ia

te s.

240 Hishinuma et al.

educational achievement, F[5, 650]  1.4, p  .2352; both SES measures, F[10, 1286]  1.6, p  .1103).

Despite these nonsignificant results, to account for SES, the two SES measures subse- quently were used as covariates and entered first into the models, followed by ethnicity, sex, and ethnicity by sex to determine whether the results would change. In examining the findings for ethnicity, sex, and ethnicity by sex, relatively minor changes were noted (see Table 3, far right column): (a) ethnicity and Factor 1 (social exclusion perpetration), p value no longer statistically significant, but just above .05; (b) ethnicity and Factor 3 (physical perpetration), p value now statistically significant; however, still no statisti- cally significant Newman-Keuls pairwise comparisons; and (c) ethnicity and Factor 5 (physical victimization), only European Americans . Japanese Americans based on the Newman-Keuls pairwise comparisons.

DISCUSSION

This study was the first of its kind to examine ethnic, sex, and ethnicity-by-sex differences for specific, underresearched, Asian American and Pacific Islander, adolescent ethnic groups with youth violence outcomes, including underresearched emotional violence, and SES measures as covariates.

Key Findings and Implications

Ethnicity and Ethnicity by Sex. The primary commonality among the ethnicity and ethnicity-by-sex results was that there was greater victimization risk for ethnic groups that had lower ethnic population figures for the two schools in question. This has been called the “minority effect” (or “group density effect,” Halpern, 1993), whereby the more important factor is whether one is a “minority” within one’s more immediate and smaller community, as opposed to the larger “State of Hawai‘i” community. Halpern (1993) suggested that there were four possible social causes of mental health differences for “ minorities”: (a) experience of prejudice, (b) dislocation and change, (c) cultural isolation and the absence of social support, and/or (d) localization of identity. Previous support for

Ethnic Group

European American

Filipino American

Native Hawaiian

Japanese American

Samoan Other

Males

Females

.80

.70

.60

.50

.40

.30

.20

.10

.00

E m

ot io

n al

V ic

ti m

iz at

io n

M ea

Figure 1. Interaction effect between ethnicity and gender for emotional victimization (Factor 4).

Asian and Pacific Islander Youth Violence 241

this phenomenon was noted by Hishinuma et al. (2005), whereby European American high school students self-reported higher rates of being victimized in five other high schools in Hawai‘i.

In addition, ethnic groups that more recently moved or immigrated to Hawai‘i appeared to have higher victimization rates. This is related to Halpern’s (1993) dislocation and change causation.

In particular, our study found that European American and Samoan youth self-reported higher rates of being physically victimized than Native Hawaiian and Japanese American adolescents. European American students constituted only 4.2% and Samoan students only 8.0% of the two schools’ population, and both ethnic groups may have moved more recently or immigrated to Hawai‘i. In contrast, Native Hawaiians constituted 21.0% of the two schools’ population and are the indigenous people of Hawai‘i. Although the Japanese American youth constituted only 5.3% of the two schools’ population, they were less likely to have been recent immigrants (Matsu, Takeshita, Izutsu, & Hishinuma, 2011). This asser- tion perhaps explains why our hypothesis, that the Pacific Islanders would have the highest youth violence rates, was only partially supported.

In addition, the fact that European Americans self-reported higher rates of being physi- cally victimized than Japanese Americans, even after statistically controlling for the two SES measures, suggested that this difference was not simply because of SES. Other vari- ables should be sought to explain this difference, including greater levels of the causal fac- tors posited by Halpern (1993; e.g., prejudice, dislocation, cultural isolation, and identity localization, which may involve both peers and school staff).

Furthermore, the statistically significant ethnicity-by-sex interaction effect suggested that girls were even more vulnerable to emotional victimization than boys for the European American and “other” ethnicity adolescents. This finding was perhaps because of the combined effects of the greater occurrence of emotional violence for girls as compared to boys and to the additional vulnerabilities of the minority effect and potentially more recent move or immigration to Hawai‘i. Furthermore, girls more than boys tend to inter- nalize problems (e.g., anxiety, depression, suicide attempts; e.g., Rosenfield, 2000), and internalizing, as compared to externalizing, symptoms and disorders are more difficult to identify by teachers and parents (e.g., Pearcy, Clopton, & Pope, 1993). Intervening at this emotional-violence level is important not only to decrease the psychological distress that emotional victimization may cause but also because emotional victimization may escalate into physical violence (Baker & Helm, 2010; Helm, Baker, & Iskandar, 2013).

Sex. Boys self-reported higher rates than girls on two physical forms of violence (i.e., perpetration, victimization), and girls self-reported higher rates than boys on social exclusion perpetration—supportive of the hypothesis that boys will have higher rates than girls for at least the physical violence indicators. Therefore, this robust finding was repli- cated even for AAPI youth.

Prevention and Intervention. In a systematic review, Hahn et al. (2007) found strong evidence that universal, school-based programs from pre-kindergarten through high school are associated with decreases in violence-related outcomes. Despite this finding, they also identified research questions their study could not address, including moderation by eth- nicity, and whether targeting cultural and social differences in these diverse populations would improve effectiveness in decreasing violence (Hahn et al., 2007). These differences are often difficult to assess because of inadequate data (Limbos et al., 2007).

The ethnicity, sex, and ethnicity-by-sex differences, and the corresponding poten- tial reasons for these differences, call for effective interventions for AAPI adolescents

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(K. Williams et al., 2007). However, the models involving ethnicity, sex, ethnicity by sex, and SES accounted for only small percentages of the variances of the dependent mea- sures. This suggested that we must consider multiple social-ecological levels to develop a more comprehensive causal model of AAPI youth violence (e.g., Goebert et al., 2012; Umemoto, Baker, Helm, Miao, Goebert, & Hishinuma, 2009; Umemoto & Hishinuma, 2011): individual (e.g., Meichenbaum, 2003), social (Collaborative for Academic, Social, and Emotional Learning, 2003; Lemerise & Arsenio, 2000), peers (Helm et al., 2013), school (Gottfredson, Gottfredson, Paine, & Gottfredson, 2005; Unnever & Cornell, 2003; Wilson, 2004), community (Cicchetti & Lynch, 1993; Lai, 2009; Miao, Umemoto, Gonda, & Hishinuma, 2011; Scarpa & Ollendick, 2003; Sheidow, Gorman-Smith, Tolman, & Henry, 2001), and societal (Umemoto & Hishinuma, 2011).

Tailoring prevention and intervention programs may be necessary to maximize program effectiveness among both boys and girls as well as across ethnocultural groups. Such tai- loring may occur within a broader context of prevention education and teaching respect for diversity (Helm & Baker, 2011; Marsella, 2009). For example, preliminary findings show promise in addressing youth violence prevention, using a structured curriculum for a high school course in ethnic studies with diverse AAPI high school students (Chung-Do & Goebert, 2009; Glassco & Makaiau, 2009; Makaiau, 2010, 2013; Sugimoto, Hishinuma, & Chang, 2008). The full-semester course rests on an underlying philosophy that behavior, self-concept, and social relationships are interconnected and can be explored in an intellec- tually safe environment. The course was designed to improve understanding of diversity, respect for others, and identity development in a multiethnic school.

As a more ethnic-specific example, Samoan adolescents self-reported higher physical victimization than Native Hawaiian and Japanese American adolescents. This was not unexpected given that other studies have found Samoan youth in Hawai‘i to self-report higher rates of youth violence (e.g., Mayeda et al., 2006) and given the relatively high correlation between violence perpetration and victimization. In addition, youth in Hawai‘i have disclosed ethnocultural stereotyping and discrimination depicting Samoans as violent perpetrators, which Samoan youth also perceived at school and in the community (Helm & Baker, 2011). Therefore, there is a need for effective and culturally relevant prevention and interventions for Samoan youth and their families (e.g., Bond & Soli, 2011; Fiaui & Hishinuma, 2009; Helm & Baker, 2011; Mayeda et al., 2006). The fact that this ethnic difference was no longer significant after controlling for the two SES measures suggested that demographic variables may be at play regarding the original differences, and thus, broader school, community, and societal factors need to be considered in prevention and intervention. For example, promotion of protective factors associated with SES may be necessary in developing and implementing youth violence prevention programs (e.g., safe neighborhoods, community cohesion, opportunities for social mobility, appropriate health and mental health care, less exposure to violence; e.g., Goebert et al., 2012; Singh & Ghandour, 2012; D. Williams, 1999).

Limitations

There were six limitations to this study: (1) The sample was drawn from only two schools on the island of O‘ahu in the State of Hawai‘i. However, these schools were intentionally selected for their higher proportions of the AAPI ethnic groups of interest. (2) Although the ethnic breakdown of the sample approximated the population for both schools com- bined, the participation rate of approximately 33% was relatively low. Given that more

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at-risk students may not have participated (e.g., those who were absent, were suspended, dropped out of high school), the prevalences may be underestimated and the regression results may be less magnified because of restriction of range. (3) The sample size was rela- tively small for three of the ethnic groups (i.e., European American, Japanese American, Samoan), thus decreasing the statistical power associated with these groups. Although this is true, this study involved a relatively large sample of substantially underresearched AAPI adolescents, and the statistically significant findings even when protecting against chance occurrence suggested that there was sufficient power to detect ethnic differences. (4) There were two somewhat heterogeneous ethnic groups (i.e., Native Hawaiians included those of both full and part-Hawaiian ancestry; “other” group included all other full ethnicities and those of mixed non-Hawaiian ancestry). Despite the heterogeneity of the Native Hawaiian group, operationally defining this group in this manner is commonly done in Hawai‘i. (5) All of the data were based on self-reports from high school students. Although corrobo- ration of self-reports with objective data would be ideal, self-reports have the advantage of tapping into attitudes, thoughts, and behaviors that are not captured through more objective means (e.g., arrests from police reports as the only measure of youth violence). (6) The social and contextual nuances associated with violence were not explicated (e.g., who was perpetrating against whom [i.e., same vs. different ethnicity of perpetrator and victim]; why the violence occurred [i.e., retaliatory, self-dense]).

Further Research and Policy Implications

Further research is needed to provide greater explication of the reasons for the minority effect and why there is greater risk for youth who more recently moved or immigrated to Hawai‘i, or to any other location where these risk factors hold true. Larger sample sizes for the ethnic groups within Asian Americans and within Pacific Islanders are needed, including greater exploration into adolescents of mixed ethnicity (i.e., Native Hawaiian or non-Native Hawaiian) and those composing the “other” ethnic group. For example, incon- sistent findings have been noted in the literature regarding mixed ethnicity and identity being associated with risk factors, such as youth violence (e.g., Choi, Harachi, Gillmore, & Catalano, 2006; Hishinuma et al., 2005).

Related to the importance of disaggregating ethnic data is the need to examine the complexity of ethnic identity. For example, an adolescent may have only Asian ancestry, but ethnoculturally may identify with being a Pacific Islander. This incongruence may happen for multiple reasons (e.g., adoption, influence by close friends and/or neighbors). Ethnocultural identity also may prove to be either a protective factor (e.g., French, Kim, & Pillado, 2006) or marker for risk, and this status may also be dependent on environmental conditions (Guerra & Smith, 2006; Mark et al., 2005; Mirabal-Colon & Velez, 2006; Smith & Hasbrouck, 2006; Ting-Tommey et al., 2000). The type of ethnocultural identity may be associated with differences in violence for AAPI adolescents. For example, a greater commitment to one’s own ethnic group generally was found to be protective and associ- ated with lower levels of violence for an AAPI adolescent group, and promoting ethno- cultural pride in combination with multicultural empathy and respect may be promising (Irwin et al., 2013).

Further research also is needed in corroborating self-reported youth violence with differing perspectives from family members, peers, teachers, and community members. There may be not only differing perspectives among these sources but also cultural factors may affect self-reporting (e.g., shame, guilt).

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Of great importance is the need for further development, implementation, and longi- tudinal evaluation of intervention programs (Thornton, Craft, Dahlberg, Lynch, & Baer, 2000) for AAPI adolescents. These efforts should include the examination of the important social and contextual nuances associated with youth violence that will help to develop effective ethnicity- and sex-specific approaches (Helm & Baker, 2011) as well as consider more universal primary prevention programs for AAPIs (e.g., Native Hawaiian children; Hishinuma et al., 2009). In addition, programmatic interventions must be designed and implemented in coordination with supporting policies. These include comprehensiveness with respect to location (i.e., not only on school campuses but also with families and in the community), and also with respect to other related risk behaviors, such as suicidality and substance use.

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Acknowledgments. This manuscript was supported by the Asian/Pacific Islander Youth Violence Prevention Center funded by the Centers for Disease Control and Prevention (CDC; R49/ CCR918619-05; Cooperative Agreement #1 U49/CE000749-01) and from the State of Hawai‘i, Department of Health, Sexual Violence Prevention Program, Maternal and Child Health Branch (ASO Log 06-263, ASO Log 10-098). The authors would also like to express their appreciation to Department of Education, State of Hawai‘i, and to the researchers and administrators of the Asian/ Pacific Islander Youth Violence Prevention Center and Department of Psychiatry, University of Hawai‘i at Mānoa. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the agencies.

Correspondence regarding this article should be directed to Earl S. Hishinuma, PhD, Department of Psychiatry, John A. Burns School of Medicine, University of Hawai‘i at Mānoa, 1356 Lusitana St., 4th Floor, Honolulu, HI 96813. E-mail: [email protected]

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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