Perceived Family Resources Based on Number of Members With ADHD

Abstract

Objective:

This study examines how the number of family members with ADHD affects other family members’ perceived resources.

Method:

A total of 40 adolescents diagnosed with ADHD and their mothers, fathers, and adolescent siblings living in the household participated. Hierarchical linear modeling was used to analyze family-level data from a total of 130 participants.

Results:

Mothers reported more resources when only the target adolescent had ADHD and more nonsupportive factors when more than one member of the family had ADHD. Fathers reported more supportive factors when only one member of the family had ADHD.

Conclusion:

Parents reported greater resources and strengths when only one adolescent family member had ADHD; however, family members had varying viewpoints. The ADHD Family Scale examined issues specific to ADHD, compared with general family stress and resource scales, and may be a useful tool for examining the impact of ADHD on all members of a family.

Keywords

ADHD adolescent family resources strengths stress

Many studies examine the effects of medication and behavioral treatment for individuals with ADHD (e.g., Abramowitz, Eckstrand, O’Leary, & Dulcan, 1992; Conners et al., 2001; Pelham et al., 1993, 2002; Wells et al., 2000), but less research has investigated the effects of ADHD on the entire family systems and how all the members of the system react and adapt. The present study views ADHD within the contexts of individuals and their families and, to some extent, communities. A quantitative cross-sectional design was used. Multiple informants provided insights regarding increases and decreases in perceived resources when multiple family members had the disorder, with one being an adolescent with ADHD.

Effect of ADHD on Adolescents

In addition to the core deficits of ADHD (or possibly because of them), adolescents with this diagnosis often have behavioral, emotional, and interpersonal problems in their home, school, and work environments (DuPaul, Guevremont, & Barkley, 1991; DuPaul & Power, 2008; Litner, 2003; Quinn, 1997; Teeter, 1998; Whalen et al., 2006). Many adolescents with ADHD experience peer rejection and troubled relationships with siblings and parents (Litner, 2003; Mikami & Pfiffner, 2008; Mulsow, O’Neal, & Murry, 2001; Quinn, 1997; Teeter, 1998; Weiss, Hechtman, & Weiss, 1999). Thus, adolescents with a diagnosis of ADHD potentially experience problems in many aspects of their lives, which in turn affect others in their environments. The bidirectional nature of human interaction dictates that entire families are affected when one or more members have ADHD.

Effect of ADHD on Families

Blanchard, Gurka, and Blackman (2006) provided a summary of the emotional, developmental, and behavioral health of children and families in the United States, based on the 2003 National Survey of Children’s Health. This survey involved children aged 0 to 17 years from 102,353 families/households. Blanchard and associates concluded that families of children with persistent developmental problems, including ADHD, “are struggling in the areas of finances, employment, parent-child relationships, and caregiver burden” (p. 1210). The authors advocate not only addressing the child’s problem but also focusing on the impacts of these problems on families and communities.

Parents and siblings of adolescents with ADHD may feel frustrated, angry, ignored, embarrassed, and/or disregarded in response to the typical characteristics associated with ADHD, especially during adolescence. Parents often have more caretaking demands due to the child’s more frequent noncompliance (related to following through with parental instructions/directions). They often must resolve school, peer, and sibling conflicts that occur frequently throughout childhood and adolescence (Anastopoulos, Guevremont, Shelton, & DuPaul, 1992; Bussing et al., 2003; Mikami & Pfiffner, 2008; Pelham & Lang, 1999). Lee, Mulsow, and Reifman (2003) reported that families in which ADHD is present commonly have increased stress, fewer resources, and limited coping skills. In addition, a child with ADHD has a 64% chance of having at least one parent with ADHD or a history of the disorder (Evans, Vallano, & Pelham, 1994). This may only serve to exacerbate the previously mentioned problems during family interactions; however, few studies have examined this phenomenon beyond its frequency of occurrence.

Purpose of the Study

This study examined adolescents with ADHD and the families in which they belong. Parental perceptions of family strengths and available family resources as well as individual family members’ (adolescents with ADHD, mothers, fathers, and adolescent siblings) perceptions of positive and negative family characteristics and feelings relative to ADHD were investigated. ADHD literature indicates that parental and family stressors increase when one member has ADHD, compared with the control families, and that stressors and problems may be exacerbated and resources decreased when more than one member has the disorder (Lee et al., 2003; Splete, 2006). Thus, the following directional hypotheses were generated:

While statistically taking into consideration the effects of family socioeconomic status (SES) and target adolescents’ gender, parents in families with more than one member with ADHD will report fewer available resources and strengths compared with parents in families with only one adolescent with ADHD.

While statistically taking into consideration the effects of family SES and target adolescents’ gender, all individuals in families with more than one member with ADHD will report more negative feelings and nonsupportive factors and fewer positive feelings and supportive factors compared with individuals in families with only one adolescent with ADHD.

Method

Participants

A total of 130 individuals comprising 40 families participated in the study, including 40 target adolescents, 40 mothers, 28 fathers, and 22 adolescent siblings. Criteria for inclusion in the study were that the adolescent (target) participant had been diagnosed with ADHD (any type) by a physician or psychologist who used teacher and parent rating scales to assist in making the diagnosis and that the diagnosis had been made ≥1 year prior to this project. Participants did not have concomitant diagnoses of oppositional-defiant disorder (ODD) or conduct disorder (CD).

The family was defined as all individuals living together in a household on a regular basis. Family sizes ranged from two members (i.e., single-parent household: mother and target adolescent) to eight members (i.e., dual-parent household: father, mother, target adolescent, and five siblings). In 22 of the families, only the target adolescent had been diagnosed with ADHD. In 18 of the families, the target adolescent and one or more additional family members had been diagnosed with ADHD.

The target adolescents with ADHD ranged in age from 12 to 18 years (M = 14.3; SD = 1.92). Twenty-nine (72.5%) of the target adolescents were male, and 11 (27.5%) were female. Mothers ranged in age from 32 to 64 (M = 44.58; SD = 7.13). Fathers ranged in age from 32 to 73 (M = 46.07; SD = 8.23). Adolescent siblings ranged in age from 12 to 19 years (M = 15.27; SD = 2.16). There were 9 males (brothers) and 13 females (sisters). Family ethnicity, as reported by mothers and fathers, was as follows: In the 28 dual-parent households, 1 couple was African American, 5 couples were Latino, 20 couples were European American, 1 couple was Latino and Native American, and 1 couple was European American and Latino. In the 12 single-parent households, 2 mothers were African American, 1 mother was Latino, and 9 mothers were European American.

Measures

Family Index of Regenerativity and Adaptation–General (FIRA-G)

FIRA-G (McCubbin, 1987) was designed to obtain seven indices of family functioning, four of which involved family strengths that are as follows: Relative and Friend Support, Social Support, Family Coping and Coherence, and Family Hardiness. All items originated from the adult Family Inventory of Life Events (FILE; McCubbin, Patterson, & Wilson, 1983).

Family Inventory of Resources for Management (FIRM)

FIRM (McCubbin, Comeau, & Harkins, 1981) contains a total of 68 items with four scales, which are as follows: Family Strengths I: Esteem and Communication, Family Strengths II: Mastery and Health, Extended Family Social Support, and Financial Well-Being. The internal reliability (Cronbach’s α) for these four scales was reported as .89 (McCubbin & Thompson, 1991). Similar reliability was found for the present study (.95 for mothers and .87 for fathers). The total of the four-scaled scores provides a total FIRM score, with a higher score indicating greater overall resources. The FIRA-G and the FIRM were well-established measures that involved general family functioning, life events, and changes associated with stresses and strengths. Table 1 provides means and standard deviations for, and correlations (Pearson’s r) between, mothers and fathers who responded to these scales in this study.

Table 1.

Means and Standard Deviations for, and Pearson’s r Between, Fathers and Mothers on FIRM Total Score and FIRA-G Subcale Scores

Scale	M	SD	Correlation
FIRM total score			.693**
Fathers (n = 28)	109.53	19.07
Mothers (n = 40)	108.65	27.93
FIRA-G: Relative and friend support			.557**
Fathers (n = 28)	22.57	6.43
Mothers (n = 40)	24.23	7.29
FIRA-G: Social support index			.405*
Fathers (n = 28)	45.25	10.49
Mothers (n = 40)	46.77	11.83
FIRA-G: Family coping and coherence			−.075
Fathers (n = 28)	16.57	2.03
Mothers (n = 40)	16.98	1.90
FIRA-G: Hardiness			.390*
Fathers (n = 28)	44.36	9.16
Mothers (n = 40)	47.00	7.05

Note: FIRM = Family Inventory of Resources for Management; FIRA-G = Family Index of Regenerativity and Adaptation–General.

Correlation is significant at the .05 level (two-tailed).

Correlation is significant at the .01 level (two-tailed).

A review of the literature specific to ADHD and family dynamics (Bullard, 1997; Ducharme, 1997; Kendall, Hatton, Beckett, & Leo, 2003; Rice, 1996) revealed difficulties and strengths specifically related to ADHD that were not necessarily measured in the other scales. These topics were compiled into a 44-item scale deemed the ADHD Family Scale, which was created for use in this research study.

ADHD Family Scale

The first 26 items of the ADHD Family Scale were about family members. Participants were individually asked to rate these items based on the extent to which each characterized the family unit as a whole. Three items were about parents, and individual participants were asked to rate the items based on their beliefs and experiences regarding the parent(s) living in the household. Ten items were about the target adolescents. Again, each participant was asked to rate the items based on his or her own experience with the target adolescent (as self, parent, or sibling). Two items were about relatives and extended family members. Finally, three items were about teachers at the target adolescent’s school. A 4-point rating scale (1 = not at all, 2 = occasionally/sometimes, 3 = frequently/often, 4 = all the time) was used for all items. A “not applicable” (N/A) response was also possible for each item.

Correlations among family members’ ratings were moderate to high on several items (see Table 2). The independence of observation assumption was violated because the data were obtained from individual members within families; however, a factor analysis was performed in hopes of identifying factors relevant to the impact of ADHD on families. For the first 26 scale items describing the family as a unit, three factors were identified (see Table 3) as follows, based on a scree plot distribution using principal axis factoring and varimax rotation with Kaiser normalization: negative feelings among family members, positive feelings among family members, and concerned feelings among family members regarding ADHD medication. For the last 18 items describing individuals within and outside the family (parent[s], target adolescent, extended family relatives, and teachers), three factors were identified (see Table 4), based on scree plot distribution using maximum likelihood extraction and varimax with Kaiser normalization rotation: social and behavioral interactions between and within family members, nonsupportive internal and external factors, and supportive internal and external factors. The internal reliability (Cronbach’s α) ranged from .57 to .87 for the six subscales of the ADHD Family Scale. Items included in each subscale are provided in the online appendix. (Please see Supplementary Material online.) Values were calculated for each subscale using groups of individuals across families (fathers, mothers, targets, and siblings) as well as the entire data set (all members in all families). Complete reliability results are provided in Table 5.

Table 2.

Correlations Between Family Member Respondents for Items on the ADHD Family Scale

	Mother–father	Mother–target	Father–target	Mother–sibling	Father–sibling	Target–sibling
Item	n = 28	n = 40	n = 28	n = 22	n = 17	n = 22
1	0.526**	0.445**	0.412*	0.440	0.574*	0.114
2	0.090	0.408**	0.136	0.129	0.433	0.377
3	0.414*	0.268	0.309	0.580**	0.094	0.310
4	0.225	0.257	0.316	0.493*	0.457	0.238
5	−1.33	0.178	0.412*	0.213	0.533*	0.138
6	−0.064	0.052	−0.060	0.207	−0.064	−0.224
7	−0.083	−0.022	−0.323	0.306	0.453	−0.678**
8	0.499**	0.103	0.245	0.216	0.127	0.160
9	0.436*	−0.021	0.550**	0.571**	0.400	0.103
10	0.183	−0.064	0.266	−0.307	0.168	0.142
11	0.136	0.277	−0.067	0.385	0.111	0.232
12	0.036	−0.042	−0.100	−0.187	0.183	0.346
13	−0.125	0.017	0.126	−0.018	0.598*	0.489*
14	0.135	−0.127	0.103	0.280	0.491	−0.473*
15	0.029	0.131	0.293	−0.223	0.044	−0.438
16	0.425*	−0.014	0.014	0.336	0.000	0.203
17	0.089	0.341*	0.035	−0.481*	0.320	0.111
18	0.187	0.246	0.240	−0.075	0.510	0.247
19	0.206	0.119	−0.252	0.131	0.058	−0.129
20	0.087	−0.047	0.027	0.464*	0.044	0.105
21	0.211	0.369*	0.202	0.306	0.277	0.451*
22	0.582**	0.195	0.180	0.606**	0.000	0.515*
24	0.394*	0.287	0.130	0.063	0.308	0.196
25	−0.132	0.321*	−0.233	0.327	−0.658**	0.242
26	−0.200	0.279	−0.193	−0.181	0.078	−0.375
27	0.085	−0.003	−0.341	−0.038	0.207	0.040
28	−0.110	0.166	0.182	0.177	0.164	0.157
29	−0.092	0.306	−0.037	0.426	0.000	0.508
30	0.077	−0.028	0.233	0.194	0.714**	0.225
31	0.447*	0.282	0.115	0.638**	0.533*	0.425
32	−0.117	0.505**	−0.059	−0.089	0.784*	−0.067
33	0.267	0.222	0.266	0.372	0.341	0.063
34	0.318	0.251	0.259	0.016	0.264	−0.263
35	0.317	0.577**	0.564**	0.015	0.361	−0.068
36	0.509*	0.542**	0.338	−0.176	0.247	0.025
37	0.325	0.410**	0.158	0.227	0.264	0.254
38	0.465*	0.176	0.254	0.203	0.307	−0.235
39	0.614**	0.312	0.088	0.450*	0.326	0.118
40	0.259	0.145	0.271	0.233	0.011	0.058
41	0.517**	0.332*	0.350	0.503*	0.763**	0.776**
42	0.049	−0.101	0.199	−0.148	0.687**	−0.032
43	0.004	0.092	0.329	0.021	0.000	0.040
44	0.005	0.295	0.489*	0.358	0.509	0.249
45	0.507**	0.280	0.232	0.568**	0.602*	0.225

Correlation is significant at the .05 level (two-tailed).

Correlation is significant at the .01 level (two-tailed).

Table 3.

Factor Loadings, Communalities (h²), and Percentage of Variance for Maximum Likelihood Extraction and Varimax Rotation on ADHD Family Scale Items Pertaining to the Family as a Unit

Item	S₁	S₂	S₃	h²
Negative feelings
Family members have feelings of
Being ignored	.68	−.02	.10	.47
Anger	.66	−.14	.05	.46
Frustration	.64	−.05	.31	.50
Being criticized	.63	−.07	−.08	.41
Guilt or self-blame	.63	.22	.13	.46
Being misunderstood by others	.61	−.08	.12	.40
Social isolation	.56	.06	.27	.39
Incompetence	.53	.22	.14	.35
Worry	.52	.09	.29	.36
A tense home atmosphere	.50	−.33	.34	.47
Exhaustion	.43	.18	.30	.31
Depression	.42	−.39	.34	.44
Health problems	.39	.01	.06	.16
Helplessness	.38	−.09	.06	.15
Embarrassment	.36	−.03	.34	.24
Inadequacy	.35	.12	.05	.14
Difficulty communicating with teachers	.34	−.05	.25	.18
Positive feelings
Family members have feelings of
Family closeness	−.15	.78	.08	.64
Contentment	.04	.73	−.02	.53
Happiness	−.03	.68	.01	.46
The need to serve others	.21	.52	.20	.36
Confidence	−.11	.47	−.07	.24
The need to protect others	.25	.45	.12	.23
Feelings regarding ADHD medication
Family members have
Worry about whether medication should be taken	−.02	−.11	.96	.94
Concern about possible side effects of medication	.18	.19	.68	.54
Concern about accurate ADD/ADHD medication dosages	.24	.15	.53	.37
Percentage of variance	18.61	10.84	9.89

Note: S₁ = Subscale 1: Negative Feelings; S₂ = Subscale 2: Positive Feelings; S₃ = Subscale 3: Feelings Regarding ADHD Medication.

Table 4.

Factor Loadings, Communalities (h²), and Percentage of Variance for Maximum Likelihood Extraction and Varimax Rotation on ADHD Family Scale Items Pertaining to Individuals Within and Outside the Family

Item	S₄	S₅	S₆	h²
Negative social/behavioral interactions
Adolescents argue with parents	.87	.03	−.16	.47
Parents argue with children	.75	.05	.01	.56
Adolescents argue with sibling(s)	.67	.12	.07	.47
Adolescents are violent toward sibling(s)	.37	.25	.02	.20
Adolescents destroy property	.28	.20	−.15	.14
Adolescents require excess time and energy for homework	.23	.11	.11	.08
Parents differ in their discipline techniques	.22	−.03	.11	.06
Parents argue with each other	.20	−.079	−.07	.05
Nonsupportive internal and external factors
Teachers do not understand ADD/ADHD	.10	.84	.13	.73
Teachers do not believe that ADD/ADHD is real	.19	.70	.05	.53
Teachers create a positive learning environment at school	.01	−.58	.39	.49
Relatives do not understand ADD/ADHD	−.13	.43	.09	.21
Adolescents have difficulty making friends	.15	.35	−.16	.17
Adolescents have difficulty interacting with others	.31	.33	−.26	.27
Supportive internal and external factors
Adolescents have high activity levels	.01	.18	.67	.49
Adolescents are creative	.19	.15	.63	.45
Adolescents have positive experiences with teachers at school	−.11	−.23	.56	.38
Relatives are supportive of our family	−.01	−.15	.20	.06
Percentage of variance	13.06	12.33	8.65

Note: S₄ = Subscale 4: Negative Social/Behavioral Interactions; S₅ = Subscale 5: Nonsupportive Internal and External Factors; S₆ = Subscale 6: Supportive Internal and External Factors.

Table 5.

Internal Reliability (Cronbach’s α) for Each Subscale of the ADHD Family Scale

Group	S₁	S₂	S₃	S₄	S₅	S₆
Fathers	.84	.78	.89	.81	.62	.56
Mothers	.85	.75	.84	.41	.61	.55
Target adolescents	.90	.75	.77	.53	.69	.48
Siblings	.88	.88	.65	.77	.74	.75
All members in all families	.87	.78	.81	.68	.68	.57

Note: S₁ = Subscale 1: Negative Feelings; S₂ = Subscale 2: Positive Feelings; S₃ = Subscale 3: Feelings Regarding ADHD Medication; S₄ = Subscale 4: Negative Social/Behavioral Interactions; S₅ = Subscale 5: Nonsupportive Internal and External Factors; S₆ = Subscale 6: Supportive Internal and External Factors.

Procedures

Each target adolescent and each sibling between the ages of 12 and 19 completed the ADHD Family Scale. In addition to the ADHD Family Scale, each parent completed a demographic questionnaire as well as the FIRA-G and the FIRM. Family SES was coded as low, moderate, or high based on mothers’ and fathers’ reports of their own education level and employment status as well as family income. The number of family members with ADHD was coded as 1 or more than 1, based on mothers’ and fathers’ reports regarding whether anyone else in the family (including themselves) had been diagnosed with any type of ADHD.

Analyses

Adolescents with ADHD are nested within hierarchical social structures, including families, peer groups, schools, and communities. This research focused on adolescents as well as other individuals within the family. The nature of the data structure was multilevel because the data were collected from different individuals belonging to the same group (a family). Thus, hierarchical linear modeling (HLM) was used to evaluate the data regarding the hypotheses.

The HLM 6.02 software program (Raudenbush, Bryk, & Congdon, 2005) was used with restricted maximum likelihood estimation and robust standard errors. Because the independent observation assumption is typically violated with families (i.e., ratings from members within the same family are related because members are evaluating their shared environment), regular regression or analysis of variance methods for analyzing the data are not ideal or suitable. A multilevel modeling approach (HLM) simultaneously estimates within- and between-family variance, which assures that covariation estimates between outcomes of the same family will be corrected for measurement error (Raudenbush, Brennan, & Barnett, 1995). Data were collected from each individual within the family, but family-level variables were also included. To test Hypothesis 1, the HLM analyses involved two levels.

Level 1 was a within-family measurement model that proposed that the outcome score (family strengths) was the sum of a true score from each parent plus a measurement error. Scores were dummy coded, using 0 and 1. Each parent had a subscale score so that there were two outcome scores from dual-parent families and only one score from single-parent families. The model was written as follows:

Y_{i j} = β_{1 i j} X_{1 i j} (Father) + β_{2 i j} X_{2 i j} (Mother) + r_{i j},

where Y_ij was the dependent measure of interest (e.g., FIRA-G subscale score, FIRM score) for individual i within family j, with i = 1, 2 scores per family and j = 1, . . ., 40 families. Thus, there were 1 to 2 scores per family, depending on the number of parents living in the household. This outcome was represented as a function of individual scores, X_qij, plus a random error, r_ij. Stated another way, the outcome score for each parent was the sum of a true score plus a measurement error. Y₁ _j represented fathers’ scores and Y_2j represented mothers’ scores. The regression coefficients, also known as distributive effects β_qj, q = 1, 2, indicated how the outcome was distributed in family j as a function of the measured parental perceptions of family resources and strengths.

Level 2 was a between-family model in which the coefficients obtained from Level 1 were predicted by the number of family members with a diagnosis of ADHD, while statistically taking into consideration the target’s gender and family SES as well as a unique family effect, u_qj. The model was written as follows:

β_{1 j} = γ_{10} + γ_{11} (Target gender) + γ_{12} (Number of ADHD members) + γ_{13} (Family SES) + u_{1 j}

β_{2 j} = γ_{20} + γ_{21} (Target gender) + γ_{22} (Number of ADHD members) + γ_{23} (Family SES) + u_{2 j}

To test Hypothesis 2, the HLM analyses again involved two levels. Level 1 was a within-family model that proposed that each subscale score on the ADHD Family Scale was the sum of a true score from each family member plus a measurement error. Scores were again dummy coded, using 0 and 1. The model was written as follows:

Y_{i j} = β_{1 i j} X_{1 i j} (Father) + β_{2 i j} X_{2 i j} (Mother) + β_{3 i j} X_{3 i j} (Sibling) + β_{4 i j} X_{4 i j} (Target) + r_{i j},

where Y_ij was the subscale score for individual i within family j, with i = 1, . . ., 4 scores per family and j = 1, . . ., 40 families. Thus, there were 2 to 4 scores per family, depending on the number of members (adolescents and adults) within the family. This outcome was represented as a function of individual scores, X_qij, plus a random error, r_ij. The regression coefficients, also known as distributive effects β_qj, q = 1, . . ., 4 indicated how the outcome was distributed in family j as a function of the measured individual perceptions of positive and negative family feelings (1 = father, 2 = mother, 3 = sibling, 4 = target adolescent).

β_{1 j} = γ_{10} + γ_{11} (Target gender) + γ_{12} (Number of ADHD members) + γ_{13} (Family SES) + u_{1 j}

β_{2 j} = γ_{20} + γ_{21} (Target gender) + γ_{22} (Number of ADHD members) + γ_{23} (Family SES) + u_{2 j}

β_{3 j} = γ_{30} + γ_{31} (Target gender) + γ_{32} (Number of ADHD members) + γ_{33} (Family SES) + u_{3 j}

β_{4 j} = γ_{40} + γ_{41} (Target gender) + γ_{42} (Number of ADHD members) + γ_{43} (Family SES) + u_{4 j}

With all analyses, dichotomous variables (target gender and coded number of ADHD members in the family) were uncentered; whereas, coded family SES (low, moderate, or high) was grand centered. A separate HLM analysis was run for each measure used.

Results

Frequencies for the categorical demographic variables based on the full sample, single-parent (mother-only) households, and two-parent households are listed in Table 6. Results for each hypothesis will be discussed in the following paragraphs.

Table 6.

Percentages for Categorical Demographic Variables

	Full sample^a (%)	Single-parent household^b (%)	Two-parent household^c (%)
Targets
Males	72.5	58.3	78.6
Females	27.5	41.7	21.4
ADHD type
Inattentive	42.5	33.3	46.4
Hyperactive	15.0	16.7	14.3
Mixed	42.5	50.0	39.3
Siblings
Males	40.9	40.0	41.2
Females	59.1	60.0	58.8
ADHD type
Non-ADHD	59.1	60.0	58.9
Inattentive	18.2	—	23.5
Hyperactive	4.5	—	5.9
Mixed	18.2	40.0	11.7
Mothers
Education level
Completed 12th grade or less	20.0	16.7	21.4
Completed some college	35.0	50.0	28.6
Completed college or more	45.0	33.3	50.0
Employment status
Not working outside the home	27.5	33.3	25.0
Technician/aide	32.5	33.3	32.1
Professional career	40.0	33.3	42.9
Fathers
Education level
Completed 12th grade or less	—	—	25.0
Completed some college	—	—	32.1
Completed college or more	—	—	42.9
Employment status
Not working outside the home	14.3	—	14.3
Technician/aide	53.6	—	53.6
Professional career	32.1	—	32.1
Family annual income level (as reported by mothers)
US$0-US$40,000	42.5	66.7	32.1
US$41,000-US$80,000	35.0	25.0	39.3
>US$80,000	22.5	8.3	28.6
Family members with ADHD
Target adolescent only	55.0	58.3	53.6
More than 1 member with ADHD	45.0	41.7	46.4

n = 40 targets, 40 mothers, 28 fathers, 22 siblings.

n = 12 targets, 12 mothers, 5 siblings.

n = 28 targets, 28 mothers, 28 fathers, 17 siblings.

Hypothesis 1: Parents’ Perceptions of Family Resources and Strengths

Results from HLM analyses of parents’ perceptions of family resources and strengths are listed in Table 7 (FIRA-G subscales) and Table 8 (FIRM subscales and total score). The number of family members diagnosed with ADHD significantly (B = −14.358; p = .04) predicted the total FIRM score for mothers (see Figure 1): Mothers reported greater overall family resources when only one family member (i.e., the target adolescent) had ADHD compared with when two or more family members had it. Although not statistically significant (p > .05), results for fathers showed a relationship in the same direction.

Table 7.

Unstandardized Level 2 Coefficients for FIRA-G Subscale

	FIRA-G subscale
Source of report	Relative and friend support	Social support	Family coping and coherence	Family hardiness
Fixed effects (least squares estimates)
Mothers
Target gender	0.290	−3.499	0.356	−1.123
Number of ADHD Members	−1.008	0.067	0.470	−2.072
Family SES	2.241	7.418**	0.602	4.281**
Fathers
Target gender	−0.077	−2.723	−0.333	1.939
Number of ADHD Members	0.146	1.367	−0.203	2.453
Family SES	0.761	6.418	0.074	2.476

Note: FIRA-G = Family Index of Regenerativity and Adaptation–General; SES = socioeconomic status.

p < .01.

Table 8.

Unstandardized Level 2 Coefficients for FIRM

	FIRM subscale^a number
Source of report	1	2	3	4	FIRM total score
Mothers
Target gender	0.355	−6.371	−0.274	1.151	−5.139
Number of ADHD members	−2.973	−6.033	−1.009	−4.343	−14.358*
Family SES	4.966*	4.877	0.578	10.678*	21.100**
Fathers
Target gender	−0.041	−3.438	0.176	−4.159	−7.461
Number of ADHD members	−0.496	0.141	−0.868	−4.419	−5.641
Family SES	1.888	−4.473	−0.229	10.506*	7.692

Note: FIRM = Family Inventory of Resources for Management; SES = socioeconomic status.

Subscale titles: 1 = Family Strengths I: Esteem and Communication; 2 = Family Strengths II: Mastery and Health; 3 = Extended Family Social Support; 4 = Financial Well-Being.

p < .05. **p <.01.

Figure 1.

Mothers’ total firm scores based on whether one or more than one family member had ADHD

Hypothesis 2: Family Members’ Perceptions of Positive/Negative Feelings and Factors

Results from HLM analyses of all family members’ perceptions of positive and negative family feelings and supportive/nonsupportive family factors are listed in Table 9. On the ADHD Family Scale, the number of family members with ADHD was a significant (B = 2.253; p = .02) predictor for mothers on Subscale 5 (Nonsupportive Internal and External Factors). Refer to Figure 2 for a box-and-whisker plot. When more than one family member living in the household had ADHD, mothers reported more nonsupportive internal and external factors (e.g., teachers and relatives do not understand ADD/ADHD; adolescents have difficulty making friends.). Although not statistically significant (p > .05), the relationship was in the same direction for fathers and target adolescents.

Table 9.

Unstandardized Level 2 Coefficients for Subscales of ADHD Family Scale

	ADHD Family Scale
	Subscale^a
Source of report	1	2	3	4	5	6
Fixed effects (least squares estimates)
Mothers
Target gender	1.525	−1.029	−0.858	−1.436	0.278	−0.029
Number of ADHD members	1.604	−0.388	0.099	0.497	2.253*	−0.467
Family SES	−1.573	2.224**	−0.877	−0.278	−0.162	1.356**
Fathers
Target gender	0.486	−0.850	−1.928	0.131	0.096	−0.419
Number of ADHD members	0.238	−1.438	−1.466	−1.287	0.947	−1.899*
Family SES	0.269	2.355*	0.447	−0.513	0.321	1.325*
Targets
Target gender	4.397	−0.083	0.224	1.412	−1.141	0.676
Number of ADHD members	0.851	0.608	−0.474	0.695	0.332	0.917
Family SES	−2.360	0.875	−0.474	−0.909	0.822	0.335
Siblings
Target gender	−1.888	−1.964	−0.389	−0.378	−0.550	−1.874
Number of ADHD members	0.290	−3.088	−1.113	−1.982	−0.246	−1.370
Family SES	−0.451	1.902	0.889	−0.393	0.175	0.312

Note: SES = socioeconomic status. Subscale Titles: 1 = Negative Feelings; 2 = Positive Feelings; 3 = Feelings Regarding ADHD Medication; 4 = Negative Social and Behavioral Interactions; 5 = Nonsupportive Internal and External Factors; 6 = Supportive Internal and External Factors.

p < .05. **p < .01.

Figure 2.

Mothers’ scores on ADHD Family Scale Subscale 5 based on whether one or more than one family member had ADHD

The number of family members with ADHD was again a significant (B = −1.899; p = .02) predictor for fathers on Subscale 6 (Supportive Internal and External Factors). When only one family member (i.e., the target adolescent) had a diagnosis of ADHD, fathers reported higher ratings, indicating more supportive internal and external factors (e.g., adolescents are creative and have positive experiences with teachers at school; relatives are supportive of the family). Although not statistically significant (p > .05), the relationship was in the same direction for mothers and adolescent siblings.

Hypothesis 2 was partially supported. When only the target adolescent had ADHD, fathers reported more positive internal and external family factors. When more than one family member had ADHD, mothers reported more nonsupportive internal and external family factors. All these findings were consistent with the hypothesis that family members would report higher negative feelings and lower positive feelings when multiple members within the family had ADHD; however, the findings were not consistent across all members within families. There were no statistically significant results for target adolescents or sibling adolescents on any of the subscales.

Discussion

The present study is unique in that it examines perceptions of all family members (ages 12 and above) in families with an adolescent member who has been diagnosed with ADHD. The study investigates family resources as well as feelings and opinions regarding issues that have been frequently associated with families when ADHD is present. Overall findings reveal that the number of family members with ADHD significantly affects reports from parents of these target adolescents in terms of perceived family resources and feelings related to ADHD.

Mothers (but not fathers) report significantly more resources for management (e.g., family members being happy and cooperating with each other) when only the target adolescent has ADHD and fewer resources/strengths (e.g., family members being physically tired and emotionally stressed) when additional family members have the disorder. Other studies (e.g., Kashdan et al., 2004) have indicated that mothers bear more responsibility for child rearing and family cohesiveness than fathers. If a typical mother’s role involves nurturing family members and identifying resources and strengths for doing so, perhaps her capacity for this role is increased when only one adolescent member has ADHD and she as well as her spouse and other child(ren) do not have the disorder.

When more than one family member living in the household has ADHD, mothers report more nonsupportive internal and external factors. Fathers and target adolescents report similarly, although their results are not statistically significant. These nonsupportive factors are differentiated from general family stressors and seemed to be more closely related to ADHD. They involve the target adolescents’ difficulty with social interactions (making friends) as well as extended family members and teachers who do not believe that ADHD exists and/or do not understand ADHD. When two or more family members have ADHD, many members (especially mothers) seem to sense that the members with ADHD are not fully supported within their extended families and communities.

Fathers report more supportive internal and external factors when only the target adolescent (vs. multiple members) has ADHD. Although not statistically significant, mothers and adolescent siblings report similarly. When only the adolescent family member has a diagnosis of ADHD, other family members tended to recognize the adolescent’s high activity levels and creativity and reported that the adolescent had positive interactions with teachers at school. Perhaps when only one adolescent family member is diagnosed with ADHD, other family members are more apt to view some of the ADHD characteristics in a positive light.

Implications

By further understanding the ways in which adolescents with ADHD are developing within the context of their families, we may be better able to identify and address barriers, such as low SES as well as intrinsic individual- and family-level strengths, such as high energy levels, creativity, and positive feelings regarding family. The number of members within a family who are diagnosed as having ADHD does not seem to negatively impact the overall family functioning, which can be viewed as a family strength. This finding encourages us to examine what is going well in these families rather than focusing only on the negative family characteristics. The families who live with ADHD are somehow drawing on resources and typically succeeding on a daily basis. We have much to learn from them.

Further research involving siblings and fathers within families is certainly warranted. Although these individuals are important, mothers, seemingly for most families, continue to remain a focal point. It is crucial that we provide mothers with access to helpful resources so that they can, in turn, provide their family members with support and build on their own family’s strengths.

Limitations and Future Directions

The sample reflected some diversity in terms of ethnicity, family income, and parent education; however, because of the small sample size and the sampling procedures, generalization of results to the entire ADHD population was limited. Unfortunately, no information is available from families who chose not to participate in the present study. This study did not include its own measure(s) for determining or confirming a diagnosis of ADHD; rather, it relied on parental reports and diagnoses previously made by clinical psychologists, educational psychologists, and/or pediatricians. All those professionals arrived at the diagnosis after using a variety of measures and collecting information from several different people who interacted with the individual on a regular basis (e.g., parents and teachers). The investigators relied on personal reports from parents regarding whether other family members, in addition to the target adolescent, had been diagnosed with ADHD; therefore, certain family members may have been “missed.”

This study differentiated families based on whether only one adolescent member or multiple (two or more) members had a diagnosis of ADHD. It did not take into account which additional member(s) in the family had ADHD. For example, a father and adolescent daughter with ADHD might impact the family differently than a mother and adolescent son with the disorder. In addition, two parents without ADHD who had two children with the disorder might interact and respond quite differently compared with two parents with ADHD whose children also had the disorder. As shown in previous studies (e.g., Fletcher, Fischer, Barkley, & Smallish, 1996; Murray & Johnston, 2006), mothers with ADHD exhibited different perceptions, interactions, and struggles with their children compared to mothers without ADHD. Although no studies were found that specifically differentiated fathers or siblings with ADHD, the possibility that particular family members with or without the disorder may impact and influence interactions with other family members is a strong one. Ideally, this study would have involved a comparison group of families with adolescents who do not have a neurobehavioral diagnosis so that differences, if any, could be identified. Lack of a comparison group is a distinct limitation and should be included in future studies.

Because of the small sample size, target adolescents in this study were not differentiated based on their ADHD type or whether they were currently taking medication. As noted in other studies (e.g., Grizenko, Paci, & Joober, 2010), these variables may influence results, and larger, future studies should attempt to consider these differences. This study did provide information regarding the generic diagnosis of ADHD as it is applied to adolescents in today’s society and its effects on these individuals as well as their family members. It involved a cross-sectional family design and thus could not adequately explore how associations between family aspects and individual characteristics or problems develop over time; however, it was a step toward investigating developmental pathway influences. A longitudinal study would be ideal for more thoroughly investigating ADHD as a developmental process. In addition, although parents did not report comorbid diagnoses in family members such as mood and/or anxiety disorders, these diagnoses may have been present and, if so, could have altered the results.

The ADHD Family Scale may prove to be useful in future studies that examine the impact of ADHD on all members of a family. The subscales seem to examine different issues that are specifically relevant to ADHD compared with the more general family stress and resources scales. The ADHD Family Scale can, hopefully, be refined and used in the future to inform a developmental-contextual approach for examining ADHD within families.

Conclusion

This study demonstrated the importance of considering multiple family members when exploring the impact that ADHD has on the entire family system. As might be expected, different family members had different perceptions and perspectives. Because ADHD is a disorder that often spans across the lifetime, family resources and strengths should be identified for different developmental periods.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Funding

This research was not supported by grants but is based on data from Melinda Corwin’s doctoral dissertation.

Bios

Melinda Corwin is an associate professor in the Department of Speech, Language, and Hearing Sciences at Texas Tech University Health Sciences Center. She earned her MS in speech and hearing sciences (1989) and her PhD in human development and family studies from Texas Tech University (2006). She currently teaches courses in language disorders across the life span and neurogenic communication disorders. She is also a clinical instructor and provides speech-language therapy to children, adolescents, and adults with various diagnoses, including ADHD and language learning disability.

Miriam Mulsow is an associate professor in the Department of Human Development and Family Studies at Texas Tech University. She holds a PhD in child and family development from the University of Georgia. Her research involves family stress and resiliency, with particular emphasis on parenting stress, ADHD, and addictions. In addition, she performs and teaches program evaluation.

Du Feng is a professor in the Department of Human Development and Family Studies at Texas Tech University. She obtained her BS in psychology from Peking University (1991) and her MS (1995) and PhD (1996) in quantitative psychology from University of Southern California. She specializes in ordinal statistics and applications of longitudinal and multivariate data analytical techniques. She also studies and teaches courses in gerontology, family studies, and health disparity research. She is an author of more than 40 peer-reviewed publications between 1993 and the present.

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