Introduction

When confronted with negative events, people have an array of responses to choose from—both legal and illegal. However, it is puzzling why some people who experience negative events resort to crime and why some who experience similar events do not. One of the leading theories in criminology that could answer these questions is the general strain theory (Agnew 1992, 2006, 2013, 2015a). This theory posits that negative life events or strains trigger people to employ coping strategies that they intend to alleviate the effects of such strains. Unfortunately, one of these coping strategies is crime. The theory adds that the effects of strain on criminal coping are mediated by negative emotions—anger, depression, or frustration. That is, people who experience strains are angered, depressed, or frustrated, and these emotions encourage them to commit crime. However, the effects of strains and negative emotions on crime depend on the individual’s characteristics like low self-control, delinquent peers, and parenting and circumstances most conducive to crime. Thus, the theory could explain why people commit crime and why some people presented with strains do not resort to criminal coping.

The general strain theory has received considerable attention from researchers and has received empirical support from most studies (Agnew 2006, 2015a; Froggio 2007). Most of these studies were conducted in the USA; however, some studies have begun using the theory in other countries such as in South Korea (Yun and Lee 2015; Jun and Choi 2015; Jang et al. 2014), China (Gao et al. 2014; Zhang et al. 2011), Russia (Botchkovar and Broidy 2013), England (Baron and Tan 2012), Iceland (Sigfusdottir et al. 2010), Canada (Baron 2008, 2009), and Italy (Froggio and Agnew 2007). In addition, there have been cross national tests of the theory across some countries in Europe (Sigfusdottir et al. 2012; Botchkovar et al. 2009) and between the USA and Taiwan (Lin et al. 2013).

To date, however, only one study has tested the theory in the Philippine setting (Maxwell 2001). This study used familial strains to explain delinquency among grade school students in Cagayan de Oro City, Mindanao, and found tentative evidence on the effects of Filipino culture on the strain-delinquency relationship. In particular, parent-to-child violence did not affect Filipino adolescent delinquency as spanking and other forms of familial discipline are culturally accepted in the country (Maxwell 2001). Another reason for this null finding might be the Filipino trait of resiliency (katatagang-loob): when faced with negative life events, Filipinos tend not to express anger and violence but instead survive in silence and search for external support and engage in creative activities (Tiangco 2005). As such, testing the theory in the Philippine setting provides a challenge for the general strain theory (Sigfusdottir et al. 2012; Agnew 2015b). There are, however, questions as to the generalizability of Maxwell’s (2001) findings to the whole Philippines, and the mediating role of negative emotion on the strain-delinquency relationship and the moderating role of some variables on this relationship and that of negative emotion-delinquency link were not tested in her study. Further, it is still unknown how other forms of interpersonal violence, say, peer violence, and social variables work in the general strain theory framework in the Philippine setting. It can be expected that peer violence will affect Filipino delinquency because such form of violence is not culturally accepted in the country. In addition, it can also be expected that social variables like parental supervision, peer support, and engagement in physical activities play a role in the general strain theory framework, because Filipinos, like most East Asian countries, have collectivistic culture.

Thus, the purpose of this study is to add to the literature by testing the general strain theory that relates negative life events or strains to negative emotions which in turn leads to criminal coping among Filipino adolescents. The study also tests the moderating role of parental care and supervision, social support, and engagement in physical activities on the strain-delinquency and depression-delinquency relationships. The findings of the study contribute to the generalizability of the theory in non-western settings and support the arguments of Agnew (2015b) that the theory can explain crime and delinquency in Asian countries. Further, the study advances GST research on less explored areas of non-criminal deviance such as suicide and truancy and the effects of engagement in physical activities that include exercise (see Agnew 2013 for a discussion of these gaps in GST research). The article proceeds by discussing the general strain theory and its core theoretical propositions, the methodology, results of the analysis, and the discussion of these results within the general strain theory approach.

Overview of the General Strain Theory

The general strain theory benefits from the confluences of social and psychological variables in explaining crime and delinquency (Agnew 1992). At its core is the notion that strains or negative life experiences drive one to resort to illicit coping strategies (one of which is crime) through negative emotions. It defines strain as “relationships in which others are not treating the individual as he or she would like to be treated” (Agnew 1992:50). Unlike classic strain theories that focused only on one type of strain—failure to achieve positively valued goals—this theory adds two major types of strains: removal of positively valued stimuli and the presentation of negative stimuli. Strains as failure to achieve positively valued goals include injustice and failure to achieve monetary goals. Strains as removal of positively valued stimuli include loss of a partner, death of a family member, and separation of parents. Finally, strains as presentation of negative or noxious stimuli include child abuse, peer abuse, criminal victimization, and physical punishment.

Earlier tests of the theory found that strains indeed induce criminal coping (Agnew and White 1992; Paternoster and Mazerolle 1994). However, some subsequent studies found varied results—some measures of strains are not related to some crimes (see, e.g., Mazerolle and Piquero 1998; Mazerolle et al. 2000; Botchkovar et al. 2009). In response to these contradictory findings, Agnew (2001, 2006, 2013) argues that researchers must use specific measures of strains instead of aggregated ones and that not all strains are conducive to crime. In particular, he proposes strains that are more likely to illicit crime and delinquency. These strains are seen as unjust, seen as high in magnitude, associated with low social control, and pressure the individual to resort to maladaptive behaviors. Such strains include, but not limited to the following: failure to achieve monetary goals, parental rejection, erratic and harsh discipline, child neglect and abuse, negative secondary school experiences, unpleasant working conditions, homelessness, peer abuse, criminal victimization, and discrimination. Subsequent research found support for the significant effects of these specified strains on crime and delinquency (see, e.g., Agnew 2002; Eitle 2002; Baron 2006; Hay and Evans 2006; Manasse and Ganem 2009; Rebellon et al. 2012; Moon and Jang 2014).

Aside from its direct effect, strains also indirectly lead to crime through negative emotions. The theory argues that individuals resort to maladaptive behaviors partially because they experience negative emotions resulting from negative events (Agnew 1992, 2006, 2015a, b). These emotions include anger, depression, and frustration. To alleviate or escape from these negative emotions, individuals may resort to maladaptive behaviors. For example, victimization may lead to retaliation, negative school experiences may lead to substance use or truancy, or the worst, other negative life events may lead to self-harm as a means of escape. Previous research provides mixed support for these predictions. Some research found that some negative emotions mediate the strain-delinquency relationship while others did not (see, e.g., Mazerolle et al. 2000; Capowich et al. 2001; Jang and Johnson 2003; Hay and Evans 2006; Botchkovar et al. 2009; Moon and Jang 2014).

To explain these contradicting findings, Agnew (2013) suggests that models predicting delinquency through negative emotions must use strains that are logically consistent with negative emotions of which they are expected to predict. For instance, “strains evaluated as unjust may be more strongly linked to anger, those involving the inability to achieve desired goals to frustration, those seen as uncontrollable to depression, and those involving impending threats seen as uncontrollable to fear” (Agnew 2013:656). In addition, these strains and the production of negative emotions must occur in circumstances that are most conducive to crime—circumstances where criminal coping may elicit lower risks and more benefits for the offender.

It is logical, however, that not all strained individuals resort to maladaptive behaviors. Thus, it is important to investigate what factors condition the effects of strains and negative emotions on crime. These factors lessen (or aggravate) the effects of strain and negative emotions on the individual. Among these factors are individual characteristics. Some individuals may be overwhelmed by the effects of strains because of their poor coping abilities brought by their individual characteristics such as low self-control, low social control, and low social support (Agnew 1992; Agnew et al. 2002). However, there has been mixed support for the strain-individual characteristic interaction in the literature (see Agnew 2006 for a review), thereby suggesting that individual characteristics are incomplete conditioning variables.

Thus, Agnew (2013) offers a more comprehensive set of factors that moderate the effects of strains and negative emotions on crime. He argues that criminal coping is likely chosen by “certain individuals experiencing certain types of strain in certain circumstances” (Agnew 2013:661). In other words, crime is highly likely when certain strains conducive to crime and negative emotions interact with certain individual characteristics (e.g., low self-control, delinquent peers, low social support, and low social control) and certain circumstances (e.g., strains occur in unsupervised settings, the potential victim is smaller than the motivated offender, or other settings that present tremendous opportunities for offending).

Current Study

In this study, it is argued that the framework of the general strain theory can model some maladaptive behaviors among Filipino adolescents. As such, the study fits the framework on the nationally representative data of Filipino secondary students collected by the Global School-based Student Health Survey. However, the data limit the current analysis to three adolescent maladaptive behaviors—suicidality, substance use, and truancy. Such behaviors, nevertheless, are considered to be among the leading health problems associated with adolescents. Self-harm and suicide rates are high during adolescence and considered as one of the leading causes of death in this age group (Hawton et al. 2012; Wasserman et al. 2005). Likewise, substance use such as smoking and drinking alcohol poses serious health problems for young people (Gold et al. 1995; Gore et al. 2011; Warren et al. 2006). Finally, truancy has also been considered a major health problem among adolescents, because of its role in increasing the risk of substance use and other risky behaviors among adolescents (Garry 1996; Heilbrunn 2007; Yiede and Kobrin 2009).

A common theme in the literature is the effect of negative life events on suicide (Liu and Miller 2014; Wasserman 2016; Hawton et al. 2012; Zhang et al. 2011; Hay et al. 2010; Garisch and Wilson 2010), substance use (Booker et al. 2004, 2008; Unger et al. 2001), and truancy (Reid 2005; Yiede and Kobrin 2009). Such events include loss of significant others or possessions, familial conflicts, school problems, and violent victimization such as bullying and harassment. And, these events are viewed to block the satisfaction of basic needs of being loved, belongingness, being respected, and awareness of one’s worth (Wasserman 2016), thereby putting an individual at a pressure of coping through conformity or deviation. Thus, the study aims to test this hypothesis:

  • Hypothesis 1: negative life events (i.e., violent experiences, sexual harassment, discrimination, social exclusion, and food deprivation) are positively related to maladaptive behaviors (i.e., suicidality, substance use, and truancy).

Although the general strain theory proposes several negative emotions (e.g., anger, frustration, and depression) in its framework, this study uses only depression as a mediator of the strain-delinquency link for a few reasons. The current data provide measures of depression only and thus do not afford the study to measure anger and other negative emotions. Nevertheless, research in eastern Asian countries reveals that Asians express more depression than anger, probably because of these countries’ collectivistic cultures in which relationships are highly valued (Agnew 2015b; Horton et al. 2012). Such collectivistic culture is also existent in the Philippines as its philosophy, as with most of Asian cultures, has emanated from Chinese philosophy of man (Tiangco 2005). Further, the Filipino philosophy of survival is not of Darwin’s survival through aggression but through katatagang-loob encompassing kawalang-karahasan (non-violence), kahinahunan (prudence), kakalmahan (calmness), determinasyon (determination), bahala-na (fatalism) and pagsusumikap (hardwork; Tiangco 2005). However, as Estanislao (2001: 104 quoted in Tiangco 2005) puts it, “…there is a cultural tendency in the Philippines to deny the presence of depression and to endure and to suffer in silence”—the Filipino trait of resiliency (katatagang-loob). With this, an empirical test of the mediational effect of depression in the strain-delinquency link in the Philippines might be a challenge to the theory’s generalizability. Nevertheless, the general literature on suicide, substance use, and truancy shows that depression plays a role in affecting maladaptive behaviors or mediating the relationship between negative life events and maladaptive behaviors (Chaiton et al. 2009; Thapar et al. 2012). The second hypothesis of this study is as follows:

  • Hypothesis 2: depression mediates the effects of negative life events on maladaptive behaviors.

One of the major Filipino cultural traits is pakikisama or sharing/merging oneself with others (Guevara 2005). This trait contributes to Filipino resiliency (katatagang-loob) and is often manifested in seeking support from family and friends and through creative activities such as arts and sports (Tiangco 2005). Thus, we argue that parental supervision, peer support, and engagement in physical activities moderate (as promotive factors) the strain-delinquency and depression-delinquency relationships. Such factors have been known to contribute to adolescent resiliency in the midst of negative life experiences (Biddle and Asare 2011; Eime et al. 2013; Fergus and Zimmerman 2005).

  • Hypothesis 3: parental care and supervision, social support, and engagement in physical activities moderate and buffer the effects of strains and depression on maladaptive behaviors.

Methodology

Data and Respondents

Data used in this study come from the Global School-based Student Health Survey (GSHS) administered in the Philippines (2011) by the World Health Organization (WHO) and the Center for Disease Control and Prevention (CDC) in collaboration with the United Nations Children’s Fund (UNICEF), United Nations Educational, Scientific, and Cultural Organization (UNESCO), and the Joint United Nations Programme on HIV/AIDS (UNAIDS; CDC 2013). The GSHS aims to collect data that could guide national and international health policies for the youth. Student information collected comprises alcohol use, dietary behaviors, drug use, hygiene, mental health, physical activity, protective factors, sexual behaviors, tobacco use, and violent experiences.

The 2011 Philippines GSHS used a two-stage sample design (CDC 2011). At the first stage, secondary schools were chosen based on their probability proportional to enrolment size with 97 % response rate. At the second stage, classes within the chosen schools were randomly selected and all students in chosen classes were requested to respond to the GSHS questionnaire with 84 % response rate. A total of 5920 students answered the GSHS questionnaire. Of these students, 56.7 % were female, while 43.3 % were male students (Gender Parity Index = 1.31). This ratio is similar to the national female to male secondary enrolment which is 1.17 (Philippine Commission on Women 2014). Most of the students aged 13 to 16 or older who were distributed from first- to fourth-year level (Table 1).

Table 1 Descriptive statistics of demographic variables

Measures

Dependent Variables

Suicidality

An index was created to measure suicidality (suicide ideation, suicide plan, and suicide attempts). Three GSHS items were used to create the index. These items comprise the following: (1) “During the past 12 months, did you ever seriously consider attempting suicide?,” (2) “During the past 12 months, did you make a plan about how you would attempt suicide?,” and (3) “During the past 12 months, how many time did you actually attempt suicide?” The first two items were answerable with either “yes = 1” or “no = 2,” and the third had these responses: “zero time = 1,” “one time = 1,” two or three times = 3,” “four or five times = 4,” and “six or more times = 5.” The items were recoded such that those respondents who answered “yes” to the first two items or at least once to the third one received a score of “1” per item. However, the index was highly skewed. Thus, following the suggestions of Farrington and Loeber (2000), the index was transformed into a binary variable: those who scored from 1 through 3 in the index were given a score of 1.

Substance Use

Substance use was initially measured by an index of items on using tobacco products and drinking alcoholic beverages. Items on smoking include the following: (1) “During the past 30 days, on how many days did you smoke cigarettes?” and (2) “During the past 30 days, on how many days did you use any tobacco products other than cigarette, such as chewing tobacco leaves?” Items on drinking alcohol include the following: (1) “During the past 30 days, on how many days did you have at least one drink containing alcohol?” and (2) “During the past 30 days, on the days you drank alcohol, how many drinks did you usually drink per day?” The first three items had these responses: “0 days = 1,” “1 or 2 days = 2,” “3 to 5 days = 3,” “6 to 9 days = 4,” “10 to 19 days = 5,” “20 to 29 days = 6,” and “all 30 days = 7,” while the fourth item was answerable with these responses: “I did not drink alcohol during the past 30 days = 1,” “less than one drink = 2,” “one drink = 3,” “two drinks = 4,” “three drinks = 5,” “four drinks = 6,” and “five or more drinks = 7). The items were recoded to create an index such that those who scored at least two received a score of 1 per item, while those who scored 1 received 0; then, the scores were added. Again, a binary variable was created for substance because of being extremely skewed. Those who scored 1 through 4 received 1, while those who scored 0 retained their scores.

Truancy

Only one item was used to measure truancy: “During the past 30 days, on how many days did you miss classes or school without permission?” Similar to the distribution of the other dependent variables, truancy was highly skewed (69 % answered 0 days); thus, it was also recoded into a binary variable (0 days = 0; at least one absence = 1).

Independent Variables

Strain Variables

Violent Experiences

Four items were tapped to create an index of violent experiences. The first three items comprise the following: (1) “During the past 12 months, how many times were you physically attacked?,” (2) During the past 12 months, how many times were you in a physical fight?,” and (3) “During the past 12 months, how many times were you seriously injured?” Responses for these items are as follows: “0 times = 1,” “1 time = 2,” “2 or 3 times = 3,” “4 or 5 times = 4,” 6 or 7 times = 5,” “8 or 9 times = 6,” “10 or 11 times = 7,” and “12 or more times = 8.” The fourth item of the index is a response to the follow-up question of bullying victimization. Bullying victimization questions use this introductory statement: “The next two questions ask about bullying. Bullying occurs when a student or group of students say or do bad and unpleasant things to another student. It is also bullying when a student is teased a lot in an unpleasant way or when a student is left out of things on purpose. It is not bullying when two students of about the same strength or power argue or fight or when teasing is done in a friendly and fun way.” After the respondent was asked how many days in the past 30 days he was bullied, this follow-up question was asked: “During the past 30 days, how were you bullied most often?” One of the responses to choose from is “I was hit, kicked, pushed, shoved around, or locked indoors.” Those who answered this response was scored 1; other answers were coded as “0.” To create the index, the other three items were also recoded into binary variables in which those who experienced at least once were scored 1 per item; then, the scores for the four items were added.

Discrimination

Discrimination was measured using three available responses to the question on how the respondents were bullied most often: (1) “I was made fun of because of my race, nationality, or color,” (2) “I was made fun of because of my religion,” and (3) I was made fun of because of how my body or face looks.” Those who answered either of these items were given a score of 1; other answers were coded as “0.”

Sexual Harassment

To measure sexual harassment, a response to the question on how the respondent was bullied most often was used. Those who responded “I was made fun of with sexual jokes, comments, or gestures” were scored 1; those who did not were scored 0.

Social Exclusion

Another response to the question on how the respondent was bullied most often was used to measure social exclusion. Respondents who answered “I was left out of activities on purpose or completely ignored” were scored 1; those who did not were scored 0.

Food Deprivation

One question was used to measure food deprivation. Respondents were asked: “During the past 30 days, how often did you go hungry because there was not enough food in your home?” Responses comprise the following: (1) “never = 1,” “rarely = 2,” “sometimes = 3,” “most of the time = 4,” and “always = 5.”

Strain (Aggregated Index)

An aggregated strain index composed of the five strain variables was created to ease the complexity of building models with interaction terms to test the third hypothesis. Violent experiences and food deprivation were recoded as binary variables. Those who scored at least 1 in violent experiences were scored 1. And, those who answered never, “rarely,” and “sometimes” to the food deprivation item were scored 0, while those who answered “most of the time” and “always” were scored 1.

Negative Emotion

Depression

Two items were tapped to measure depression (Cronbach alpha = 0.474). These items comprise the following: (1) “During the past 12 months, how often have you felt lonely?” and (2) “During the past 12 months, how often have you been so worried about something that you could not sleep at night?” Responses for these two items were never = 1, rarely = 2, sometimes = 3, most of the time = 4, and always = 5.

Conditioning Variables

Parental Care and Supervision

A scale of four items was used to measure parental care and supervision (Cronbach alpha = 0.717). These items comprise the following: (1) “During the past 30 days, how often did your parent or guardians check to see if your homework was done?,” (2) “During the past 30 days, how often did your parent or guardians understand your problems and worries?,” (3) “During the past 30 days, how often did your parent or guardians really know what you were doing with your free time?,” and (4) “During the past 30 days, how often did your parent or guardians go through your things without your approval?” Responses to these items were never = 1, rarely = 2, sometimes = 3, most of the time = 4, and always = 5.

Social Support

A scale of social support was created using two questions. The first question was “During the past 30 days, how often were most of the students in your school kind and helpful?” with responses of never = 1, rarely = 2, sometimes = 3, most of the time = 4, and always = 5. The second question was “how many close friends do you have?” with responses of “zero friend = 1,” “one friend = 2,” “two friends = 3,” and “three or more friends = 4.” The scale was created by calculating first the items’ factor weights using factor analysis. Then, the weights were multiplied to the original scores of the corresponding variables and the results were added.

Physical Activities

Engagement in physical activities was measured using three questions. Before the items were asked, this introductory statement was given: “The next three questions ask about physical activity. Physical activity is any activity that increases your heart rate and makes you get out of breath some of the time. Physical activity can be done in sports, playing with friends, or walking to school. Some examples of physical activity are running, fast walking, biking, dancing, football, and other activities.” These items comprised the following: (1) “During the past 7 days, on how many days were you physically active for a total of at least 60 min per day?,” (2) “During the past 7 days, on how many days did you walk or ride a bicycle to or from school?,” and (3) During the school year, on how many days did you go to physical education (PE) class each week?” Responses to the first two items ranged from 0 to “7 days” and coded from 1 through “8,” while responses to the third item ranged from “0 days” to “5 or more days” coded from 1 through “6.” To create the scale, the same procedure as that of creating the social support scale was used.

Control Variables

Three control variables were used in this study—age, gender, and body mass index. Of these variables, age and gender have been the more commonly known correlates of delinquency. Nevertheless, research reveals that obesity increases the risk of depression (Luppino et al. 2010), suicide (Heneghan et al. 2009; Steele and Doey 2007), substance use (Lanza et al. 2014, 2015), and truancy (Conroe 2007; Lanza and Huang 2015); thus, BMI must also be controlled in the current study. Those who aged 11 years or younger were scored 1, 12 years old = 2, 13 years old = 3, 14 years old = 4, 15 years old = 4, 15 years old = 5, and 16 years old or older = 6. Female was coded 1; male, 0. Body mass index was created by dividing the weight in kilograms by the square of height in meters.

Results

This section presents the results of the analysis. It comprises (a) the descriptive statistics of the key variables and the bivariate correlations and (b) binary logistic regression models of suicidality, substance use, and truancy.

Descriptive Results

Table 2 shows the descriptive statistics of the key variables. As seen, strains (violent experiences, discrimination, sexual harassment, and social exclusion) and maladaptive behaviors (suicidality, substance use, and truancy) are highly skewed—only very few respondents experienced strains and resorted to maladaptive behaviors. Only 23 % of the respondents resorted to any suicidal behavior, 27 % used any substances, and 31 % missed classes without permission.

Table 2 Descriptive statistics of the key variables

Bivariate Correlations

Maladaptive behaviors correlate with strains, depression, conditioning variables, and control variables as shown in Table 3. Suicidal behavior is positively related to violent experiences, discrimination, sexual harassment, food deprivation, and the aggregated strain index. This relationship implies that those adolescents who experienced these strains were more likely to resort to suicidal behaviors. Also, those who were depressed were more prone to suicidal behaviors as shown by the two variables’ significant positive correlation coefficient. However, suicidal behavior is negatively related to two conditioning variables—parental care and supervision and social support—which implies that these conditioning variables tend to decrease the adolescents’ tendency to resort to suicidal behaviors. Finally, suicidal behavior is positively related to all control variables—age, sex, and body mass index (BMI). Those who were older, were female, and had higher BMI had higher risk of resorting to suicidal behaviors.

Table 3 Bivariate correlations of all variables

Substance use is positively related to violent experiences, sexual harassment, food deprivation, and the aggregated strain index. As these strains increased, tendency to use substances such as tobacco products and alcoholic beverages also increased. Substance use is also positively related to depression—depressed adolescents tend to use substances than those who were not. The use of substances is negatively related to parental care and supervision and social support. This means that those adolescents who received adequate parental care and supervision and had more friends and helpful peers were less likely to use substances. Finally, older male adolescents and those who had higher BMI were more likely to use substances.

Truancy is also related to some independent variables. It is positively related to some strains—sexual harassment, food deprivation, and the aggregated strain index. Adolescents who experienced these strains were more likely to miss classes without permission. Truancy is also positively related to depression: those who were depressed tend to miss classes. However, the relationship of truancy to each conditioning variable differs. While parental care and supervision encourages school attendance, engagement in physical activities encourages truancy. Finally, truancy is related to only one demographic variable —sex. Males were prone to miss classes without permission.

Although Table 3 shows that maladaptive behaviors correlate with the independent variables, the nature of this relationships is still unknown. Some relationships may be mediated or moderated by other variables. Thus, binary logistic models were built to test the hypotheses of this study on the direct, mediating, and moderating roles of the key variables, controlling for sociodemographic factors. These models are discussed in the next subsection.

Logistic Regression Models of Maladaptive Behaviors

This subsection presents the binary logistic regression models of the three maladaptive behaviors—suicidal behavior, substance use, and truancy. Five models were built for each maladaptive behavior. The first model contained the five strain variables and the three control variables. The second model added the negative emotion of depression. The third one entered the three conditioning variables into the equation. Finally, the fourth and the fifth models added the interaction terms between the aggregated strain index and the conditioning variables (e.g., strain × parenting) and the interaction terms of depression and the conditioning variables (e.g., depression × parenting). By building these models, we were able to test the three hypotheses of the study.

Suicidal Behavior

Table 4 presents the binary logistic regression models of suicidal behaviors. In support to first hypothesis, three strain variables—violent experiences, discrimination, and sexual harassment victimization—significantly predicted suicidal behaviors net of the effects of the demographic variables in model. However, as expected in the second hypothesis, these effects are mediated or attenuated by depression as shown in model 2. Specifically, the effect of sexual harassment victimization on suicidal behaviors became nonsignificant, and the coefficients of the effects of violent experiences and discrimination had 16 and 29 % reduction, respectively. These findings suggest that those adolescents who experienced physical violence, discrimination, and sexual harassment resorted to suicidal behaviors, partly because they experienced depression.

Table 4 Binary logistic regression models of suicidal behaviors

In model 3, two conditioning variables (parental care and supervision and social support) help predict suicidal behavior. These variables reduce the likelihood of suicidal behaviors. To test the third hypothesis, interaction terms of strain and the three conditioning variables and depression and these variables were created. Following Moon and Jang (2014), the strains were aggregated to create an aggregated strain index. This index was used to create interaction terms to lessen the complexity of the analysis. All continuous variables were mean centered before creating the interaction terms to reduce multicollinearity. The interaction terms were then entered in models 4 and 5. Contrary to the third hypothesis, none of the interaction terms significantly predicted suicidal behaviors, except that of depression × social support. But, the positive coefficient of this interaction term seems counterintuitive and suggests that those depressed adolescents with adequate social support tend to resort to suicidal behaviors, thereby contradicting the protective effect of social support expected in hypothesis 3. This anomaly was investigated by building another model which excluded the original measure of depression and the other interaction terms to reduce possible multicollinearity. As a result, depression × social support failed to significantly predict suicidal behaviors (b = 0.041, p = 0.083). Thus, its significant effect in model 5 is just a product of multicollinearity even though the variables were mean centered before building the models.

Substance Use

In Table 5, the results of the logistic regression models of substance use are shown. Model 1 partially supports the first hypothesis—violent experience and food deprivation significantly affect substance use in the expected direction, controlling for gender, age, and BMI. Those adolescents who experienced violence and experienced food deprivation tend to use substances. When depression was entered in model 2, the effect of food deprivation on substance use was diminished and the effect of violent experiences was reduced by 7 %, thereby providing support to hypothesis 2. Depression tends to mediate the effects of food deprivation and violent experiences on substance use.

Table 5 Binary logistic regression models of substance use

Surprisingly, the original nonsignificant coefficient of social exclusion increased and became significant in model 2. Possibly, depression is a suppressor variable of social exclusion. A suppressor variable accounts for a large error in the model, and thus, the previously entered nonsignificant variable is left with a lesser error for it to account, thereby becoming significant (Conger 1974; Thompson and Levine 1997). Criminologist recognizes that some variables have suppressor effects on other variables that are uncorrelated with delinquency (see Lipton and Smith 1983 for an example). To clarify the effects of social exclusion on substance use, model 2 was run for those adolescents who scored 1 through 5 in the depression scale and for those adolescents who scored from 6 through 10. For those who scored in the lower half of the depression scale, social support did not significantly predict substance use (b = −0.334, p = 0.386). Although the significance level is still a little higher than 0.05, the coefficient of social exclusion predicting substance use of adolescents who scored at the upper half of the depression scale increased dramatically by 20 % (b = −0.532, p = 0.075). Social exclusion, therefore, is a poor predictor of substance use. This is supported by its marginal significance level in predicting substance use in model 2 (b = −0.464, p = 0.049). Depression acts as a suppressor variable of the errors unaccounted by social exclusion. Moreover, it became nonsignificant in model 3.

In model 3, the conditioning variables were entered into the equation in predicting substance use. As seen from Table 5, only parental care and supervision significantly predicted substance use: likelihood of using substances was reduced in adolescents who received adequate parental care and supervision. To test hypothesis 3, interaction terms were entered in models 4 and 5. Only two interaction terms significantly predicted substance use. In line with the expectation of the third hypothesis, engagement in physical activities buffers the effects of depression. However, a counterintuitive finding was found for interaction term of strain and parental care and supervision. Its positive significant coefficient implies that adolescents who experienced strains and have adequate parental care and supervision were more likely to use substances. To conduct a deeper analysis, a model that excluded original strain and parental care and supervision measures and the other interaction terms was run of which the results showed that the effect of strain × parenting became nonsignificant, thereby suggesting problems with multicollinearity.

Truancy

Five binary logistic regression models of truancy are presented in Table 6. Model 1 was used to test hypothesis 1 of which the results show partial support. Three strains—violent experiences, sexual harassment victimization, and food deprivation—are positively related to truancy after controlling for the demographic factors. Adolescents who experienced physical violence, sexual harassment, and food deprivation were more likely to miss classes without permission. However, the effects of these strains are attenuated by depression as shown in model 2. Specifically, depression reduces the coefficient of violent experiences by 7.4 %, sexual harassment by 15.3 %, and food deprivation by 23.4 %. Thus, hypothesis receives partial support —depression attenuates the effects of some strains on truancy. Violent strains, sexual harassment, and food deprivation elicit depression which in turn encourages truancy among adolescents.

Table 6 Binary logistic regression models of truancy

Conditioning variables were entered in the model 3 to have a feel on the direct effects of these variables on truancy. As seen, social support tends to decrease the likelihood of truancy, while physical activities counterintuitively increase the likelihood of truancy. These effects remain significant in the two remaining models which include the interaction terms in the equation in predicting truancy. As shown in models 4 and 5, three interaction terms significantly predicted truancy—strain × parenting, strain × social support, and depression × physical activities. Physical activities seem to buffer the effects of depression on truancy. This finding is puzzling, because the direct effect of physical activities on truancy is positive, as noted above. Thus, a model that excluded potential sources of multicollinearity was run of which the results showed that depression × physical activities ceased to significantly predict truancy (b = −0.012, p = 0.139).

Two other counterintuitive findings are that of the effects of strain × parenting and strain × social support. Instead of reducing the effects of strain, these conditioning variables aggravate the said effects on truancy. These findings contradict the third hypothesis. So, models were run so that the original values of the variables in the interaction terms and the other interaction terms were excluded to reduce possible multicollinearity. The results of these models support the earlier findings—strain × parenting (b = 0.045, p = 0.001) and strain × social support (b = 0.152, p = 0.001) continued to positively predict truancy. Thus, adolescents who had adequate parental care and supervision and social support tended to miss classes without permission when they experienced strains.

Discussion

The general strain theory has received much attention from researchers and general support from previous studies. To our knowledge, however, there has been limited test of the theory in the Philippine setting. Previous test of theory in the country used familial strain to explain grade school students’ delinquency in a city in Mindanao, Philippines (Maxwell 2001). Although this study provides initial evidence that the theory can be applied in the country, it ignores the role of negative emotions and some conditioning variables on the effect of negative life events on delinquency. To further investigate the applicability of the theory in the country, the current study aimed to test the core theoretical propositions of the theory that predict positive effects of strains (i.e., violent experiences, discrimination, sexual harassment victimization, social exclusion, and food deprivation) on delinquency (i.e., suicidal behaviors, substance use, and truancy) mediating effects of negative emotions (depression) on the strain-delinquency relationship and the moderating effects of individual characteristics (i.e., parental care and supervision, social support, and engagement in physical activities) in the strain-delinquency and depression-delinquency relationship. The current study is important primarily because it fills some gaps in the general strain theory research, especially on the areas of suicide and the use of engagement in physical activities as a moderating variable in the theory (see Agnew 2013 for a discussion of these gaps). Further, the study offers contribution to the generalizability of the general strain theory which might be limited in the Philippine context as some negative life events (e.g., parent-to-child violence) are culturally accepted and Filipinos generally are resilient to most negative life events.

Results show general support for these predictions with some important exceptions, thereby calling for further research and model building. Of special note is the consistent effect of violent experiences on the three maladaptive behaviors. Such finding suggests that, although parent-to-child violence tends not to affect Filipino delinquency (Maxwell 2001), peer violence increases maladaptive behaviors among Filipino adolescents. It could that, unlike parent-to-child violence, peer violence is not culturally accepted in the Philippines and thus exerts positive effect on maladaptive behaviors, an effect that could offset the protective effect of the Filipino trait of resiliency (katatagang-loob). Further, depression consistently mediated some strain-delinquency relationships in the study, thereby lending support to previous research stating that depression plays a key role in the general strain theory framework as applied in Asian countries, because Asians are more likely to respond to negative events with depression than anger (Agnew 2015b), and this is also true among Filipinos who value non-violence (kawalang-karahasan), prudence (kahinahunan), and calmness (kakalmahan) over anger and violence (Tiangco 2005).

In predicting suicidal behaviors, the findings show partial support to the first and second hypotheses but not to the third one. In particular, three strains—violent experiences, discrimination, and sexual harassment victimization—have direct positive effects on suicidal behavior. That is, adolescents have higher likelihood to resort to suicidal behaviors when they experience violence (e.g., physical bullying, physical attack, physical injury, and physical fight); discrimination stemming from their looks, religion, and nationality; and sexual harassment in the form of sexual jokes, comments, and gestures. These effects confirm Agnew’s (2001; 2006, 2013) arguments that these strains are seen as unjust and high in magnitude and therefore should encourage an individual to engage in delinquency, in this case, suicidality comprising suicide ideation, suicide plan, and suicide attempts. The results are in line with prior research finding positive effects of violent experiences, discrimination, and sexual harassment and other negative life events on suicide (Hay et al. 2010; Klomek et al. 2008; Chiodo et al. 2009; Bakken and Gunter 2012; Garisch and Wilson 2010; Wasserman 2016; Liu and Miller 2014).

Moreover, the findings also support the second hypothesis. Depression mediates the effects of sexual harassment and attenuates the effects of violent experiences and discrimination on suicidal behaviors. In other words, strains elicit depression which in turn pushes someone to escape from strains through suicidality—a finding supporting Agnew’s (1992) predictions and previous research (e.g., Garisch and Wilson 2010). Of further note, however, is the null finding on the moderating effects of social variables (i.e., parental care and supervision, social support, and engagement in physical activities) on suicidal behavior. These findings contradict Agnew’s (1992) moderation hypothesis but consistent with some previous studies (Agnew 2006). As the results suggest, parental care and supervision and social support have direct negative effects on suicidality but no moderating effects.

Another set of models provide partial support for the theory in predicting substance use. Substance use is positively related to violent experiences and food deprivation, and depression mediates food deprivation-substance use relationship and decreases the effects of violent experiences on substances use, thereby providing partial support for the first and second hypotheses. Adolescents who experience violence and food deprivation are more likely to use tobacco products and drink alcoholic beverages, perhaps, to alleviate these negative life events. These results support predictions from the theory (Agnew 1992; 2001) and findings of prior research (e.g., Yun and Lee 2015; Sharp et al. 2012). Moreover, only one interaction effect—depression × physical activities—is positively related to substance use. Engagement in physical activities has conditioning effects on depression-substance use relationship, but it has no direct effect on the said maladaptive behavior. Adolescents who engage in physical activities do not resort to substance use even when presented with strains. This effect is opposite that of parental care and supervision. Parental care and supervision has direct negative effect on substance use but no moderating effects.

The models used to predict truancy partially support the first and second hypotheses but contradict the third hypothesis. Three strains have positive direct effects on truancy, depression attenuates these effects, but parental care and supervision and social support aggravate the effects of strains on truancy. In particular, violent experiences, sexual harassment victimization, and food deprivation encourage adolescents to miss classes without permission, thereby providing support for the general strain theory (Agnew 1992; 2001). The effect of these strains is so strong that adolescents can only alleviate through truancy. This is reinforced by the effect of depression on strain-truancy relationship: none of the effects of these strains was not diminished (but only attenuated) even when depression was entered into the second model. Surprisingly, however, the moderating effects of parental care and supervision and social support on strain contradict the expected buffering effects. Specifically, these social variables aggravate the effects of strains on truancy. Adolescents miss classes without permission even when they have adequate parental care and supervision and social support when they experience violence, sexual harassment, and food deprivations miss. These findings run contrary to the predictions of the theory (Agnew 1992). One possible reason for this anomaly is that strained adolescents run to their parents for support and thus need to miss classes for the simple reason that they cannot locate their parents at school but at their homes. Strained adolescents may also collude with their close friends to alleviate the effects of negative life events by truancy when their peers are themselves also truant. Such behavior is further aggravated by the Filipino cultural trait of pakikisama. Filipinos love to share/merge oneself with others (Guevara 2005) and tend to seek external support and engage in recreational activities when confronted with negative life events (Tiangco 2005).

The findings in this study provide more support to the first and second hypotheses than to the third. These mixed findings might stem from the incomplete models used to explain suicidal behaviors, substance use, and truancy. The study fails to include other conditioning variables such as self-control and delinquent peers and the nature of circumstances in which those strains occurred because the GSHS does not include these factors. This limitation, however, is also common in most previous studies (Agnew 2006, 2013). Thus, in order to fully test the theory, researchers must attempt first to devise nationally representative surveys intended to test the general strain theory. These surveys must include comprehensive list of strains most conducive to crime, individual characteristics expected to condition the effects of these strains, negative emotions, and the circumstances in which those strains occur. Given the mixed findings found in the current study and some previous studies, researchers may attempt to build comprehensive crime-specific models for each maladaptive behavior. It is likely that deviant acts are diverse behaviors that require crime-specific models based on the general strain theory. One excellent example is the work of Agnew (2010) on terrorism. Nevertheless, the current findings add to the literature of general strain theory.

Conclusion

The findings of this study partially support the arguments by Agnew (2015b) that the general strain theory is applicable in Asian countries including the Philippines. In general, some strains are positively related with maladaptive behaviors—suicidal behaviors, substance use, and truancy—among Filipino adolescents. Of these strains, violent experiences have the most consistent effect on maladaptive behaviors. Moreover, the effects of these strains are partially mediated or attenuated by depression. That is, negative life events lead to maladaptive behaviors partially through depression. However, models built to examine the moderation hypothesis revealed mixed results—engagement in physical activities buffers the effects of depression on substance use, but parental care and supervision and social support aggravate the effects of strain on truancy. Overall, the current findings support the theory but call for further research and theory building—delinquent acts are diverse behaviors, and thus, each may require a crime-specific model of the general strain theory.