Published on in Vol 3, No 1 (2014): Jan-Mar

Stress and Mental Health in Families With Different Income Levels: A Strategy to Collect Multi-Actor Data

Stress and Mental Health in Families With Different Income Levels: A Strategy to Collect Multi-Actor Data

Stress and Mental Health in Families With Different Income Levels: A Strategy to Collect Multi-Actor Data

Authors of this article:

Koen Ponnet 1, 2 Author Orcid Image ;   Edwin Wouters 2

Original Paper

1Department of Sociology, University of Antwerp, Antwerp, Belgium

2Higher Institute for Family Sciences, HuBrussel, Brussels, Belgium

Corresponding Author:

Koen Ponnet, PhD

Department of Sociology, University of Antwerp

Sint-Jacobstraat 2

Antwerp, B2000


Phone: 32 484147972

Fax:32 32655793


Background: Several studies have focused on family stress processes, examining the association between various sources of stress and the mental health and well-being of parents and adolescents. The majority of these studies take the individual as the unit of analysis. Multi-actor panel data make it possible to examine the dynamics of the family context over time and the differentiating effects of individual roles within the same family. Accurate information about family processes allows practitioners to provide support that enhances family resilience and minimizes the risk of mental health problems.

Objective: Our study contributes to the research on family stress processes by focusing on families with different income levels, and by collecting panel data from mothers, fathers, and adolescents within the same family.

Methods: The relationship between mothers, fathers, and children (RMFC) study is an ongoing Flemish multi-actor panel study that aims to enhance our understanding of family processes that protect the mental health and well-being of two-parent families with a target adolescent between 11 and 17 years old. Mothers, fathers, and children provide information about various aspects of family life, including finances, sources of stress, health, mental health, parenting, and coping strategies. Measures have been chosen whenever possible that have sound conceptual underpinnings and robust psychometric properties. The study posed two challenges. First, economically disadvantaged families are difficult to reach. Second, the collection of multi-actor data is often plagued by high nonresponse. To ensure that the families were targeted as successfully as possible, the study employed a purposive nonprobability sampling method.

Results: The RMFC study is one of the largest triadic panel studies of its kind. The first wave of quantitative data collection was conducted between February 2012 and January 2013. A total of 2566 individuals of 880 families participated in our study. The second wave of data collection will be undertaken 6-12 months later.

Conclusions: The strength of the RMFC study is its multi-actor panel approach of data collection among families with different income levels. Strategies that were followed to address the empirical issues involved with the sampling design are discussed, together with theoretical and practical implications.

JMIR Res Protoc 2014;3(1):e1




Growing up and living with financial hardship is detrimental to one’s physical and mental health. Rates of psychopathology and various types of mental disorders (eg, depression, anxiety) are higher among individuals from low-income families than among individuals from middle- and high-income families [1,2]. Financial hardship creates a context of stress in which stressors build on one another and contribute to mental health problems for adults and children [3]. In addition, children from low-income families are more likely to engage in problematic behavior, such as aggressive behavior and substance abuse [4]. Most research on the negative influence of financial hardship or stress on families and children has been based on the family stress model [5,6]. This model specifies that high levels of financial stress have detrimental effects on parental psychopathology, interparental conflict, and parenting, and these parental problems damage children’s mental health and well-being.

Despite strong support for the original and expanded family stress models (eg, including social support, health problems) in a variety of contexts, most studies use data from one family member to examine relationships between family members [7]. Our study expands upon previous studies on stress processes by including paired data from both parents and an adolescent. Given the lack of extensive research using a dyadic approach to consider family stress processes [8], research that takes into account the interdependence and mutual influence between mothers and fathers—or between parents and adolescents—could improve our understanding of the processes that protect the mental health and well-being of parents and adolescents. This understanding is essential for developing and implementing successful intervention programs. Accurate information about the mechanisms of stress allows practitioners to provide support that enhances family resilience and minimizes the risk of mental health problems.

Our paper describes how the dyadic approach to data collection and analysis differs from the more common individualistic approach. This is followed by a description of the aims and study design of a project entitled, “Relationships between mothers, fathers, and children” (RMFC). The unique contribution of the interuniversity RMFC project is its multi-actor panel approach to data collection: several types of information (eg, finances, various sources of stress, health, mental health, parenting, and coping strategies) were collected from mothers, fathers, and adolescents within the same families. The first wave of data collection started in February 2012 and ended in January 2013. The second wave of data collection will be undertaken 6-12 months later. The outcomes will include a detailed picture of family functioning and an enhanced understanding of processes that protect the mental health and well-being of both parents and adolescents.

Individualistic Versus Dyadic Approaches

One common shortcoming of many studies on family stress processes is that they focus on either mothers or fathers. Because mothers and fathers belong to the same family, however, they should not be viewed simply as two independent individuals. They share a characteristic known as nonindependence [9]. The characteristic of nonindependence can be assumed if two scores from two members of a dyad are more similar to one another than are two scores from two people who are not members of the same dyad. Information about nonindependence has theoretical and statistical implications. Theoretically, nonindependence can be used to infer reciprocity, synchrony, or influence within a dyad. Statistically, it requires that the data be analysed in ways that include both the dyad and the person as units of analysis. If it is ignored, nonindependence can bias tests of significance [9,10].

By administering data from two parents within the same family, both the person and the dyad can be used as units of analysis. The choice to focus on the person or the dyad in various constructs is related to actor-partner effects within the dyads [11]. An actor effect refers to the impact of an independent variable of a person on an outcome variable of the same person (eg, a mother who experiences high levels of financial stress is more likely to experience depressive feelings). A partner effect occurs when a person’s score on an independent variable affects the partner’s score on an outcome variable (eg, increased levels of stress experienced by one parent might be negatively associated with the partner’s marital satisfaction). As such, the use of a dyadic approach enables researchers to study separate paths through which financial stress experienced by mothers and fathers affects depressive symptoms, health, marital problems, or parenting behaviors of both the person and the partner.

The dyadic approach to data collection and analysis is nevertheless not limited to mother-father dyads. Mother-adolescent or father-adolescent dyads can also be used as unit of analysis. Although several researchers advocate the use of multiple informants in studies on family functioning [12], the tendency to consider children or adolescents as active agents is quite recent [13]. Epstein and colleagues [14] described three reasons for considering the perceptions of multiple family members (ie, mothers, fathers, and adolescents) in the assessment of families. First, each family informant provides a unique perspective on events occurring within their families. Second, different family members may provide slightly different information based on their own experiences in the family and on differential knowledge about the others. Third, although family members can witness the overt behaviors of themselves and other family members, they can be aware of only their own internal states and perceptions.

We strongly believe that inclusion of multiple family members in studies on family stress processes could enhance knowledge concerning their mutual influence. For this reason, the RMFC project (as outlined below) applied a multi-actor panel design, including information on both of the married or cohabiting parents, as well as on a target adolescent between 11 and 17 years of age.


The overall aim of the RMFC project is to explore how various sources of stress affect the mental health and well-being of parents and adolescents. Our study contributes to previous research on family stress processes in several ways.

First, most previous research studies on family stress processes were conducted in the United States. Our study was conducted in the Dutch-speaking part of Belgium (Flanders), and it should be seen in this context. Because Belgium is quite different from the United States in terms of economic and social security, the experiences and responses of families might differ.

Second, the RMFC study focuses on low-, middle-, and high-income families. Although financial stress and ongoing strains seem to be more prevalent in low-income families compared to middle- or high-income families, it appears that low-income families are also more vulnerable to events and strains (ie, different sources of stress have more devastating impact in these families) [4]. An important issue is how to understand the processes that are responsible for the variability that exists among families with different income levels. This includes the identification of factors that cause some families, or family members, to experience mental health problems whereas other families seem not to be compromised. To do so, we collected information on various sources of stress (eg, financial stress, parental stress, marital stress, and daily stress) and coping strategies to manage that stress.

Third, the RMFC project is based on a family-system approach (as described above), in which the family is considered as a complex, integrated whole in which individual family members are necessarily interdependent [15,16]. For this reason, data were collected from mothers, fathers, and adolescents. These triadic data make it possible to examine pathways within and between family members.

Fourth, families were invited to take part in a follow-up study. One major advantage of the panel design stems from its ability to compare the same individual at different times, and hence permit within-individual analyses of individual change. From a multi-actor design standpoint, each family member can have a unique trajectory. The trajectories can differ in magnitude (eg, the rate of change can be more steep for mothers than that for fathers) or pattern (eg, change can be linear for mothers and nonlinear for fathers) [17].


In general, probability sampling is the preferred approach for scientifically conducted surveys. A probability sample is defined as a sample in which individuals are chosen at random, such that each individual has a calculable, nonzero probability of selection. The RMFC project, however, used a purposively nonprobabilistic sampling design with oversampling of low-income families. The design was selected for two reasons.

First, the RMFC project involved gaining access to economically disadvantaged families, in addition to middle- to high-income families. This posed a challenge, given that many economically disadvantaged families are “hidden” and notoriously difficult to access in a systematic way [18]. In most studies in which the representative household survey is the golden standard for data collection, such hidden population segments are either lost by definition or, at best, grossly underrepresented [19]. Thus, most studies of low-income families use some form of nonprobability sampling in order to recruit participants [20]. The design has also the advantage of being affordable.

Second, multi-actor data are highly valuable for investigating questions about family functioning, and they improve the reliability of information on the subjective characteristics of household members. Nevertheless, the collection of such data is often plagued by high nonresponse [21]. For example, the recent “Divorce in Flanders” study, in which the sample was drawn from the Belgian National Register, applied a multi-actor design, including information on both currently and formerly married partners, as well as on their children aged 10 years or older. As noted by the researchers [22], the response rate for dyadic data (ie, both mother and father responded to the questionnaire) from married families was 31.41%, while the response rate for triadic data from married families (ie, mother, father, and a child responded to the questionnaire) was 12.75%. This made it difficult to generalize the triadic findings. One of the problems associated with a multi-actor approach is that data collection is complicated by nonresponse on the part of one family member. Whether a particular family member will respond depends upon individual characteristics, in addition to characteristics of the mutual relationships between all family members involved. In a study on nonresponse by secondary respondents in multi-actor surveys, Kalmijn and Liefbroer [21] reported that a parent is more likely to grant permission to collect data from the child if the relationship between the parent and the child is intensive and of good quality. The quality of the relationship also has a positive effect on the likelihood that children will return the questionnaire [21]. Relationship quality thus has an impact on the response process, regardless of the sampling design that is selected.

Taken together, given the lack of a sampling frame for our target population of families, a random selection from the study population was not a realistic option. As recommended by some authors [23,24], however, the RMFC project followed several strategies in order to address the empirical issues involved in the use of a nonprobability sample. More specifically, efforts were made to ensure that the study sample provided adequate statistical power for hypothesis testing. It has been shown that, other things being equal, large samples always produce estimates about true population parameters that are more efficient and unbiased than are those produced by small samples. Furthermore, the researchers engaged in multi-agency collaboration. Finally, a national sample, the European Union Statistics on Income and Living Conditions (EU-SILC) [25], was used to compare our data. Because the purpose of the EU-SILC is different from the purpose of our study, it was possible to use probability sampling.

Calculation of A Priori Sample Size

The power of a statistical test depends upon the following parameters: the reliability of the sample results, the sample size, the effect size, and the significance criterion. Following the proposed conventions described by Cohen [26], we adopted a desired power value of at least .80 and a desired alpha score of no greater than .05. Based on previous multi-actor research studies on family stress processes [7,27] and taking into account the number of measures that we wanted to include in our future family stress models (see below), we expect to study structural equation models with a maximum of 22 observed and 8 latent variables. Using the statistical program of Soper [28], the calculation of a priori sample size (with an anticipated medium effect size of 0.3) returned a recommended minimum sample size of 241 households. A more demanding effect size (ie, 0.1) would require us to recruit 625 households.


Two-parent families with a target adolescent in secondary school (ie, between 11 and 17 years of age) were recruited from February 2012 through January 2013. Families were recruited from five provinces of the Dutch-speaking part of Belgium (ie, Flanders), with assistance from undergraduate students from two institutes of higher education: the Higher Institute for Family Sciences and the University of Antwerp. A two-stage strategy was used to reach the households. First, each of the students from the Higher Institute for Family Sciences (n=85) was instructed to recruit low-, middle-, and high-income two-parent families. Students received course credit for their recruitment efforts. The average age of the students from the Higher Institute was 34.85 (SD 1.24) and most were working in the social services. As such, the project took advantage of the social networks of the students in order to obtain a large set of potential respondents. Each of the targeted families (mother, father, and target adolescent) was sent a letter explaining the purpose of the research. The families were subsequently contacted and asked to participate. In total, 1020 packages of envelopes and questionnaires were distributed (12 per student), and 824/1020 (80.78%) were returned by post. Second, four 21-year-old students from the University of Antwerp recruited 56 low-income families through community agencies, including centers for general welfare (CAW) and public centers for social welfare (OCMW), as well as through service and meeting centers. The students contacted 25 community agencies distributed across the different regions, and 14 volunteered to cooperate. Personnel in the community agencies selected potential families, and the students contacted them to assess their willingness to participate. Once they agreed, families were given the packages of envelopes and questionnaires.

Ethics and Data Collection

Each participant received a plain-language statement and a written informed-consent form. The study protocol was approved by the Ethics Committee of the University of Antwerp (Belgian registration number: B300201215397).

Each family received a package of three envelopes and questionnaires. A letter accompanying the questionnaire introduced the study as an investigation of “the relationship between mothers, fathers, and children” and provided information on the purpose of the study in lay terms. The first page of the questionnaire instructed the target participants to complete the booklets individually and not to discuss the content of the questionnaire with one another. The booklets were to be returned in a stamped envelope. Mothers, fathers, and adolescents were asked to sign written consent forms, which were to be returned by post in a separate envelope. All families were also asked if they were willing to take part in future research. It was made clear in the written informed-consent form that participation was voluntary. In total, 51.2% (418/817) families of the triads volunteered to be followed up.

Content of the Parent Questionnaire

The questionnaires for mothers and fathers were identical (except for such phrasings as “he/she” or “father/mother”) and contained 290 items. A small pilot study (six mothers and fathers) revealed that it took about 40 minutes to complete the parent survey.

The questionnaire included items on sociodemographic indicators, including age, education, nationality, country of origin, religiosity, occupation, civil status, length of relationship, number of household members, and, in the case of multiple children, the age of youngest and oldest child in the household. Parents were also asked to provide sociodemographic information on the target adolescent, including the age and gender of the adolescent, relationship to the adolescent (eg, biological mother, stepmother), education, school years repeated, and the presence of any developmental disorders.

To gain insights into various aspects of family functioning, measures have been chosen whenever possible that have sound conceptual underpinnings and robust psychometric properties. To assess parental mental health, the Hospital Anxiety and Depression Scale [29] and a short form of the CES-D [30] were included. The physical health item was drawn from the EU-SILC instrument [31]. Interparental relationship was measured using the O’Leary-Porter Scale [32], subscales from the Conflicts and Problem-Solving Strategies questionnaire [33], the Multidimensional Stress Questionnaire for Couples [34], and the Quality of Marriage Index [35]. Parent-adolescent relationship was assessed using the Parent-Adolescent Communication Scale developed by Barnes and Olson [36], subscales from the Parental Behavior Scale [37], and the Psychological Control Scale [38]. The questionnaire also included subscales from the Dutch version of the Parenting Stress Index [39] and the Parenting Sense-of-Competence Scale [40]. Information about the family’s financial situation was assessed with self-constructed items on savings, financial stress, financial insecurity and financial needs, as well as with items drawn from the EU-SILC [31]. Consistent with other studies involving fragile families [41,42], items on coping strategies and social support were included as well, like the Carver Coping Scale [43]. Finally, the questionnaire included items about the adolescent’s school competence, and the adolescent’s emotional and behavioral problems were assessed using the Child Behavioral Checklist [44].

Content of the Adolescent Questionnaire

The adolescent questionnaire contained 191 items and took about 25 minutes to complete. Sociodemographic questions included gender, age, number of brothers and sisters, education, and the marital status of parents. Information on stress was assessed with items drawn from the Sources of Stress Index [45]. Adolescents completed scales on parent-adolescent relationship twice, once for the mother-child relationship and once for the father-child relationship. Scales included were the Parent-Adolescent Communication Scale developed by Barnes and Olson [36], subscales from the Parental Behavior Scale [37], and the Psychological Control Scale [38]. Peer attachment was assessed with a subscale from the Inventory of Parent and Peer Attachment [46]. Finally, similar to the parent questionnaire but adapted to the adolescent perspective, items were included about adolescents’ school competence, coping strategies, and the adolescent’s emotional and behavioral problems.

Sample Characteristics

Over the 12-month survey period, 880 households were recruited: 824 households in the first stage and 56 households in the second stage (see the above-mentioned recruitment procedure). The dataset contained information on 817 triads (mother, father, and adolescent) and 857 mother-father dyads. Table 1 provides an overview of the number of participants. The average ages of fathers and mothers were 46.03 (SD 5.10) and 43.72 (SD 4.56) years, respectively. Within our sample, 2.7% (23/848) of the mothers and 4.0% (34/850) of the fathers had completed preprimary or primary education; 33.7% (286/848) of the mothers and 41.5% (353/850) of the fathers had completed secondary education; and 63.6% (539/848) of the mothers and 54.5% (463/850) of the fathers had completed postsecondary education. With regard to work status, 95.3% (810/850) of the fathers and 84.3% (721/855) of the mothers worked either full-time or part-time. Furthermore, three-person households accounted for 10.4% (89/855) of the sample, four-person households for 46.8% (400/855), five-person households for 29.2% (249/855), six-person household for 9.6% (82/855), and households of seven or more people for 4.1% (35/855). Using the modified OECD equivalence scale [47], the average household income of our sample was €1592.95 (SD 604.17).

Table 1. Overview of the RMFC dataset (N=2566).

Households, n (%)Individuals, n (%)
Triadic data (mother, father, and adolescent)817 (92.84)2451 (95.51)
Dyadic data (mother and father)40 (4.54)80 (3.12)
Dyadic data (parent and adolescent)12 (1.36)24 (0.94)
Individual data (mother or father)11 (1.25)11 (0.43)

Comparisons Between the RMFC and EU-SILC Samples

The EU-SILC is the EU reference source for microlevel data on income and living conditions. The dataset includes internationally and cross-temporary comparable variables for all EU Member States [48]. The reference population of the EU-SILC consists of private households residing in the participating countries at the time of selection. In this study, we selected households from the Dutch-speaking part of Belgium that had at least one child between 11 and 17 years of age (317/3084, 10.28%). Calculations are based on the EU-SILC 2011 user database.

Our findings revealed that the mean age of the mothers and that of the fathers did not differ significantly between the two samples (F1,1156=3.25 for mothers and F1,1156=2.25 for fathers). As shown in Table 2, the educational attainment of mothers in the RMFC sample was somewhat higher than was that of the EU-SILC sample (χ24=18.66, P<.001). With regard to fathers’ educational attainment, no significant differences were found between the two samples (χ24=8.69, P=.069). With regard to the employment of parents (Table 2), no significant differences were found between the samples for mothers (χ24=4.28, P=.369) or for fathers (χ24=1.41, P=.888). As shown in Table 2, households in the EU-SILC sample were more likely to consist of three members and less likely to consist of five or more members (χ24=14.28, P=.006). As expected, low-income households were oversampled in the RMFC dataset, relative to the EU-SILC dataset. Figure 1 presents an overview of household income.

Table 2. Characteristics of the RMFC and the EU-SILC sample.

RMFC sample, (n=857)EU-SILC sample, (n=317)

n (%)n (%)
Educational level of mothers

Preprimary education12/848 (1.42)3/317 (0.95)

Primary education11/848 (1.30)8/317 (2.52)

Lower secondary education53/848 (6.25)24/317 (7.57)

(Upper) secondary education233/848 (27.48)122/317 (38.49)

Postsecondary education539/848 (63.56)160/317 (50.47)
Educational level of fathers

Preprimary education23/850 (2.71)3/317 (0.95)

Primary education11/850 (1.29)9/317 (2.84)

Lower secondary education99/850 (11.65)31/317 (9.78)

(Upper) secondary education254/850 (29.88)112/317 (35.33)

Postsecondary education463/850 (54.47)162/317 (51.10)

Mothers721/855 (84.33)266/317 (83.91)

Fathers810/850 (95.29)296/317 (93.38)
Household members

Three89/855 (10.41)54/317 (17.03)

Four400/855 (46.78)158/317 (49.84)

Five249/855 (29.12)71/317 (22.39)

Six82/855 (9.59)24/317 (7.57)

Seven or more35/855 (4.09)10/317 (3.15)
Figure 1. Equivalised household income of the RMFC and the SILC sample.
View this figure

An Example of Future Research Directions

During the past two decades, a large body of research has focused on family stress processes [49], examining family-based pathways through which financial stress is associated with the adjustment of parents and adolescents. Most research on the negative influence of financial hardship on families and adolescents has been based on the family stress model [6,49]. This model predicts that high levels of financial stress have detrimental effects on parental mental health, interparental conflict, and parenting, and these parental problems damage children’s mental health and well-being (see Figure 2). To date, studies that have applied the family stress model have typically analysed data on mothers and fathers separately [50]. These studies thus neglect the interdependence of the two parents and the mutual influence that they have on each other.

The RMFC study may contribute to the research on family stress processes by its multi-actor approach, which enables us to test more advanced theoretical models. For instance, as shown in Figure 3, analyses can be grounded in the actor-partner interdependence model (APIM) [9], a multi-actor approach which proposes that the predictor variable of both the respondent (actor effects) and the respondent’s partner (partner effects) influence the respondent’s outcome variable [51]. The APIM allows for the testing of both actor and partner effects, and may thus provide better insights into how mothers and fathers each respond to financial stress.

Figure 2. An individual approach of the family stress model.
View this figure
Figure 3. An actor-partner approach of the family stress model. A: actor effects; P: partner effects.
View this figure

Principal Findings

We described a strategy to collect multi-actor data from families with different income levels. The families participating in the study were living in the Dutch-speaking part of Belgium. To improve our understanding of processes that protect the mental health and well-being of both parents and adolescents, we collected information about various aspects of family life, including finances, stress, health, mental health, parenting, and coping strategies.

Gaining access to economically disadvantaged families and recruiting mothers, fathers, and adolescents to participate in a research study poses two challenges. As noted above, it would be impossible to obtain a random sample of the study population, given the absence of a comprehensive population list. The RMFC project therefore employed a nonprobability sampling method, purposive sampling, in order to ensure that this group was targeted as successfully as possible. One major drawback of purposive sampling is that it limits the ability to generalize results. To mitigate this problem, the researchers attempted to obtain a large sample size, and they engaged in multi-agency research collaborations. A posteriori comparisons between the RMFC sample and the EU-SILC probability sample revealed more similarities than differences between the demographic characteristics of the families in the two samples. For all of these reasons, the present nonprobability sampling procedure can be considered as an alternative or as a complementary strategy for attaining more comprehensive data with which to investigate research questions concerning family stress processes.


Multi-actor information on family functioning has both theoretical and practical implications. For example, one limitation in the current literature that can be overcome by researchers using the RMFC data involves the relative lack of attention to possible gender differences in the pathways from stress to parenting [52,53]. This limitation stems from the fact that early parenting research focused almost exclusively on mothers, partly due to the common assumption that mothers play a central role in child development [54].

By focusing on the dyad as unit of analysis, researchers can examine effects within and between parents and begin to understand the dynamic processes that constitute the relationship [17]. The multi-actor panel approach will make it possible to examine the dynamics of the family context over time and the differentiating effects of individual roles within the same family. In this manner, the study will provide better insight into differences in the ways in which family members respond to different sources of stress. This knowledge might subsequently help practitioners in their efforts to support fragile families. When the coping strategies are identified and matched to particular stressors and characteristics of the family members, practitioners may then teach the family members to use the strategies that best align to their particular situation and characteristics.


This study was funded with support from the FWO Methusalem Research Fund (41/FA040100/FFB2998) and the Research Fund (BOF) of University of Antwerp (41/FA040300/5/5628). Additional funding for the study was provided by the Higher Institute for Family Sciences. We would like to thank Bea Cantillon, PhD, who encouraged the researchers to collect data from low-income families. Further, we are thankful to the students and the representatives of the community agencies for their important contributions to this work.

Authors' Contributions

KP is the PI and leads the study. KP conceived the study and prepared the first draft of this paper which has been reviewed by EW.

Conflicts of Interest

None declared.

  1. Santiago CD, Etter EM, Wadsworth ME, Raviv T. Predictors of responses to stress among families coping with poverty-related stress. Anxiety Stress Coping 2012 May;25(3):239-258. [CrossRef] [Medline]
  2. Wadsworth ME, Achenbach TM. Explaining the link between low socioeconomic status and psychopathology: testing two mechanisms of the social causation hypothesis. J Consult Clin Psychol 2005 Dec;73(6):1146-1153. [CrossRef] [Medline]
  3. Wadsworth ME, Raviv T, Reinhard C, Wolff B, Santiago CD, Einhorn L. An indirect effects model of the association between poverty and child functioning: the role of children's poverty-related stress. J Loss Trauma 2008 Feb 18;13(2-3):156-185. [CrossRef]
  4. Thoits PA. Life stress, social support, and psychological vulnerability: epidemiological considerations. J Community Psychol 1982 Oct;10(4):341-362. [Medline]
  5. Conger RD, Conger KJ. Resilience in midwestern families: selected findings from the first decade of a prospective, longitudinal study. J Marriage Family 2002 May;64(2):361-373. [CrossRef]
  6. Conger RD, Ge X, Elder GH, Lorenz FO, Simons RL. Economic stress, coercive family process, and developmental problems of adolescents. Child Dev 1994 Apr;65(2 Spec No):541-561. [Medline]
  7. Parke RD, Coltrane S, Duffy S, Buriel R, Dennis J, Powers J, et al. Economic stress, parenting, and child adjustment in Mexican American and European American families. Child Dev 2004;75(6):1632-1656. [CrossRef] [Medline]
  8. Falconier M, Epstein N. Couples experiencing financial strain: what we know and what we can do. Family Relat 2011;60(3):303-317. [CrossRef]
  9. Kenny D, Kashy D, Cook WH, Simpson JA. Dyadic Data Analysis (Methodology in the Social Sciences). New York: The Guilford Press; 2006.
  10. Alferes VR, Kenny DA. SPSS programs for the measurement of nonindependence in standard dyadic designs. Behav Res Methods 2009 Feb;41(1):47-54. [CrossRef] [Medline]
  11. Watne T, Brennan L. Doing more with less: the analytical secrets of dyadic data. 2010 Presented at: Annual Conference Proceedings 2010; November 29--December 1; Christchurch, New Zealand p. 1-7.
  12. Bastaits K, Ponnet K, Mortelmans D. Parenting of divorced fathers and the association with children's self-esteem. J Youth Adolesc 2012 Dec;41(12):1643-1656. [CrossRef] [Medline]
  13. Ben-Arieh A. Where are the children? Children’s role in measuring and monitoring their well-being. Soc Indic Res 2005 Dec;74(3):573-596. [CrossRef]
  14. Epstein MK, Renk K, Duhig AM, Bosco GL, Phares V. Interparental conflict, adolescent behavioral problems, and adolescent competence: convergent and discriminant validity. Educat Psychol Measure 2004 Jun 01;64(3):475-495. [CrossRef]
  15. Cox MJ, Paley B. Families as systems. Annu Rev Psychol 1997;48:243-267. [CrossRef] [Medline]
  16. Minuchin S. Families and Family Therapy. Cambridge, MA: Harvard University Press; 1974.
  17. Lyons KS, Sayer AG. Longitudinal dyad models in family research. J Marriage Family 2005 Nov;67(4):1048-1060. [CrossRef]
  18. Faugier J, Sargeant M. Sampling hard to reach populations. J Adv Nurs 1997 Oct;26(4):790-797. [Medline]
  19. Agadjanian V, Zotova N. Sampling and surveying hard-to-reach populations for demographic research. A study of female labor migrants in Moscow, Russia. Dem Res 2012 Feb 28;26:131-150. [CrossRef]
  20. Boag-Munroe G, Evangelou M. From hard to reach to how to reach: a systematic review of the literature on hard-to-reach families. Res Paper Educ 2012 Apr;27(2):209-239. [CrossRef]
  21. Kalmijn M, Liefbroer AC. Nonresponse of secondary respondents in multi-actor surveys: determinants, consequences, and possible remedies. J Family Issue 2010 Dec 17;32(6):735-766. [CrossRef]
  22. Mortelmans D, Pasteels I, Van Bavel J, Bracke P, Matthijs K, Van Peer C. Divorce in Flanders. Data collection and code book.   URL: [accessed 2013-12-18] [WebCite Cache]
  23. Atkinson R, Flint J. Accessing hidden and hard-to-reach populations: snowball research strategies. Social Res Update 2001;33:1-8 [FREE Full text]
  24. Guo S, Hussey DL. Nonprobability sampling in social work research. J Social Serv Res 2004 May 10;30(3):1-18. [CrossRef]
  25. EU Statistics on Income and Living Conditions.   URL: [accessed 2013-12-18] [WebCite Cache]
  26. Cohen J. Some statistical issues in psychological research. In: Wolman B, editor. Handbook of Clinical Psychology. New York: McGraw-Hill; 1965:95-121.
  27. Ponnet K, Wouters E, Goedemé T, Mortelmans D. An Exploration of Family-Based Pathways Through Which Parents’ Financial Stress Is Associated With Problem Behaviour of Adolescents. Working Paper 12. Antwerp: Centre for Social Policy, University Antwerp; 2012:1-28.
  28. Soper D. A priori sample size calculator for structural equation models (Online Software). 2013.   URL: [accessed 2013-12-18] [WebCite Cache]
  29. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983 Jun;67(6):361-370. [Medline]
  30. Kohout FJ, Berkman LF, Evans DA, Cornoni-Huntley J. Two shorter forms of the CES-D (Center for Epidemiological Studies Depression) depression symptoms index. J Aging Health 1993 May;5(2):179-193. [Medline]
  31. Eurostat. Survey on income and living conditions (SILC) questionnaire manual. 2008.   URL: [accessed 2013-12-18] [WebCite Cache]
  32. Porter B, O'Leary KD. Marital discord and childhood behavior problems. J Abnorm Child Psychol 1980 Sep;8(3):287-295. [Medline]
  33. Kerig PK. Assessing the links between interparental conflict and child adjustment: the conflicts and problem-solving scales. J Family Psychol 1996;10(4):454-473. [CrossRef]
  34. Bodenmann G, Ledermann T, Bradbury TN. Stress, sex, and satisfaction in marriage. Person Relation 2007 Dec;14(4):551-569. [CrossRef]
  35. Norton R. Measuring marital quality: a critical look at the dependent variable. J Marriage Family 1983;45(1):141-151.
  36. Barnes HL, Olson DH. Parent-adolescent communication and the circumplex model. Child Develop 1985 Apr;56(2):438-447. [CrossRef]
  37. Van Leeuwen K, Vermulst A, Kroes G, De Meyer G, Veerman J. De verkorte schaal voor ouderlijk gedrag. VSOG voor ouders van jeugdigen van 4 t/m 18 jaar. Nijmegen: Katholieke Universiteit Leuven/Praktikon bv; 2011.
  38. Barber BK. Parental psychological control: revisiting a neglected construct. Child Dev 1996 Dec;67(6):3296-3319. [Medline]
  39. de Brock A, Vermulst A, Gerris J, Abidin R. Nijmeegse Opvoeding Screenings Instrument Manual. Nijmegen: Universiteit Nijmegen; 1992.
  40. Johnston C, Mash EJ. A measure of parenting satisfaction and efficacy. J Clin Child Psychol 1989 Jun;18(2):167-175. [CrossRef]
  41. Lee C, Lee J, August G. Financial stress, parental depressive symptoms, parenting practices, and children's externalizing problem behaviors: underlying processes. Family Relat 2011;60(4):476-490. [CrossRef]
  42. Lever JP, Piñol NL, Uralde JH. Poverty, psychological resources and subjective well-being. Soc Indic Res 2005 Sep;73(3):375-408. [CrossRef]
  43. Carver C. You want to measure coping but your protocol's too long: consider the brief COPE. Int J Behav Med 1997;4(1):92-100. [CrossRef]
  44. Achenbach T. Manual for the Child Behavior Checklist. Burlington, VT: Department of Psychiatry, University of Vermont; 1991.
  45. Suldo SM, Shaunessy E, Thalji A, Michalowski J, Shaffer E. Sources of stress for students in high school college preparatory and general education programs: group differences and associations with adjustment. Adolescence 2009;44(176):925-948. [Medline]
  46. Armsden GC, Greenberg MT. The inventory of parent and peer attachment: individual differences and their relationship to psychological well-being in adolescence. J Youth Adolesc 1987 Oct;16(5):427-454. [CrossRef] [Medline]
  47. Hagenaars A, Vos KD. Poverty Statistics in the Late 1980s: Research Based on Micro-data. Luxembourg: Office for Official Publications of the European Communities; 1994.
  48. Goedemé T. How much confidence can we have in EU-SILC? Complex sample designs and the standard error of the Europe 2020 poverty indicators. Soc Indic Res 2011 Aug 19;110(1):89-110. [CrossRef]
  49. Conger RD, Conger KJ, Martin MJ. Socioeconomic status, family processes, and individual development. J Marriage Fam 2010 Jun;72(3):685-704 [FREE Full text] [CrossRef] [Medline]
  50. Falconier MK, Epstein NB. Relationship satisfaction in Argentinean couples under economic strain: gender differences in a dyadic stress model. J Soc Person Relation 2010 Sep 17;27(6):781-799. [CrossRef]
  51. Ponnet K, Wouters E, Mortelmans D, Pasteels I, De Backer C, Van Leeuwen K, et al. The influence of mothers' and fathers' parenting stress and depressive symptoms on own and partner's parent-child communication. Fam Process 2013 Jun;52(2):312-324. [CrossRef] [Medline]
  52. Barnett MA. Economic disadvantage in complex family systems: expansion of family stress models. Clin Child Fam Psychol Rev 2008 Sep;11(3):145-161. [CrossRef] [Medline]
  53. Ponnet K, Mortelmans D, Wouters E, Van Leeuwen K, Bastaits K, Pasteels I. Parenting stress and marital relationship as determinants of mothers' and fathers' parenting. Personal Relation 2013;20(2):259-276. [CrossRef]
  54. Adamsons K, Buehler C. Mothering versus fathering versus parenting: measurement equivalence in parenting measures. Parenting 2007 Jul 30;7(3):271-303. [CrossRef]

APIM: actor-partner interdependence model
EU-SILC: European Union Statistics on Income and Living Conditions
RMFC: relationship between mothers, fathers, and children

Edited by G Eysenbach; submitted 18.07.13; peer-reviewed by P Rosen, E Ennis, B Bunting; comments to author 28.11.13; accepted 10.12.13; published 02.01.14


©Koen Ponnet, Edwin Wouters. Originally published in JMIR Research Protocols (, 02.01.2014.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.