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. Author manuscript; available in PMC: 2017 Nov 15.
Published in final edited form as: J Community Psychol. 2017 Feb 8;45(2):230–249. doi: 10.1002/jcop.21844

ASSESSING PARENTING AND FAMILY FUNCTIONING MEASURES FOR URBAN AMERICAN INDIANS

Stephanie L Ayers 1, Stephen Kulis 2, Monica Tsethlikai 3
PMCID: PMC5687281  NIHMSID: NIHMS887009  PMID: 29151655

Abstract

Urban American Indian (AI) families often “live in two worlds,” and widely used parenting measures may not adequately capture their parenting styles. Drawing from baseline surveys of AI parents living in 3 urban communities in Arizona (n = 606), this study examines the applicability of using 6 previously validated measures with urban AI parents: parent self-agency, parental supervision, positive parenting practices, discipline, family cohesion, and parent–adolescent conflict. A 4-step factor analytic sequential procedure was employed, and results indicate the only measure remaining as a single factor is discipline. The χ2 difference tests of the remaining 5 measures indicate multiple factors fit the data significantly better than the previously validated single factor. These findings indicate previously validated measures are not adequate holistic descriptions of the parenting and familial experiences of urban AIs. Understanding how urban AIs conceptualize parenting provides a foundation for strengthening urban AI families.

Although the urban American Indian (AI) population has increased steadily for several decades—both in numbers and as a proportion of all AIs—little is known about how AI families function in the urban environment and how family dynamics operate outside tribal Indian communities (Machamer & Gruber, 1998). The descriptions of traditional parenting styles among reservation-based families are primarily qualitative, and although there is some consensus that traditional AI parenting styles include active support and guidance from the extended family (e.g., Cross, 1998; Glover, 2001; Limb, Hodge, & Panos, 2008; Stauss, 1995), the extent to which these practices are maintained in an urban environment is unknown.

In addition, parenting and family functioning measures that have been validated with non-Native populations may not adequately capture parenting styles and family relationships for urban AI parents who are “living in two worlds”– having to operate in both the AI world and the mainstream world (Garrett, 1995; LaFromboise, Albright, & Harris, 2010). It is therefor important to examine AI parenting practices, family functioning, and parent–child relationships among urban AI families today to understand specific facets that may be targeted and strengthened in urban AI families.

The Role of Culture in Parenting

For AIs, the family environment plays an important role in the socialization of children, relying heavily on the extended family to include relations by blood, clan, tribe, and formal and informal adoption (Swaim, Oetting, Jumper Thurman, Beauvais, & Edwards, 1993; Seideman, Jacobson, Primeaux, Burns, & Weatherby, 1996). In this extended family environment, the role of “parent” can include multiple people—grandparents, aunts, and uncles—each taking on a specific role in the socialization of the child (Machamer & Gruber, 1998; Swaim et al., 1993; Waller & Yellow Bird, 2002). The extended family takes on the role of instructing the child in beliefs, values, traditions, and morals, as well as protecting the child from risky situations (Machamer & Gruber, 1998). The parents’ role is primarily to give encouragement, affection, and economic support, while the aunts and uncles provide the discipline, supervision, and monitoring (Garrett & Garrett, 1994; Machamer & Gruber, 1998). Elders are responsible for the passing down of values, beliefs, and traditions through storytelling (Davis, Dionne, & Fortin, 2014; Swaim et al., 1993).

As a result, the extended communal family serves as a strong and pervasive influence on family functioning (Seideman et al., 1996), holding the family accountable to cultural beliefs and values, creating group solidarity (Red Horse, Lewis, Feit, & Decker, 1978), and ensuring adherence to traditions based on history and ancestry (Machamer & Gruber, 1998). This network of family members gives AI children more association, contact, and exposure to influences from adults, resulting in more sensitivity to the values and beliefs of the family and tribe (Swaim et al., 1993). AI youth are able to turn to adults other than parents for advice and support and have a large support system of people willing to assume the role of caretaker (LaFromboise & Dixon, 2003).

Traditional parenting styles also differ from those of mainstream society. Although mainstream parenting styles typically include teaching using overt and clear directives and lectures, AI parenting styles are based on observation, nonverbal communication, patience, role playing, modeling, and storytelling (Garrett, 1996; Garrett & Garrett, 1994; Guilmet & Whited, 1989). Direct confrontation, intrusive questioning, lecturing, and overt suggestions may be taken as intrusive, regardless of the age of the person (Everett, Proctor, & Cartmell, 1983; Garrett & Garrett, 1994). AI children learn about their world and how they relate to it through these more passive, noninterfering learning styles (Everett et al., 1983; Garrett, 1996). Native children learn through observation and experience, by making their own decisions, and understanding the consequences on those decisions, as long as this is balanced with their obligation to family and tribe (Davis et al., 2014).

Urban AI Parents

Although the identities of AIs can be complex and diverse, urban AIs often have shared experiences of living simultaneously in more than one cultural world (LaFromboise et al., 2010; Moran, Fleming, Somervall, & Manson, 1999). Unlike other racial or ethnic groups, AIs may be citizens of two sovereign nations–first their tribe and second the United States. These experiences along with the historical context of federal government policies heighten tensions between AI traditional values and those of the dominant culture (Stubben, 2001).

Navigation of these sometimes opposing belief systems (LaFromboise et al., 2010) may influence family structure, family functioning, and the family environment. Urban AI families are operating daily within social environments in which AI cultural traditions may not be practiced regularly and social interactions with non-native individuals and institutions are pervasive. For example, in the school environment, AIs often feel “pressure to compromise their basic cultural values and behaviors in order to successfully meet the expectations and standards” (Garrett 1996, p. 3).

Maintenance of traditional AI family structures and family composition can be challenging in the urban environment as families lose the daily contact and guidance traditionally offered by the extended family (Machamer & Gruber, 1998). In many AI tribes, the extended family and community provide the needed support during times of stress and assistance with resources to cope with that stress (LaFromboise, Heyle, & Ozer, 1990). Once in the urban area, families may become isolated, both geographically and culturally (LaFramboise Heyle, & Ozer, 1990). Without a community that shares similar tenets, ideologies, and histories, families may acculturate and begin to embrace mainstream culture, values, and parenting styles (Garrett, 1996). If urban AI social networks are maintained, it may not be with individuals tied to a specific place, tribe, or culture, but with multi-tribal and multicultural AI families. Integration into an urban AI network may connect urban families to more secular, urbanized pan-Indian traditions and ways of life (Kunitz & Levy, 1994; Paper, 2007), thus potentially diminishing culturally based or tribally based parenting.

Despite the potential challenges faced by AI families residing in urban areas, it is important to acknowledge that geographic relocation does not necessarily lessen the importance of family networks, nor does it inevitably weaken traditional values (Weaver & White, 1997). Urban AI families can effectively operate in both the AI world and the mainstream world. Urban AIs can adopt some practices from mainstream culture and simultaneously preserve customs from their own native culture (Henze & Vanett, 1993; LaFramboise, Albright, & Harris, 2010; Walters, 1999). As a result, urban AIs can be successfully bicultural–“both highly acculturated and tribally or ethnically identified” (Walters, 1999, p. 165).

A small study of urban AI mothers in the Midwest found that those who more strongly endorsed the presence of AI cultural values in their lives also reported more positive attitudes toward their lives in the city than mothers with lower levels of endorsement (Tsethlikai, Peyton, & O’Brien, 2007). Being bicultural enables many urban AI families to maintain close ties to reservation communities (Howard & Lobo, 2013), as well as creating new social connections in the urban areas that can play the same role of the extended family on the reservation (National Indian Child Abuse and Neglect Resource Center, 1980).

Prior Applications of Validated Parenting and Family Functioning Measures

In general, there has been substantial research on conceptualizing and understanding parenting practices (e.g., communication, involvement, and discipline) and family functioning (e.g., cohesion and conflict), particularly as it is linked to adolescent outcomes. Parents who are supportive, connected, engaged, involved, warm, and close with their adolescents can reduce the risk of youth antisocial behaviors (Ackard, Neumark-Sztainer, Story, & Perry, 2006; Borawski, Levers-Landis, Lovegreen, & Trapl, 2003; Davidson 2008; Flouri & Buchanan, 2003; Mmari 2010; Nelson, Padilla-Walker, & Nielson, 2015; Sen 2010).

This abundance of research also applies to the measurement of parenting and family functioning. For example, the six measures of interest in this article–parent self-agency, parental supervision, positive parenting practices, discipline, family cohesion, and parent–adolescent conflict–have been cited more than 2,500 times and examined extensively across a variety of racial/ethnic minority groups, including African Americans (Gorman-Smith, Tolan, Zelli, & Huesmann, 1996), Hispanics/Latinos (Chang, Natsuaki, & Chen, 2013; Dumka, Soterzinger, Jackson, & Roosa, 1996; Gorman-Smith et al., 1996; Piedra, Byoun, Guardini, & Cintrón, 2012), Asian/Asian Americans (Costigan & Koryzma, 2011; Chang et al., 2013), and Middle Eastern descent (Assadi, Smetana, Shahmansouri, & Mohammadi, 2011).

Regardless of the racial/ethnic group examined, these six measures have each been deemed reliable to use as one construct. For example, studies using the parent self-agency measure (Dumka et al., 1996) have found good internal consistency as one 10-item scale with a variety of racial/ethnic groups, including Spanish-speaking Latina immigrant mothers (Piedra et al., 2012), immigrant Chinese fathers (Costigan & Koryzma, 2011), and Iranian mothers (Abarashi, Tahmassian, Mazaheri, Panaghi, & Mansoori, 2014). This example highlights the far-reaching applicability of these parenting measures across diverse racial/ethnic populations.

However, researchers have noted that for AI parents living on reservations, validated measures do not adequately capture how AIs conceptualize family life because they do not take into account the influence of culture on these worldviews and may introduce bias through conflictual questions (Whitbeck, 2006; Thrane et al., 2004). Because measures are typically based on “European values and socialization techniques . . . [they may] impose values that do not approximate the family systems and values of the Native American culture” (Whitbeck, 2006, p. 185). Because no prior studies have examined any of these six parenting and family functioning measures of interest with any AI population, it remains unclear if these widely used parenting and family functioning measures are applicable with urban AI families.

Purpose of Study

The key unanswered question, therefore, is whether these extensively used parenting and family functioning measures are relevant to the lives of urban AI families. On the one hand, using measures validated with Western or mainstream populations may not account for cultural differences in AI approaches to parenting and parent–child relationships (Thrane et al., 2004). On the other hand, given that urban AI families must “live in two worlds,” navigating both mainstream and native cultures (Garrett, 1995; LaFromboise et al., 2010; Walters, 1999), these measures may capture the lived experience in the urban environment. This current study provides the first steps to this line of research by examining a central research question: Do widely used parenting and family functioning measures, which have been previously validated with other racial/ethnic minority groups, characterize how urban AIs conceptualize parenting and family functioning?

METHOD

Participants

Data for this study come from baseline surveys (N = 606) of a randomized control trial of a prevention intervention, Parenting in 2 Worlds (P2W). P2W is designed to strengthen protective factors against risky behaviors for urban AI youth through a culturally adapted parenting intervention that focuses on improving family functioning and parent–child communication. This culturally adapted parenting curriculum was developed and tested through community-based participatory research in three urban AI communities in Arizona using identical recruitment, survey administration, and approved human subjects protection procedures. Eligible participants lived in one of the three urban areas and were parents or guardians of an AI youth between the ages of 10 and 17.

Participant characteristics are reported in Table 1. In the total sample (n = 606), the majority of participants are female (77.3%) with a mean age of 36.8 years (range: 18–70). Of those listing a single tribal affiliation, the most common were Navajo (32.5%), Tohono O’odham (28.0%), and Pascua Yaqui (6.3%). Participants who are single (never married and not cohabitating) were the most numerous (35.8%), followed by those not married but living with a partner (28.3%). On average, participants have received a high school diploma or GED (mean [M] = 2.7) and report annual household incomes between $10,000 and $20,000 (M = 2.7). Nearly all—over 92%– have family members currently living on a reservation. Approximately half (45.9%) of the participants have a parent still living on the reservation and over three-quarters have extended family currently living on a reservation. A large majority of the sample lived on a reservation at some time (76.5%), including for most of their childhood (53.8%). On average, the sample has lived in the urban area for 17.3 years in households with an average of 4.6 people.

Table 1.

Descriptive Statistics for Sample of Urban American Indian Parents

Total sample (N=606)
Subset 1 (N=295)
Subset 2 (N=307)
% (M) SD % (M) SD % (M) SD pa
Gender
 Male 22.7 24.2 20.9 0.33
 Female 77.3 75.8 79.1
American Indian Tribe
 Navajo 32.5 35.4 29.7 0.16
 Tohono O’odham 28.0 27.3 28.8
 Multiple Tribes 15.6 15.5 15.7
 Other tribe 7.8 5.1 10.5
 Pascua Yaqui 6.3 7.7 4.9
 Apache 3.6 4.0 3.3
 Hopi 3.3 2.7 3.9
 No tribal affiliation 2.8 2.4 3.3
Marital status
 Married and living with spouse 13.6 13.5 p=.46 13.6
 Married but not living with spouse 6.9 7.4 6.3
 Not married but living with partner 28.3 29.1 27.6
 Divorced or separated 11.4 12.5 10.3
 Widowed 4.0 5.1 3.0
 Single, never married 35.8 32.4 39.2
Relatives currently living on a reservation (Yes)
 Parent 45.9 49.7 42.2 0.07
 Siblings 42.6 42.3 42.9 0.93
 Grandparent(s) 32.9 34.2 31.7 0.55
 Other extended family (aunts, uncles, cousins) 78.5 78.5 78.5 1.00
 No relatives 7.8 7.0 8.6 0.54
Ever lived on a reservation (Yes) 76.5 78.4 74.7 0.33
Lived on reservation most of childhood (Yes) 53.8 57.8 50.0 0.07
Years in urban area (17.3) 12.6 (17.1) 12.5 (17.4) 12.7 0.77
Age (36.8) 9.1 (37.3) 9.3 (36.4) 8.9 0.23
Educationb (2.7) 1.4 (2.7) 1.4 (2.6) 1.4 0.77
Incomec (2.7) 2.2 (2.9) 2.3 (2.6) 2.2 0.23
Household Number (4.6) 2.1 (4.6) 2.0 (4.7) 2.2 0.81
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Note. M = mean; SD = standard deviation.

a

For continuous variables, independent t tests were conducted between Subset 1 and Subset 2. For categorical variables, Pearson chi-square tests were conducted between Subset 1 and Subset 2. The p-value is for a two-tailed significance test.

b

Education was coded as (1) Did not complete high school to (6) Earned a post-graduate degree. A mean of 2.7 is between (2) Received a high school diploma or GED and (3) Attended or graduated from a technical or trade school.

c

Income was coded as (1) Less than $10,000 annually to (8) $75,000 or more annually. A mean of 2.7 is between (2) $10,000–$14,999 and (3) $15,000–$19,999.

Measures

We used six parenting and family function scales in this analysis: parent self-agency (Dumka et al., 1996); parental supervision (Loeber, Farrington, Stouthamer-Loeber, & Van Kammen, 1998); positive parenting practices (Gorman-Smith et al., 1996); discipline (Coleman & Karraker, 2000); family cohesion (Olson, Portner, & Lavee, 1985); and parent–adolescent conflict (Robin & Foster, 1989).

Parental self-agency

We used this 10-item scale to measure how confident the parent feels in his or her ability to parent successfully (Dumka et al., 1996). Parents rated their experience (e.g., “I feel sure of myself as a mother/father” and “I know things about being a mother/father that would be helpful to other parents”) on a 5-point Likert scale ranging from 1 (never) to 5 (always).

Parental supervision

We used this seven-item scale, which came from a larger 18-item measure of parenting practices (Loeber et al., 1998), to measure parental supervision and knowledge of the youth’s whereabouts (e.g., “When you and your child are both at home, do you know what he/she is doing?” and “Do you know who your child’s friends are when he/she is not at home?”). Parents rated each item on a 5-point Likert scale ranging from 1 (almost never) to 5 (almost always).

Positive parenting practices

We used this six-item scale to assess the frequency of encouragement and rewards for good behavior (e.g., “In the past 12 months, when your youth did something that you liked or approved of, how often did you . . . Do something special together, such as going to the movies, to a game, playing a game, or going somewhere?; and Give him/her a hug, pat on the back, or a kiss for it?”). Parents rated their responses on a 5-point Likert scale ranging from 1 (never) to 5 (always; Gorman-Smith et al., 1996).

Discipline

We used this five-item scale, which is part of a larger Self-Efficacy for Parenting Tasks Index (Coleman & Karraker, 2000), to measure parents’ establishment of structure and discipline for their child (e.g., “I am good at disciplining my child” and “I have trouble deciding on appropriate rules for my child”). Parents rated their responses on a 6-point Likert scale ranging from 1 (strongly disagree) to 6 (strongly agree).

Family cohesion

We used the 16-item Family Adaptability and Cohesion Evaluation Scale (FACES-III; Olson et al., 1985) to assess family cohesion. Parents rated each item (e.g., “My family members are supportive of each other during difficult times” and “Our family does things together”) on a 5-point Likert scale ranging from 1 (almost never) to 5 (almost always),

Parent–adolescent conflict

This scale was adapted the Conflict Behavior Questionnaire (Robin & Foster, 1989), a 17-item instrument used to assess positive and negative interactions in the parent–adolescent relationship (e.g., “We almost never seem to agree” and “My child often doesn’t do what I ask”). Parents rated their responses on a 4-point Likert score ranging from 1 (never) to 4 (always).

Statistical Analysis

This study examines the construct validity (Brown, 2006; Thompson, 2004) of parenting and family functioning measures in an urban AI sample by employing a four-step sequential procedure, as described by Floyd and Widaman, (1995), using the maximum likelihood estimation in Mplus (version 7, 2012). First, a priori single-factor confirmatory factor analysis (CFA) models on each of the six measures were constrained as a single factor to confirm prior studies (Assadi et al., 2011; Costigan & Koryzma, 2011; Chang et al., 2013; Dumka et al., 1996; Gorman-Smith et al., 1996; Loeber et al., 1998; Piedra et al., 2012). Second, if the single-factor CFA had poor model fit, then an exploratory factor analysis (EFA) was performed to determine and discover the underlying factor structure for urban AI parents (DeCoster, 1998). Third, to confirm the specified constructs of the EFA, newly estimated CFA models were tested (Decoster, 1998). Fourth, to ensure the single-factor CFA model would not fit the data as well as the newly estimated CFA models, a χ2 difference test was performed between the two CFA models.

Cross-validation of findings occurred in two ways. The sample was randomly split into two subsets (Floyd & Widaman, 1995)–Subset 1 (N = 295) and Subset 2 (N = 307)–and using the CFA results, new scale reliabilities were assessed using Cronbach’s alpha (α).

For all EFA and CFA analyses, items need loadings greater than .30 on one factor to be retained, and to minimize cross-loadings, the loading on one factor needs to be at least .10 higher than on any other factor. Each EFA model is rotated using the Varimax procedure and uses eigenvalues of 1.00 or higher to help ascertain meaningful factors. In addition, to evaluate the goodness of fit in the models, the χ2, χ2/degree of freedom [df], root mean square error of approximation (RMSEA), and comparative fit index (CFI) are examined. A significant χ2(p <.05) indicates a poor fitting model due to the null hypothesis predicting that the model fits the analyzed covariance matrix. However, the χ2 is sensitive to large (<200) sample sizes and may mistakenly indicate a poor fitting model (Kline, 2005). To adjust for this sensitivity, the normed chi-square (χ2/df) is tested with a χ2/df ratio of 3.0 or less indicating a good fit; however, a ratio of 5.0 is considered an acceptable fit (Hooper, Coughlan, & Mullen, 2008). The RMSEA can be less than .08 for an acceptable fit (Hooper et al., 2008).

RESULTS

Significance tests ensure no demographic differences exist between the two randomly split subsets (Table 1). The results from the EFA on Subset 1 are presented in Table 2, and the CFA results on Subset 2 are presented in Table 3

Table 2.

Exploratory Factor Analysis of Parenting Scales for Urban American Indian Parents: Varimax Rotated Factor Loadings and Maximum Likelihood Estimation

Subset 1 (N=295)
Short item wording M (SD) Factor 1 Factor 2 Factor 3 Factor 4 χ2 (df) χ2 /df RMSEA
Parental self-agency (N=295) 4.40(11), p=.95 0.40 0.00
 Sure as parent 4.12 (0.96) 0.77 0.06 0.21 0.04
 Good job as parent 3.95 (0.97) 0.81 0.21 0.20 0.05
 Useless as parent† 4.15 (1.04) 0.57 0.33 0.07 0.19
 Know things helpful to parents 3.21 (0.94) 0.44 0.00 0.25 −0.02
 Child gets own way† 3.74 (0.99) 0.22 0.59 0.22 0.26
 Usually give in to child† 3.82 (0.96) 0.13 0.87 0.13 0.25
 Solve most problems 3.82 (0.94) 0.29 0.15 0.95 0.19
 Keep trying until things change 3.98 (1.07) 0.18 0.12 0.32 0.01
 Child not do what I want† 3.41 (0.96) 0.09 0.15 0.07 0.94
 Little I can do† 3.59 (1.07) 0.01 0.23 0.06 0.49
Parental supervision (N=295) 29.60(8), p=.0002 3.70 0.10
 Set time home on school nights 4.56 (0.96) 0.97 0.24
 Set time home on weekends 4.46 (1.01) 0.78 0.24
 Know if time set was missed 4.62 (0.88) 0.22 0.65
 Know what time to be home 4.54 (0.88) 0.37 0.66
 Know what child is doing 4.58 (0.73) 0.12 0.53
 Know who friends are 4.34 (1.00) 0.26 0.52
 Child knows how to get in touch 4.77 (.73) 0.07 0.47
Positive parenting practices (N=295) 3.61(4), p=.75 0.90 0.00
 Give a reward 4.03 (1.04) 0.82 0.28
 Give a special privilege 4.01 (1.00) 0.80 0.20
 Do something special together 4.02 (1.05) 0.59 0.36
 Give a wink or smile 4.43 (0.82) 0.20 0.71
 Give praise or approval 4.55 (0.72) 0.29 0.79
 Give hug, pat on back, kiss 4.47 (0.86) 0.31 0.76
Family cohesion (N=282) 194.56(89), p<.0001 2.19 0.04
 Family members share interests 3.52 (1.17) 0.74 0.23
 Family does things together 3.87 (1.07) 0.77 0.24
 Family spends free time together 3.55 (1.10) 0.69 0.27
 Family feels very close to each other 4.04 (1.18) 0.67 0.29
 Family consults on personal decisions 3.31 (1.18) 0.61 −0.01
 Family knows each other’s friends 3.75 (1.17) 0.64 0.00
 Family goes along with decisions 3.63 (1.05) 0.59 0.21
 Family gathers in same room together 3.61 (1.17) 0.64 0.29
 Family approves of each other’s friends 3.35 (1.11) 0.57 0.02
 Family is supportive in difficult times 4.13 (1.12) 0.58 0.27
 Family is closer to outside people† 3.55 (1.23) 0.14 .58
 Family has difficulty doing things† 3.77 (1.08) 0.09 0.60
 Family pairs up rather than together† 3.61 (1.09) 0.06 0.55
 Family members go own way† 3.42 (1.05) 0.34 0.54
 Family discusses problems to outside 3.12 (1.22) 0.03 0.38
 Family avoid each other at home† 4.06 (1.04) 0.28 0.51
Parent–adolescent conflict (N=294) 224.53(103), p=.001 2.18 0.06
 We never seem to agree 2.23 (0.75) 0.37 0.09
 My child doesn’t do what I ask 2.30 (0.83) 0.48 0.23
 The talks we have are frustrating 2.02 (0.89) 0.68 0.26
 My child often seems angry at me 2.01 (0.90) 0.66 0.32
 My child acts impatient when I talk 2.16 (0.88) 0.69 0.23
 I don’t think we get along very well 1.61 (0.89) 0.58 0.32
 My child never understands my side 2.19 (0.88) 0.74 0.16
 We have big arguments over little things 1.82 (0.91) 0.74 0.18
 My child is defensive when I talk 2.08 (0.96) 0.71 0.20
 We argue a lot about the rules 1.96 (0.95) 0.74 0.21
 My child tells me I’m unfair 2.19 (1.00) 0.68 0.08
 My child is easy to get along with† 1.31 (0.50) 0.28 0.57
 My child responds well when corrected† 1.74 (0.74) 0.39 0.67
 My child is well-behaved during talks† 1.65 (0.68) 0.38 0.62
 My child likes to talk to me† 1.38 (0.61) 0.29 0.62
 My child usually listens to what I say† 1.69 (0.70) 0.23 0.76
 My child and I compromise during fights† 2.11 (0.83) −0.07 0.41
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Note. M = mean; SD = standard deviation; df = degree of freedom; RMSEA = root mean standard error of approximation.

†

Indicates reverse coded items

Table 3.

Confirmatory Factor Analysis of Parenting Scales for Urban American Indian Parents with Maximum Likelihood Estimation

Subset 2 (N=307)
Short item wording M (SD) Factor 1 Factor 2 Factor 3 Factor 4 χ2 (df) χ2 /df RMSEA CFI χ2 difference test from Ho a
Parental self-agency (N=289) 61.59(27), p=.0002 2.28 0.067 0.95 198.36(3), p<.001
 Sure as parent 4.00 (0.94) 0.76
 Good job as parent 3.88 (0.96) 0.87
 Useless as parent† 4.06 (0.98) 0.67
 Know things helpful to parents 3.20 (0.93) 0.49
 Child gets own way† 3.61 (1.02) 0.87
 Usually give in to child† 3.71 (1.03) 0.77
 Solve most problems 3.76 (0.97) 0.87
 Keep trying until things change 3.90 (0.99) 0.59
 Child not do what I want† 3.46 (0.97) 0.63
 Little I can do† 3.64 (1.04) 0.77
Parental supervision (N=297) 12.91(12), p=.37 1.08 0.016 0.99 56.31(1),
 Set time home on school nights 4.55 (0.90) 0.94
 Set time home on weekends 4.43 (1.02) 0.86
 Know if time set was missed 4.62 (0.97) 0.73
 Know what time to be home 4.48 (0.93) 0.83
 Know what child is doing 4.50 (0.83) 0.58
 Know who friends are 4.19 (1.17) 0.65
 Child knows how to get in touch 4.69 (.83) 0.57
Positive parenting practices (N=298) 11.71(8), p=.16 1.46 0.039 0.99 101.76(1), p<.001
 Give a reward 3.94 (1.02) 0.85
 Give a special privilege 3.91 (1.05) 0.83
 Do something special together 3.82 (1.07) 0.83
 Give a wink or smile 4.41 (0.85) 0.78
 Give praise or approval 4.46 (0.72) 0.80
 Give hug, pat on back, kiss 4.37 (0.92) 0.84
Discipline (N=295) 5.85(5), p=.32 1.17 0.024 0.99
 Trouble deciding rules 3.31 (1.40) 0.61
 Child never listens 2.88 (1.41) 0.70
 More trouble with discipline 3.05 (1.45) 0.88
 Ineffective discipline 2.78 (1.38) 0.82
 Good at discipline† 2.77 (1.26) 0.59
Family cohesion (N=270) 129.04(99), p=.023 1.30 0.034 0.97 44.90(1), p<.001
 Family members share interests 3.56 (1.12) 0.70
 Family does things together 3.82 (1.06) 0.72
 Family spends free time together 3.54 (1.15) 0.79
 Family feels very close to each other 4.10 (1.05) 0.82
 Family consults on personal decisions 3.36 (1.23) 0.62
 Family knows each other’s friends 3.58 (1.24) 0.56
 Family goes along with decisions 3.58 (1.10) 0.72
 Family gathers in same room together 3.58 (1.20) 0.70
 Family approves of each other’s friends 3.23 (1.12) 0.47
 Family is supportive in difficult times 4.09 (1.16) 0.59
 Family is closer to outside people† 3.58 (1.13) 0.56
 Family has difficulty doing things† 3.60 (1.12) 0.49
 Family pairs up rather than together† 3.50 (1.07) 0.39
 Family members go own way† 3.34 (1.13) 0.67
 Family discusses problems to outside people† 3.08 (1.22) 0.39
 Family avoid each other at home† 3.99 (1.07) 0.79
Parent–adolescent conflict (N=289) 171.54(118), p<.001 1.45 0.040 0.96 163.89(1), p<.001
 We never seem to agree 2.24 (0.75) 0.43
 My child doesn’t do what I ask 2.32 (0.82) 0.54
 The talks we have are frustrating 2.17 (0.91) 0.71
 My child often seems angry at me 2.08 (0.92) 0.71
 My child acts impatient when I talk 2.35 (0.91) 0.67
 I don’t think we get along very well 1.68 (0.90) 0.57
 My child never understands my side 2.23 (0.86) 0.69
 We have big arguments over little things 1.81 (0.91) 0.79
 My child is defensive when I talk 2.15 (0.96) 0.77
 We argue a lot about the rules 2.01 (0.98) 0.74
 My child tells me I’m unfair 2.24 (0.98) 0.64
 My child is easy to get along with† 1.40 (0.59) 0.67
 My child responds when corrected† 1.75 (0.72) 0.79
 My child is well-behaved during talks† 1.68 (0.69) 0.76
 My child likes to talk to me† 1.54 (0.73) 0.64
 My child usually listens to what I say† 1.70 (0.67) 0.70
 My child and I compromise during fights† 2.07 (0.72) (.19;dropped)
Open in a new tab

Note. M = mean; SD = standard deviation; df = degree of freedom; RMSEA = root mean standard error of approximation; CFI = comparative fit index.

†

Indicates reverse coded items.

a

Ho: The data will fit a one factor model.

When parent self-agency was constrained as a single-factor CFA model, the overall model fit was poor, χ2 = 262.96(33), p <.001; RMSEA = 0.16, CFI = .68, not shown. The EFA indicated a four-factor solution, and it was cross-validated with a CFA and with Cronbach’s alpha. Example items are as follows: Factor 1, I know I am doing a good job as a mother/father (α = .77); Factor 2, My child usually ends up getting his/her way (α = .82); Factor 3, I can solve most problems between my child and me (α = .67); and Factor 4, No matter what I try, my child will not do what I want (α = .66). The χ2 difference test indicates the four-factor model fits the data significantly better than a one-factor CFA model, χ2 = 198.36(3), p <.001; not shown.

For parental supervision, the single-factor CFA model had a poor fit, χ2 = 105.19(13), p <.001; RMSEA = 0.16, CFI = .73, not shown. The EFA and CFA indicate a two-factor solution. Included items are as follows: Factor 1, Does your child have a set time to be home on school nights? (α = .89); and Factor 2, If your child did not come home by the time that was set, would you know? (α = .89). The χ2 difference test indicates the two-factor model is the better fitting model, χ2 = 56.31(1), p <.001; not shown.

The single-factor CFA model for positive parenting practices fit the data poorly, χ2 = 76.78(9), p <.001; RMSEA = 0.16, CFI = .87, not shown. The EFA indicates a two-factor solution with cross-validation with the CFA and Cronbach’s alpha. Example items are as follows: Factor 1, Give him/her some reward for it, like a present, extra money, or something special to eat (α = .87); and Factor 2, Give him/her a wink or a smile (α = .84). Last, the χ2 difference test indicates the two-factor model fits the data significantly better, χ2 = 101.76(1), p <.001; not shown. The discipline scale remains a single factor (α = .84).

The family cohesion single-factor CFA model fit the data marginally well, χ2 = 168.99(100), p <.001; RMSEA = 0.051, CFI = .93, not shown. However, the EFA and CFA indicate a two-factor solution with example items loading as follows: Factor 1, Family members like to spend their free time with each other (α = .89); and Factor 2, Family members feel closer to people outside the family than to other family members (α = .70). The χ2 difference test indicates the two-factor model fits the data significantly better, χ2 = 44.90(1), p <.001; not shown.

For parent–adolescent conflict, the single- factor CFA model has poor fit, χ2 = 280.91(119), p <.001; RMSEA = 0.069, CFI = .87, not shown. The EFA results in a two-factor model with items including: Factor 1, The talks we have are frustrating (α = .90); and Factor 2, My child is easy to get along with (α = .83). The CFA model does cross-validate a two-factor model. However, the item, “My child and I compromise during fights,” loads on Factor 2 at 0.19. This is lower than the 0.30 threshold needed to retain an item, thus this question has been dropped from the analyses. The χ2 difference test indicates the two-factor parent–adolescent conflict CFA model fits the data significantly better, χ2 = 163.89(1), p <.001; not shown.

DISCUSSION

This article examined if using parenting measures validated among non-Hispanic Whites and other racial/ethnic groups apply to urban AI parents. Previous research has indicated that for AI parents living on the reservation, validated parenting measures do not adequately capture how AIs parent their children (Whitbeck, 2006). However, AIs living in the urban area face unique challenges including familial composition, social interactions, migration patterns, residential instability, and cultural disruptions (Ackerman, 1988, 1989; Lobo, 2001; Salo, 1995) that may uniquely affect their parenting style and parent–child relationships. Using both EFAs and CFAs, the only scale that remains as a single factor is the discipline scale, which suggests that these questions consistently measure a unitary underlying construct for urban AI parents. All the other scales examined–parental self-agency, parental supervision, positive parenting practices, family cohesion, and parent–adolescent conflict–however, are not adequate holistic descriptions of the parenting and familial experiences of urban AIs. Unlike prior studies with other racial/ethnic groups, these five scales are not single constructs for urban AIs and appear to capture multiple distinctive facets of parenting for this population.

These findings suggest that urban AI parents can “live in two worlds” and continue to draw from tribal cultural practices for child rearing while at the same time adopting some mainstream societal practices. For example, parenting self-agency separated into four factors. Dumka et al. (1996) state that “low levels of parenting self-agency have been linked to a passive coping approach to parent–child interaction . . . [while] parents with high parenting self-agency [are] more active and directive in a task situation with children” (p. 216). If one accounts for the parenting style of AI parents–one that is based on observation, nonverbal communication, patience, role playing, and modeling without directives (Garrett & Garrett, 1994; Guilmet & Whited, 1989)–then the finding that parenting self-agency is not a single construct for urban AI families is consistent with traditional parenting styles.

The first facet included four items related to an overall sense of confidence in the effectiveness of their parenting. A second facet tapped into permissiveness or a sense of always giving in and allowing the child to get his or her way. It could be that in urban areas, parents are more likely to assert control to protect their children. The third facet related to success and persistence in parental problem solving, while the fourth facet concerned a sense of failure and impotence as a parent, with parents indicating that they have little success with or control over their children. More research is needed to better understand the role of traditional permissive parenting practices in urban settings and verify that this view of traditional parenting has been maintained in reservations settings.

Parental supervision separated into two factors: the act of supervision (setting curfew times for school nights and weekends) versus supervisory knowledge of the child’s whereabouts, activities, and friends. In Native families supervision is traditionally provided by the extended family, including aunts, uncles, and grandparents (Machamer & Gruber, 1998; Garrett & Garrett, 1994). This may explain why the act of supervision is a different construct than the knowledge of the child’s whereabouts, activities, and friends.

In addition, cultural practices among tribal AI families indicate that parents’ primary role is to give encouragement and affection (Machamer & Gruber, 1998; Garrett & Garrett, 1994). This is supported by the findings that the positive practices parenting scale has two distinct components, one relating to expressive gestures to demonstrate approval of the child’s behavior and the other tapping more tangibly rewarding positive actions. Expressing affection and encouragement through a wink, praise, or hug were items that loaded on a different factor than providing a reward, privilege, or special shared activity. It could be that more tangible rewards are viewed differently because they are more instrumental inducements, harder for the parent to provide, or given out on a less regular basis.

The discipline scale was the only scale confirmed as a single factor. Although disciplining children in AI communities is traditionally provided by the extended family, it may be that once in the urban area, families can lose regular contact and guidance traditionally offered by the extended family (Machamer & Gruber, 1998) and may take on all aspects of discipline. Urban AI parents may begin taking on the disciplinary roles once provided by the extended family. Future research should examine if length of time living in the urban area can become an explanatory variable for the differences between the supervision and discipline scales.

For family cohesion, the measures appear to tap two separate aspects of family cohesion: internal family cohesion and external family cohesion/supports. Inspection of the first factor reveals that parents endorsed the items indicative of strong internal family cohesion, including Family feels very close to each other and Family is supportive in difficult times. The items on the second factor may tap into the parents ’ perceptions of external cohesion or support, such as discussing problems with and being close to nonfamily members. It could be that family members are seeking external support in their new urban environments to supplement strong internal family cohesion. This interpretation makes sense in light of the fact that the majority of respondents were single mothers or living with a partner but not married and average incomes were low.

A positive view of seeking external supports to enhance family cohesion, rather than a negative interpretation, is supported by research by Libby, Orton, Beals, Buchwald, and Manson (2008), who found that instrumental and perceived family support contributed to enhanced parenting satisfaction, whereas negative social support reduced satisfaction and was related to impaired parenting practices. Traditionally, AI families have large extended family networks that support them; thus it could be that urban families are maintaining this tradition by finding sources of support outside of the home.

On the parent–adolescent conflict scale there is a clear separation of items that tap into the parent’s perceptions of conflict: (a) parent centered conflict and (b) aspects of the child’s temperament, preferences, and behavior that contribute to conflict. Traditional views of parenting honor their children’s right to develop independently of the parent by allowing them to find out who they are by exploring and learning from experience, and thus it makes sense that parents would view items that were centered on aspects of the child’s demeanor and temperament (My child is easy to get along with) as separate from the items centered on their perceptions of sources of conflict with the child (We never seem to agree).

In addition to the scales separating by traditional and mainstream parenting practices components, questions sometimes sorted into positively and negatively worded realms. Both the FACES-III and the parent-adolescent conflict scale are illustrative of this. In both scales, the positively worded questions loaded on one factor, and the second factor contained the negatively worded questions. This finding may be partially explained through AI culture and the values of balance and harmony. In order to achieve balance and harmony, one must learn to coexist with and accept the interconnected forces and influences in life (Lowe, 2002). Although in mainstream culture, these are often seen as differing and antagonistic (e.g., “My child is easy to get along with” vs. “I don’t think my child and I get along very well”), in AI culture, these dual forces are often viewed as circular and complementary rather than linear and in opposition (Allen, 1986). Thus, for many AIs negative family relationships may not be viewed as simply the opposite of positive relationships, but as two distinct realms, and simply reverse coding the negatively valenced items may not capture a single underlying cultural construct.

The sorting of positively and negatively worded items onto distinct factors may also partially be explained methodologically. Scales are commonly created with both positive and negative valence questions to account for acquiescence response set bias–the tendency for the participant to give identical responses to all questions regardless of the wording or content of the question (Barnette, 2000). The underlying assumption is that the negatively worded questions are the opposite of the positively worded question and, with reverse coding, will equivalently measure the same underlying construct (Schmitz & Baer, 2001). However, prior research, in general, has noted that mixing negatively worded questions can often be too confusing, result in differential factor loadings, and threaten construct validity (Barnette, 2000; Locker, Jokovic, & Allison, 2007; Schmitz & Baer, 2001).

Limitations

There are several limitations to this study. First, the study did not capture any qualitative data on how urban AIs parent their children or how the families function. Thus, no explanatory conclusions can be drawn from this study about why these scales factor out as they do. To better measure and understand parenting and family functioning, qualitative data should be gathered to understand the cultural and contextual ways that urban AIs parent.

Second, this study cannot be generalized to all urban AI parents or compared to either rural or reservation-dwelling AI parents. Generalizable research with these geographically distinct groups of AI parents is lacking, and thus comparisons to these subgroups are unobtainable. Although this sample encompasses many tribes and urban AI communities in several cities with distinct migration histories, the majority of this sample is representative of families whose heritage is rooted in Arizona tribes. Urban AI parents from other tribes, with varied cultural practices, and living in other urban parts of the United States may have other unique familial, parenting, or cultural challenges not experienced in this sample.

Additionally, exacerbating family stress and poverty should be examined in future multigroup analyses to determine if these two factors can undermine positive parenting intentions. Future studies should collect data from a larger, more representative sample to test the generalizability of these findings.

Conclusion

In conclusion, this is the first study we are aware of that examines how previously validated parenting measures apply to urban AI parents. These findings indicate that urban AI parents integrate traditional tribal parenting practices with mainstream societal parenting styles, and that measures designed to combine positively and negatively valenced questions do not always represent a single underlying construct. Understanding how urban AIs parent their children moves science forward by beginning to take into account how AIs conceptualize parenting, as well as providing a foundation for further research on how urban AI parents can strengthen their families and protect their children from engaging in risky behaviors.

Acknowledgments

Funding for this research was supported in part by the National Institute on Minority Health and Health Disparities of the National Institutes of Health, award 1 R01 MD 006110 (S. Kulis, P.I.). The content is solely the responsibility of the authors and does not necessarily represent the views or policies of the NIMHD, the NIH, or the Department of Health and Human Services.

Contributor Information

Stephanie L. Ayers, Southwest Interdisciplinary Research Center, Arizona State University

Stephen Kulis, Southwest Interdisciplinary Research Center, Arizona State University and T. Denny Sanford School of Social and Family Dynamics, Arizona State University.

Monica Tsethlikai, T. Denny Sanford School of Social and Family Dynamics, Arizona State University.

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