Investigating the Potential Causal Relationship BetweenParental Knowledge and Youth Risky Behavior: a PropensityScore Analysis

Melissa A. Lippold & Donna L. Coffman &

Mark T. Greenberg

# Society for Prevention Research 2013

Abstract This longitudinal study aims to explore the poten-tial causal relationship between parental knowledge and youthrisky behavior among a sample of rural, early adolescents(84 % White, 47 % male). Using inverse propensityweighting, the sample was adjusted by controlling for 33potential confounding variables. Confounding variables in-clude other aspects of the parent–child relationship, parentalmonitoring, demographic variables, and earlier levels of prob-lem behavior. The effect of parental knowledge was signifi-cant for youth substance and polysubstance use initiation,alcohol and cigarette use, attitudes towards substance use,and delinquency. Our results suggest that parental knowledgemay be causally related to substance use during middleschool, as the relationship between knowledge and youthoutcomes remained after controlling for 33 different con-founding variables. The discussion focuses on understandingissues of causality in parenting and intervention implications.

Keywords Parental knowledge . Propensity scores .

Substance use . Delinquency

Correlational studies have found that youth whose parentshave high levels of knowledge about youth activities are lesslikely to engage in a host of problem behaviors, such as

substance use and delinquency (for a review see Crouter andHead 2002). Although the literature has confirmed a strongrelationship between parental knowledge and youth out-comes, it is not clear if parental knowledge causes lower levelsof risky behavior or if it is a reflection of other characteristicsof the parent–child relationship. This longitudinal study aimsto explore the potential causal relationship between parentalknowledge and youth risky behavior among early adolescentsusing inverse propensity weighting (IPW; Hirano and Imbens2001), a propensity score technique. Using IPW allows re-searchers to control for a much larger number of potentialconfounders than traditional regression-based methods, there-by strengthening our ability to draw causal inferences.Understanding whether or not parental knowledge may berelated to youth outcomes after controlling for confoundershas important intervention implications, because if an effect isidentified, parental knowledge is likely an effective mediatorto target in interventions.

Correlational Studies on Parental Knowledge

A strong, consistent link has been established between paren-tal knowledge and youth risky behaviors (Crouter and Head2002). Parents who are knowledgeable about youth activitiesmay have the information necessary to provide structure,supervision, and discipline necessary to monitor peer relation-ships and subsequently, to reduce youth deviant behavior(Crouter and Head 2002). Several studies have suggested thatyouth who have parents with high levels of knowledge are lesslikely to engage in delinquency, to select antisocial peers, andbe influenced by antisocial peers (Laird et al. 2008; Veronneauand Dishion 2010). Yet, whether or not the link betweenknowledge and risky behavior holds once researchers accountfor many other aspects of the parent–child relationship has notyet been tested.

M. A. Lippold (*)The School of Social Work, The University of North Carolina atChapel Hill, Chapel Hill, NC, USAe-mail: mlippold@unc.edu

D. L. CoffmanThe Methodology Center, The Pennsylvania State University,State College, PA, USA

M. T. GreenbergThe Department of Human Development and Family Studies andthe Prevention Research Center, The Pennsylvania State University,University Park, PA, USA

Prev SciDOI 10.1007/s11121-013-0443-1

Parental Knowledge vs. Parental Monitoring

Measurement issues in the literature have clouded the distinc-tion between parental knowledge of youth activities and pa-rental attempts to monitor youth.Measures of knowledge haveoften been combined with measures of parent efforts to solicitinformation, the use of behavioral control strategies such assetting rules about behavior, and parental supervision (Crouterand Head 2002). The lack of specificity in these constructs hasmade it difficult to discern the effects of knowledge alone onyouth behaviors apart from parent efforts to monitor and otherbehaviors, such as youth disclosure of information.

This study specifically addresses whether or not parentalknowledge is related to youth outcomes once we control for abroad range of confounding variables. We chose to focus onthe role of parental knowledge rather than parental monitoringbecause knowledge is a central construct that links othermonitoring-related behaviors to youth outcomes. Severalstudies suggest that the effects of parent efforts to monitorand child disclosure on youth outcomes depend on whether ornot they lead to increases in parental knowledge (Fletcheret al. 2004; Lippold et al. 2013b; Vieno et al. 2009).

By using propensity scores, this analysis explores if therelationship between knowledge and youth outcomes holdsregardless of how parents obtain that information and regard-less of other aspects of the parent–child relationship. In thisframework, we view other behaviors that may lead to knowl-edge, such as parental monitoring, child disclosure, and su-pervision as potential confounders. Using propensity scoremethods increases our confidence that parental knowledgemay be causally related to youth outcomes and that increasesin knowledge will likely lead to changes in child behavioracross different family contexts. Research questions that ex-plore relationships between knowledge and other behaviors,such as parental monitoring, are fruitful areas for future re-search but are beyond the scope of the current paper.

Confounder Variables

Despite the strong correlational evidence that parental knowl-edge is linked to youth outcomes, researchers have yet todetermine if having greater parental knowledge is linked torisky behavior once a broad range of potential confounders areaccounted for. There may be other systematic differencesbetween families with different levels of parental knowledgethat are also related to youth outcomes. These confoundervariables make it difficult to discern if it is high levels ofparental knowledge that are protective against problem behav-ior, or if the association is driven by other aspects of theparent–child relationship, other aspects of the monitoringprocess, or pre-existing youth behavior.

Issues of statistical power make it difficult to control for abroad array of confounders using traditional regression or

structural equation methods. Although recent correlationalstudies have included measures of various behaviors relatedto monitoring in their models (e.g., parental solicitation, dis-closure), they typically have not had the power to include awide array of other parenting and parent–child relationshipvariables that may relate to knowledge. Further, even thoughmany studies include earlier measures of a particular youthproblem behavior as a control variable, few studies haveincluded measures of multiple problem behaviors. Becausebehaviors tend to cluster (Jessor 1993), it may be important toaccount for a wide range of youth problem behaviors, ratherthan one specific behavior. In this study, we control for 33confounder variables including aspects of the monitoringprocess, other aspects of the parent–child relationship, andpre-existing problem behaviors.

Research suggests there may be several confounding var-iables that are related to both parental knowledge and youthoutcomes. Some studies suggest that parent attempts to mon-itor youth and set rules about youth behavior may lead toparental knowledge and subsequently, youth outcomes(Soenens et al. 2006; Fletcher et al. 2004). Recent studiessuggest that parental knowledge may be gained through youthdisclosure and youth decisions to share information with theirparents (Stattin and Kerr 2000). In fact, some researchersargue that the protective effects of parental knowledge maysolely be due to youth disclosure (Kerr et al. 2010). It isdifficult to discern from these studies if knowledge itself hasa causal effect on youth outcomes, or if it reflects parentattempts to solicit information or high levels of parent–childcommunication and youth disclosure, as these studies onlycontrol for a very limited number of potential confounders.

Other studies have found links between parental knowl-edge and other parenting characteristics. Parents who aresupportive and have warm parent–child relationships are morelikely to have high levels of knowledge and lower levels ofyouth problem behavior (Soenens et al. 2006; Fletcher et al.2004). A warm and positive parent–child relationship, char-acterized by mutual trust has also been associated with in-creased adolescent disclosure (Kerr et al. 1999; Smetana et al.2006; Soenens et al. 2006). Thus, it is possible that warm,trusting relationships between parents and youth may explainthe association between parental knowledge and youthoutcomes.

Lastly, there are several studies that suggest that earlierlevels of youth problem behavior can influence both laterlevels of problem behavior and parental knowledge (Jangand Smith 1997; Laird et al. 2003; Kerr et al. 2010).Reciprocal relationships have been found between knowledgeand delinquency, suggesting that parental knowledge influ-ences and is influenced by youth behaviors (Laird et al. 2003;Keijsers et al. 2010). Thus, another confounder may be earlierengagement in risky behavior. Youth with problem behaviormay also have low levels of knowledge, making it difficult to

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discern whether knowledge is a causal mechanism that pro-tects youth from later antisocial behavior.

The Benefits and Limits of a Propensity Score Approach

To understand causality it is necessary to account for potentialconfounders by considering the counterfactual—that is, tounderstand how youth would fare if their parents had a differ-ent level of knowledge given their score on a broad range ofpotential confounder variables (Guo and Fraser 2010; Rubin2005). Random assignment controls for confounders in ex-perimental studies by evenly distributing them on averagebetween treatment groups. However, in observational studiesit is not possible to randomize parents into different levels ofknowledge. Propensity score techniques, such as IPW, allowresearchers to adjust the data for confounding variables in theabsence of randomization assuming that all confounders aremeasured (Rosenbaum and Rubin 1983). Conceptually, pro-pensity score techniques allow researchers to estimate thecausal effect of knowledge on youth risky behavior as iffamilies were randomly assigned to different levels of knowl-edge. These models allow researchers to control for a larger,more diverse array of potential confounders than traditionalregression methods and the ability to control for these con-founders increases our confidence in drawing causal infer-ences. In addition, propensity score techniques, unlike tradi-tional regression methods, do not assume that the relationshipbetween the confounders and youth risky behavior is linearand/or that there are no interactions between the confoundersand parental knowledge.

However, propensity score techniques have some limita-tions. Like traditional regression methods, these methods as-sume that there are no unmeasured confounders. This is astrong assumption that cannot be tested in practice. However,the more potential confounders that are included in the pro-pensity model, the more plausible the assumption becomes.Thus, it is imperative that researchers measure as many poten-tial confounders as possible. In addition, the impact of anunmeasured confounder is mitigated if a measured potentialconfounder is highly correlated with the unmeasured con-founder. In addition, a sensitivity analysis (see, e.g.,Rosenbaum 2002) can be conducted, which attempts to deter-mine how influential an unmeasured confounder would needto be in order to change the estimate in a meaningful way (e.g.,change the significance or sign of the estimate). Sensitivityanalysis is still being developed for continuous exposures, thuswe are unable to do one. In summary, propensity scores maybe particularly useful in situations where one cannot use ran-domization, such as this study, and may strengthen our abilityto infer a causal relationship, particularly when they include alarge number of confounding variables. However, propensityscores cannot replace randomization and randomized trials areconsidered the gold standard for drawing causal inferences.

Intervention Implications

Understanding the potential role of parental knowledge onyouth risky behavior has important intervention implications(Dishion and Patterson 1999). Preventive interventions arelikely to be most effective at reducing youth problem behaviorif they target causal mechanisms as mediators. Thus, identi-fying if parental knowledge is a causal mechanism may shedlight on the extent to which intervention models should spe-cifically aim to improve parental knowledge.

Two intervention studies have explored the mediating ef-fect of parental monitoring on youth outcomes (Dishionet al.2003; Spoth et al. 1998). These studies suggest thatchanges in parent efforts to monitor youth may mediate theeffects of family based interventions on youth outcomes.However, it should be noted that neither of these studiesspecifically measured parental knowledge as a mediator ofintervention effects. Dishion et al. (2003) used observationalmeasures of parental monitoring, while Spoth et al. (1998)combined items tapping into parental monitoring with otherconstructs such as discipline to form an overall latent variablefor general childmanagement. Although, these studies certain-ly suggest that there may be causal processes at work, whetheror not changes in parental knowledge are causally related toyouth outcomes has not been addressed in prior literature.

This Study

Here, we investigate the relationship between parental knowl-edge of youth activities and youth delinquency, substance use,attitudes towards substance use, and antisocial peer relation-ships once we account for a broad range of confounders,thereby strengthening our ability to draw causal inferences.We use IPW, a propensity score technique. Our propensitymodels include 33 confounder variables measured whenyouth are in the fall grade 6. These confounders include otheraspects of parental monitoring, parent–child communication,other aspects of the parent–child relationship, demographicvariables, and initial levels of youth problem behaviors.

Method

Study Participants

Participants were a randomly-selected subset of sixth gradersparticipating in the PROSPER project (Promoting School-Community-University Partnerships to Enhance Resilience),a large-scale effectiveness trial of preventive interventionsaimed at reducing substance use initiation among rural ado-lescents (Spoth et al. 2004). Participants resided in 28 ruralcommunities and small towns in Iowa and Pennsylvania.Initial eligibility requirements for communities considered

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for the studies were (a) school district enrollment from 1,300to 5,200, and (b) at least 15 % of the student populationeligible for free or reduced-cost lunches (for more informationsee Spoth et al. 2004).

The PROSPER project involved youth from two succes-sive cohorts of sixth graders. Students in each of these cohortscompleted in-school questionnaires. Data were collected inthe fall and spring of sixth grade, and annually thereafter. Onaverage, 88 % of all eligible students completed in-schoolassessments at each data collection point. In addition, familiesof students in the second cohort were randomly selected forparticipation in in-home assessments with their sixth gradechild. A total of 2,267 families from the in-school assessmentsample were recruited for in-home family assessments; ofthose recruited for the in-home sample 979 (43 %) completedthe in-home assessments. The in-home assessments included afamily composition interview, written questionnaires complet-ed independently by the youth, mother, and if present, father.

The current study includes three waves of data from youthand their mothers; wave 1 (the intervention pre-test) when theyouth were in the fall of sixth grade, wave 2, when youth werein the spring of sixth grade, and wave 3, when youth were inthe spring of seventh grade. At wave 1, 977 families complet-ed the in-home questionnaire. By wave 3, the sample haddecreased to 801 cases (83 % of those at wave 1). The meansample age at wave 1: youth (M =11.3 years, SD=0.49);mothers (M =38.7, SD=6.05); and fathers (M =41.2, SD=7.14). Sixty-one percent of youth resided in Iowa and 39 %lived in Pennsylvania and 47 % were male. The averagehousehold income was $51,000 (in 2003) and 62 % of youthhad parents with some post-secondary education. Most of theyouth in our sample were living in two-parent homes; 80 %were living with a parent who was married and 54 % wereliving with both biological parents. The vast majority of youthwere White (84 %); 6 % were Hispanic, 3 % AfricanAmerican, 2 % were Native American/American Indian,1 % Asian and 4 % identified as Other.

To test for selection bias in the in-home sample, youth inthe in-home sample were compared to youth in the totalsample assessed at school (e.g., youth in the in-school samplethat did and did not participate in the in-home assessments;N =4,400) on a series of demographic and behavioral out-comes. Youth in the in-home sample were not different fromthe total sample at wave 1 on receipt of free or reduced lunch(33.6 versus 33.0 % respectively), living with two biologicalparents (59.3 versus 62.5 %), race (88.6 % versus 86.5 %White), or gender (49.5 vs. 46.8 % male). In addition, nodifferences were found between the samples in substanceuse initiation. However, youth that received in-home assess-ments were less likely to engage in delinquent behavior thanyouth in the total sample (M =0.58, SE=0.06 versusM =0.82,SE=0.04): F(1, 27)=18.32, p <0.01. Youth in the in-homesample also perceived fewer benefits from using substances

(M =4.77, SE=0.01 versus 4.71, SE=0.02): F(1, 27)=12.36,p <0.01). There were no differences between samples ondemographic variables.

Measures

All measures of parenting are youth report and were gatheredfrom the PROSPER in-home data. We use PROSPER in-school data for our measures of youth delinquency and sub-stance use (Redmond et al. 2007). Items were adapted fromthe Iowa Youth and Families Project (Conger 1989;McMahon and Metzler 1998; Spoth et al. 1998). Measuresof our confounding variables were collected when youth werein the fall of sixth grade, our predictor variable parentalknowledge was measured at wave 2, when youth were in thespring of sixth grade, and our youth outcome variables weremeasured at wave 3, when youth were in the spring of seventhgrade. To capture both mother and youth perspectives, con-founding variables include youth and mother reports of allconstructs (Lippold et al. 2013a). The predictor variable,parental knowledge at wave 2, is a composite measure thataverages youth and mother reports. Youth outcome variablesare based on youth reports, as mother reports were not avail-able for these items.

Predictor Variable

Maternal Knowledge of Youth Activity Youth and motherperceptions of maternal knowledge were measured using fiveLikert-type items (1=always to 5=never). Items ask youthhow often their mother knows where they are and who theyare with, when they do something really well at school orsomeplace else away from home, and how often their motherknows when they do not do things they have asked him/ her todo. Similar items were also asked to mothers about theirperceptions of their knowledge of youth activities (α =0.83youth, α =0.70 mother). Mother and youth reports were aver-aged to create a composite score for our analysis.

Outcome Variables

Delinquency Twelve items assessed participants’ involve-ment in four deviant behaviors in the past 12 months (α =0.88), including: (a) taking something worth less than $25, (b)beating up someone or physically fighting with someone outof anger, (c) purposely damaging or destroying someoneelse’s property, (d) throwing objects such as rocks or bottlesat people to hurt or scare them. Responses were coded: Never(0) or Once or more (1) and summed. At wave 3, 29.83 % ofyouth had engaged in at least one delinquent act.

Antisocial Peers Three items measured whether participants’closest friends engaged in antisocial behaviors (α =0.81). One

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item, for example, reads: “These friends sometimes get intotrouble with the police.” Responses were scored on a five-point Likert scale ranging from Strongly Disagree (1) toStrongly Agree (5).

Substance Use Initiation A four-item index was used to mea-sure substance use initiation The items asked youth if theyhave ever had a drink of alcohol, ever drunk more than a fewsips of alcohol, ever smoked a cigarette, or ever smokedmarijuana or hashish (0=no ; 1=yes). At wave 3, 49.87 %had initiated at least one substance.

Polysubstance Initiation Initiation of alcohol, tobacco, anddrug use was assessed by asking participants to indicatewhether they had ever used seven different substances(e.g., cigarettes, ecstasy, glue, Vicodin). Responses werecoded No (0) or Yes (1) with the index ranging from 0 to7 with higher scores indicating greater amounts ofpolysubstance use (α =0.66). At wave 3, 33.35 % had engagedin polysubstance use.

Cigarette Use This scale summed two dichotomous items thatasked youth if they have ever smoked cigarettes and if theyhave smoked cigarettes in the past month No (0) or Yes (1)(α =0.70). At wave 3, 14.02 % had used cigarettes.

Alcohol Use A cumulative index of participants’ alcohol usewas created using six items about various forms of beer, wine,and liquor consumption (e.g., more than just a few sips, everhad a drink, drunkenness). Items were coded to create anindex ranging from 0 to 6 with higher scores indicating greateramounts of alcohol use (α =0.78). At wave 3, 49.18 % hadused some form of alcohol.

Positive Substance Use Expectancies Beliefs about the use ofalcohol, cigarettes, and marijuana were assessed using 11items (α =0.95). Example items include: “Smoking cigarettesmakes you look cool” and “Kids who use marijuana (pot)have more friends.” Agreement was recorded on a five-pointLikert scale: Strongly Disagree (1) to Strongly Agree (5),coded such that higher scores indicate youth perceive fewersocial benefits from substance use.

Attitudes Towards Substances Youth views on whether or notit is wrong to use substances was measured with three items(α =0.83). An example item is “How wrong do you think it isfor someone your age to smoke cigarettes?” Each item wascoded on a four-point scale: Not at all wrong (1) to Verywrong (4).

Substance Use Norms Youth were asked three items to assesstheir perceptions of peer substance use (α =0.85). Youth wereasked how many people their age smoke cigarettes, use

alcohol, and smoke marijuana. Each item was coded on afive-point scale: None or almost none (1) to All or almostall (5).

Confounder Variables The propensity models included 33confounder variables: youth and mother reports of othermonitoring-related behaviors, other aspects of parenting andthe parent–child relationship, and demographic variables. Wealso included time 1 measures of our predictor variable (pa-rental knowledge as reported bymothers and youth) and youthoutcome variables. A summary of confounder variables canbe found in Table 1.

Results

Analysis progressed through a series of four steps. First, weimputed missing data using Proc MI in SAS (Schafer 1997;Schafer and Graham 2002). This allowed us to retain all caseswith missing data on the confounders in our analysis.1

Second, we estimated a propensity score for each case andcomputed the weights. Applying weights to the observedsample allowed us to mimic a randomized sample by evenlydistributing confounders across levels of knowledge. Third,we checked the balance of our sample to ensure that theconfounding had been properly accounted for. Lastly, wecalculated the average causal effect (ACE) of parental knowl-edge on each youth outcome using our weighted sample.

Estimating Propensity Scores and Applying Weights

Conceptually, IPW is similar to using survey weights in ananalysis. In IPW, individuals with a low probability of havingtheir reported level of knowledge given their levels of con-founders are up-weighted, and those with a high probability ofhaving their reported level of knowledge given their levels ofconfounders are down-weighted. Thus, assuming no unmea-sured confounders, the weighted sample mimics a randomizedsample where individuals are randomly assigned to levels ofparental knowledge and confounders are evenly distributedbetween families with different levels of knowledge.

Each individual case was assigned a propensity score.Estimating propensity scores and creating weights for contin-uous variables, such as parental knowledge, is only slightlymore difficult than it is for a binary variable (for an example ofestimating propensity weights for continuous variables, seeCoffman et al. 2002). The propensity score may be obtainedfrom the distribution of the standardized residuals from a

1 Model results presented are from one imputed dataset. Models were runon five different imputed datasets and the same general pattern was foundacross datasets. Model estimates were not averaged across imputeddatasets because studies suggest that Rubin’s rules do not apply topropensity score models (Qu and Lipkovich 2009).

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linear regression of parental knowledge on the measuredconfounders (Imai and Van Dyk 2004; Robins et al. 2000). 2

Standardized residuals are calculated by subtracting each in-dividual’s fitted value of parental knowledge from their ob-served value and dividing by the square root of the estimatederror variance. Given the assumptions of the linear regressionmodel, these residuals should be normally distributed with amean of 0 and a standard deviation of 1.0. Propensity scoresare then estimated as the probability of an individual’s stan-dardized residual under this standard normal probability dis-tribution function. Each case is assigned a weight of meanparental knowledge/propensity score, also called the InversePropensity Weight. Because large weights can lead to estima-tion problems, stabilized weights were computed by dividingthe sample mean of parental knowledge by an individual’spropensity score. In addition, 13 cases had extreme weightsthat were greater than 10 or smaller than 0.10. To minimizeestimation problems, weights over 10 were set to 10 and thoseless than 0.10 were set to 0.10. Thus, after IPW, individualswith a high propensity for their level of knowledge are givensmaller weights than those with a low propensity for theirlevel of knowledge.

Checking Balance

After weights are applied, the balance is checked. Weightingthe sample based on IPWs should reduce the relationshipbetween the confounder variables and parental knowledge.Therefore, correlations between study confounders and paren-tal knowledge are expected to be smaller in the weightedsample than the unweighted sample. As seen in Table 2, allof the correlations between our confounders and knowledgedecreased after sample weights were applied. All of the cor-relations in the weighted sample had an absolute value of 0.12or less, which meets Cohen’s definition of a small effect(Cohen 1988, 1992). This indicates that our confounders wereeffectively balanced across levels of knowledge in our weight-ed sample.

Calculating the ACE

Once the sample was balanced, weighted regression wasused to estimate the effect of parental knowledge on youthoutcomes. The beta coefficient from this regression can beinterpreted as the effect of a one unit increase in knowledgeon youth outcomes. Table 3 displays the beta weights andsignificance for each outcome. The effect of parentalknowledge was significant for substance and polysubstanceuse initiation (p <0.05), cigarette use (p <0.05), alcohol use

2 Results of the regression analysis used to obtain propensity scores areavailable from the first author upon request. The squared multiple corre-lation for the regression model is 0.37.

Table 1 Confounder variables

Construct Item description Alpha (youth/mother)

Parent efforts to monitor 5 items [1=almost always true to 5=almost always false]. Example items include “Mostafternoons or evenings my parents ask me if I have homework to do for the next day”and “I’m not allowed to leave home after dinner without my parent’s permission”.

0.69/0.66

Child disclosure Youth were asked how strongly they agree with the statement "I share my thoughts and feelingswith my mother" [1=strongly agree to 5=strongly disagree]

–

Quality of communication 6 items on a 1–5 Likert-type scale. The youth-report scale has 5 items on a 1–7 scale. Exampleitems include how often the mother listens to the child’s point of view, criticizes the child’sideas, and appreciates the child’s ideas.

0.84/0.74

Amount ofcommunication

6-item scale. Items include how often parents and child talk about plans for the day, his or herschool work, and what’s going on in her life [1=every day to 6=never].

0.71/0.65

Affective quality 3 items. During the past month, how often did your mother act loving and affectionate towards you?[1=always to 7= never]

0.75/0.82

Consistent discipline 3-item scale [1=always, 5=never]. Examples of items include “Once a discipline has beendecided, how often can he or she get out of it?”

0.56/0.83

Supervision Youth were asked to rate how often (1) Is an adult home when you come home from school;(2) Do you get home from school before your parents are home (1=Always and 5=Never).

0.73/0.71

Standard setting 4 items on a 1–5 scale. When you don’t understand why your mom makes a rule for you tofollow, how often does she explain the reason? [almost always to almost never]

0.70/0.70

Parent–youth conflict 3 items [1=always to 5=never]. During an average week, how often do you and this childhave serious arguments

0.66/0.70

Parent education 0=high school education or less; 1=some college –

Dual bio parent status 0=not living with biological parents; 1=living with both biological parents –

Gender 0=female; 1=male –

condition 0=control group; 1=intervention condition –

Confounder variables were measured at wave 1 and include T1 levels of parental knowledge and youth outcome variables (not shown). All variableswere recoded such that higher scores indicate higher levels of each construct

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(p <0.05), attitudes towards substances (p <0.001), and de-linquency (p <0.05). The effect of parental knowledge didnot reach statistical significance for substance use norms,substance use expectancies, or antisocial peer relationships.

Discussion

Our results suggest that parental knowledge may be causallyrelated to substance use during middle school, as the relation-ship between knowledge and youth outcomes remained aftercontrolling for 33 different confounding variables. Obtaininghigh levels of knowledge about youth activities may leadyouth to engage in less alcohol, cigarettes, and other drugs.This work supports some prior correlational analysis thatfound that parental knowledge was related to youth substanceuse (Barnes et al. 2006). This study extends this work bysuggesting that when accounting for a broad array of possibleconfounding variables, parental knowledge remains an impor-tant protective factor for youth development.

Parental knowledge was also related to individual attitudestowards whether or not it is wrong to use substances.However, parental knowledge was not associated with youthperceptions of substance use norms among peers or youthexpectancies (perceptions of the potential social benefits ofsubstance use). All three of these mechanisms have beenidentified as important intermediary steps in youth decisionsto use alcohol and other substances (Patel and Fromme 2009).These findings suggest that parents may use knowledge toinfluence their child’s individual perceptions of whether or notit is wrong to use substances. For example, parents who areaware of upcoming youth activities may be more likely todiscuss the possibility of alcohol being present at an event andtheir views on whether or not youth substance use is a morallyacceptable behavior for their child. However, parents may beless likely to use knowledge to discuss or correct perceptionsof peer attitudes that may influence use, such as youth per-ceptions of the overall prevalence of substance use amongpeers or how substance use may influence a child’s socialstatus. Or alternately, it may be that it is effective for parents touse knowledge to change youth individual perceptions ofwhether or not it is wrong for them to use substances, but thatparent attempts to use knowledge to alter youth perceptions ofpeer norms and potential peer related social benefits may beless effective as these factors may be more affected by otherpeer group and community-level factors.

Parental knowledge was also related to delinquency but notantisocial peer associations. This finding partially supportsprior studies that have found that youth who have parents withhigh levels of knowledge are less likely to engage in delin-quency (Laird et al. 2008; Veronneau and Dishion 2010).However, our findings differ from others that have foundknowledge to be related to the selection and influence of

Table 2 Correlations of confounders at wave 1 with parental knowledgeat wave 2 in the unweighted and weighted sample

Unweightedcorrelation

Weightedcorrelation

(Youth/mother) (Youth/mother)

Monitoring-related behaviors

Knowledge 0.37/0.39 −0.04/0.02Parental solicitation 0.11/0.25 0.02/-0.04

Child disclosure 0.25/N.A. 0.02/N.A.

Quality of communication 0.28/.032 −0.06/0.07Amount of communication 0.31/0.25 0.0/0.02

Supervision 0.12/0.12 0.0/-0.07

Other aspects of parenting

Affective quality 0.28/0.28 0.07/0.06

Consistent discipline 0.19/0.13 0.0/0.12

Standard setting 0.18/0.21 0.10/-0.04

Parent–youth conflict 0.21/0.26 0.02/0.03

Demographics

Parent education 0.09 0.02

Dual bio parent status 0.10 −0.01Gender −0.21 −0.04Condition 0.01 −0.01

Problem behavior at wave 1

Delinquency −0.22 −0.03Substance use initiation −0.11 0.01

Polysubstance use initiation −0.11 0.05

Alcohol use −0.16 0.04

Substance use expectancies 0.20 0

Attitudes towards substances 0.11 −0.07Substance use norms −0.1 −0.02Antisocial peer associations −0.24 −0.06Cigarette use −0.03 −0.06

Youth report/mother report: all confounders were measured when youthwere in the fall of grade 6. Child disclosure and youth outcomes wereonly available as reported by youth

Table 3 Average causal effect (ACE) of parental knowledge on youthrisky behavior

B SE t p value

Delinquency −0.81 0.37 −2.18 <0.05

Substance use initiation −0.51 0.22 −2.36 <0.05

Polysubstance use initiation −0.40 0.17 −2.37 <0.05

Alcohol use −0.69 0.31 −2.23 <0.05

Substance use expectancies 0.09 0.1 1.36 0.17 ns

Attitudes towards substances 0.31 0.11 2.78 <0.001

Substance use norms −0.16 0.13 −1.19 0.23 ns

Antisocial peer associations −0.15 0.17 −0.93 0.35 ns

Cigarette use −0.25 0.12 −2.18 <0.05

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antisocial peers (Laird et al. 2008; Veronneau and Dishion2010), which is one of themainmechanisms bywhich parentalknowledge is theorized to exert its influence on delinquency.Therefore although our findings suggests that knowledge maybe an important causal mechanism for preventing delinquency;it does not suggest that knowledge allows parents to moreclosely monitor or intervene in deviant peer relationships.

There are several possible explanations for this finding.Perhaps parents who know about youth activities are morelikely to discuss their own child’s behaviors and less likely todiscuss the behavior of their peers. Alternately, parents withknowledge may be more likely to discuss peer pressure withtheir children, thus equipping youth with the skills needed toresist engaging in deviant behavior even in the presence ofdeviant peers. It is also possible that parents with knowledgemay attempt to intervene in antisocial peer friendships orprovide guidance on the types of friends to choose, but theseattempts may be unsuccessful. In fact, a recent study suggeststhat parent attempts to prohibit friendships with deviant peersmay increase the likelihood that youth have deviant peerfriendships (Keijsers et al. 2012), raising questions as towhether or not parent attempts to intervene in deviant peerrelationships have unintended consequences. More specificmeasures of parent behavior related to their children’s peersare needed to further our understanding of these processes.

This work has potential intervention implications. Earlyuse of substances can have long-term negative consequencesincluding a higher risk of adult alcohol disorders (Grant andDawson 1997; Dewit et al. 2000). The present findings sug-gest that targeting parental knowledge in family-based inter-ventions is likely to reduce the risk of substance use during themiddle school years, a critical time for prevention efforts. Italso suggests that increasing knowledge is likely to be linkedto reductions in risky behavior regardless of other character-istics of the parent–child relationship. Knowledge may be asalient intervention target.

More work is needed to understand how knowledge isgained in families and the specific mechanisms that linkknowledge to youth substance use. Our work suggests thatparental knowledge may lead to parent efforts to influenceyouth attitudes towards substances. This is important as youthwho perceive drinking to be acceptable may be more likely touse substances (Callas et al. 2004; Patel and Fromme 2009).More work is needed to understand other possible mecha-nisms that link knowledge to substance use and delinquency.For example, theory suggests that parents use knowledge toprovide structure, supervision, and discipline but these medi-ational links have not been adequately tested (Stattin et al.2010). Propensity score techniques that investigate causalmediation processes may help fill this gap. Lastly, additionalresearch is needed that explores the causal relationship be-tween other monitoring related behaviors and parental knowl-edge. Several studies have explored the roles of disclosure and

parental monitoring in predicting parental knowledge butthese studies have controlled for a very limited number ofpotential confounders. Propensity score techniques wouldallow researchers to draw stronger causal inferences aboutpotential predictors of knowledge, which would have impor-tant intervention implications.

There are several limitations to this study. First, propensityscore methods assume that all of the confounders are includedin our models. It is possible that there are unmeasured con-founders that could influence both knowledge and youthoutcomes that are not included in our model. Note, however,that traditional regression methods also rely on this assump-tion and that propensity score methods have an advantageover traditional regression in that they reduce a large numberof confounders into a single-number summary. Second, par-enting behaviors occur in patterns. Isolating the specific ef-fects of knowledge removes it from the broader context of theparent–child relationship. This strategy is helpful, as it ad-dresses if knowledge affects youth outcomes regardless ofhow parents obtain information. Yet, this study does notreflect how knowledge occurs in conjunction with other par-enting behaviors. Third, our measures of some of the con-founders are limited. Specifically our measure of youth dis-closure taps into youth disclosure of thoughts and feelings notyouth disclosure of information. Although analysis in a highlycited dataset (Stattin and Kerr 2000) suggests these two con-structs are highly related (r =0.67), limitations of this andother measures could have influenced our propensity scoreestimates. Lastly, this study was conducted on a sample ofearly adolescents residing in rural communities and smalltowns. These study findings may not be generalizable to otherstudy populations, such as youth in urban settings, and maynot apply to older or younger youth.

Despite these limitations, this study takes a unique ap-proach to understanding the role that parental knowledgeplays in the development of problem behavior in early ado-lescence. By accounting for 33 different confounder variables,this study suggests that parental knowledge may be causallyrelated to youth decisions to use substances and may be animportant element to target in our preventive interventions.

Acknowledgments Work on this paper was supported by researchgrants P50 DA10075-16, R01 DA013709, and F31-DA028047 fromthe National Institute on Drug Abuse. The content is solely the responsi-bility of the authors and does necessarily represent the official views ofthe National Institute on Drug Abuse or the National Institutes of Health.The authors would like to thank the anonymous reviewers for theirhelpful feedback on this article.

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