Fam Proc 31:231-250, 1992

The California Family Health Project: I. Introduction and a Descriptionof Adult Health

LAWRENCE FISHER, Ph.D.a

DONALD C. RANSOM, Ph.D.a

HOWARD E. TERRY, M.A.a

MACK LIPKIN, JR., M.D.b

RICHARD WEISS, Ph.D.a

aCenter for the Study of the Family and Health, Department of Family and Community Medicine, University of California, SanFrancisco, Box 0900, San Francisco CA 94143.bDepartment of Medicine, New York University, New York NY.

Little research has addressed patterns of family and health relationships that reflect both the scope and complexity offamily life and the breadth and diversity of health. In the first of a series of articles, we describe the California FamilyHealth Project, a study in which four large "domains" of family life (Structure/Organization, World View, ProblemSolving, and Emotional Management) were mapped, described, and compared with a large battery of adult healthmeasures. We first present a brief critical overview of the literature on family and health research, then explain ourrationale, define our approach to the multivariate analysis of family and health data, and describe our sample of 225community-based families. To prepare for analyses with the family variables, we next present descriptive data based onseparate principal components analysis (PCA) and multidimensional scaling analysis (MDS) of 14 self-reported healthscores for husbands and for wives. No grouping or clustering of health variables emerged for either husbands or wivesin the PCAs. A two-dimensional MDS analysis for husbands and for wives displayed the health variables in a circularpattern in which no predominant descriptive dimension or group of discrete dimensions emerged. Consequently, wedecided that all 14 health scores will be used in the analyses, with the family variables to follow.

Fam Proc 31:231-250, 1992Family research provides a general framework for investigating the processes through which psychological, social, and

cultural factors influence the health and well-being of family members. Historically, research has focused on those aspectsof the family system, or those characteristics of the family as reported by a family member, that are significantly associatedwith the health of family members. This process has identified bits and pieces of family health, illness, or care relations, butno mapping or broad description of relationships has been accomplished. The problem has not been due to a lack of generaltheories. Research by Pearse and Crocker (1943), Richardson (1945), Angell (1936), Hill (1949), McCubbin andPatterson (1983), Patterson (1988), and Pratt (1976), together provide a diverse yet comprehensive set of ideas to guidefamily and health research. The difficulty seems to have come in defining comprehensive and manageable empirical studiesof a subject as complex as the family and health.

We identified three primary problems as we reviewed the clinical and empirical literatures. First, much of family andhealth research is narrow, or what we call "low dimensional"; that is, only one or perhaps two dimensions of familystructure, behavior, or family member appraisals are assessed. Consequently, it often seems that studies claiming to assessdifferent qualities of the family are really measuring the same construct using different instruments and different labels.Also, these studies do not suggest how several broad dimensions of the family are linked in their association with health.Finally, low-dimensional studies restrict the development of conceptually interesting models of family influences on healthin which conditional relationships are acknowledged. In contrast, multidimensional family studies have the potential toidentify the patterns and elaborate the pathways through which family life and the health of family members mutuallyinfluence each other.

Second, the range of health or illness criteria employed in family and health studies is also typically narrow and limited toone or two indicators. This would not be a problem if the field were able to differentiate among family patterns identifiedwith different disorders or health practices. At the current time, we have little certainty whether a finding that suggests areduced risk of asthma, for example, also suggests good control of diabetes.

Third, the vast majority of family and health research has been undertaken with families in which there is at least onemember with an identified health problem. Little work has been undertaken with community-based samples of familieswhose members for the most part are well, but whose health varies along multiple dimensions.

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In brief, we found that family and health research generally has relied on low-dimensional assessment, employed anarrow range of health criteria, and focused on families with specific health problems instead of families in general. Inresponse to these issues, we designed the California Family Health Project. Our approach joins ranks with several majorfamily research projects that have developed strategies for integrating many variables in assessments of complex familyprocesses, for example, Patterson (1982), Hauser and colleagues (1985), Hetherington, Cox, and Cox (1982), Cowan andCowan (1990).

We expand upon these studies by (1) building both a multidomain framework of the family and a multidimensionalassessment of health for each primary household member, and (2) choosing a community-based rather than a referred orclinically defined sample of families. Rather than contrast families with well and ill members on one or two familyvariables, we sought to explore how a relatively large number of family variables covaried with a relatively large number ofhealth scores for each family member. Our goal was to identify subsets of family and health variables that fell together indefinable and consistent patterns from which we might develop a better grasp of the complexity of family and healthrelationships. We hoped to identify empirically which groups of family variables fell together, which groups of healthvariables fell together, and the variability in patterns of relationships between family and health variables attributable togender and generation. We did not search for the "magic bullet" of family and health relationships, the mythical underlyingfamily construct or model that would "explain" family and health relationships. Rather, we sought to design a descriptivestudy, one that would map relationships between a broad but defined group of family variables and health variables. At theend of the study we wanted to have in hand a set of basic building blocks, or groups of family and health linkages that wecould then use to construct broader and more encompassing models of family and health transaction than are presentlyavailable. These goals led us to address various conceptual views of the family and alternative strategies for defining health.Further, it led us to consider several approaches for analyzing complex, multivariate family and health data, and to confrontour desire to simplify, or perhaps to oversimplify, the very complex relationships both within and between these twoconstructs.

This series of articles documents the problems we faced, outlines the course of action we decided upon, and reports thepatterns of family and health linkages we discovered. In this first article, we provide the background, rationale, and designof the research. In each of the several articles to follow, we report associations between a specific group of family variablesand a uniform set of adult health variables. In the sixth article in this series, we present analyses of family and healthrelations across several "domains" of family variables. In the last article, we summarize the patterns of family and healthrelations we found; we also review the relatively large number of defined subgroups of family variables that wereassociated with subgroups of health variables, staying true, we hope, to the complexity of the data.

THEORETICAL FRAMEWORKS FOR FAMILY AND HEALTH RESEARCHGiven our broad aims, we initially asked two questions. First, how can theory help us choose dimensions of family life

relevant to health from which specific family variables might be selected for study? Second, what family-related variableshave been consistently associated with health in past research?

Family Theory and HealthAs we read the literature, we find it useful to think about the family in terms of three categories of theory: global theories,

midrange theories, and dimensional approaches. Global theories refer to broad conceptual perspectives for observingfamily life (Burr, Hill, Nye, & Reiss, 1979). Examples frequently adopted in family and health research are general systemstheory, symbolic interaction theory, and developmental theory. These global theories offer languages for talking aboutfamily processes and provide frameworks for interpreting data. The problem we find with global theories is that, althoughtheir relevance is wide-ranging, they provide us with little aid in selecting specific variables for study or in constructingspecific hypotheses. Because they favor different points of emphasis for observation and explanation, however, we haveadopted these three as complementary lenses for viewing family-health relationships.

Although global theories provide a broad platform from which to observe the family and an abstract language fordescribing family processes, they give us little direction for selecting specific variables for study. We find that "midrange"and "dimensional" theories meet this need. Midrange theories describe how families work by focusing on limited butwell-defined domains of family structure, belief, problem solving, relations with the surrounding environment, and so on.Appreciating the immense complexity of family life, each midrange framework helps carve out a particular area of familyprocess without necessarily articulating its relationships with other areas. Several midrange family approaches have beenapplied successfully to health research. For example, Reiss (1981) and his colleagues developed an approach centering onfamily problem-solving styles and family members' shared constructions of the world; and Olson and colleagues developedthe Circumplex Model (1983, 1991).

We find that dimensional approaches seldom are derived from an overarching model of the family; rather, they areimportant because of their clinical and descriptive utility. For example, Moos and Moos (1981, 1990) employed ten

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dimensions in the Family Environment Scale; the McMaster Family Model (Epstein, Baldwin, & Bishop, 1983) includedseven family scales; and the Beavers-Timberlawn system (Beavers, 1982) contained 13 subscales.

Some work has been done to integrate midrange and dimensional approaches, for example, Fisher (1976, 1977),Barnhill (1979), and Bloom (1985). After reviewing the field, Walsh (1982) concluded that it is difficult to integrate whatwe are here calling midrange and dimensional approaches because (1) each theorist focuses on favored characteristics; (2)each interprets the literature of the others selectively; and (3) the models themselves, although internally coherent, do notprovide the operational mechanisms to assist in comparisons. She argued for more rigorous theory building and empiricaltesting, a goal that informs the rationale for the program of research described here.

Empirical ResearchThe second question behind our theoretical review concerns which aspects of the family have documented links with

health. We will not attempt to summarize the extensive literature here, but instead refer to reviews by Litman (1974), Pratt(1976), Schmidt (1978), Ransom (1981), Gillis (1989), and especially to Campbell's (1986) systematic and criticalassessment of the field. Based on these accounts and our own reading of the primary resources, we make three generalobservations that set the stage for our work.

First, several family variables have shown consistent relationships with measures of health or illness. Low familycohesion, high family conflict, too permeable or too rigid intra- and extrafamilial boundaries, low levels of familyorganization, and poor spousal support have received the most intensive study and usually are associated with physical andmental illness. Distant or hostile family affiliative tone, negative family member beliefs, criticality, and lack of clarity ofcommunication have likewise been associated with poor mental health. Several other family variables, such as patterns ofdecision making and specific role behaviors, also have displayed associations with health, but there has been insufficientresearch from which to draw general conclusions.

We find the evidence from the literature consistent, but not compelling. Because of the chicken and egg problem inherentin cross-sectional designs, the direction of influence often is unclear. The relative absence of longitudinal designs in familyand health research is a serious concern. Further, we find no evidence to demonstrate that particular family variables areassociated with specific mental or physical disorders, and there are disorders in which family variables have not beenimplicated empirically, despite intensive study.1

Second, as suggested earlier, we find the use of somewhat isolated family variables in health research problematicbecause (1) it imposes a relatively simplistic approach on what are very complex processes; (2) it ignores the problem ofpotential redundant measures; and (3) it precludes the search for alternative relationships between aspects of family andhealth. We found only a few programs of research that studied multiple family variables in relation to multiple healthindices for multiple family members (for example, Cowan & Cowan, 1991). Although no program of research, includingour own, can completely surmount these three problems, we found few projects that sought to address them directly.

Third, we find that few distinctions have been drawn regarding the role of the family in the etiology of disease, thereduction, maintenance, or recurrence of disease, and the enhancement of health (see Ransom, 1981). Our reading of theliterature suggested, however, that the role of the family in each of these relationships may vary considerably (Campbell,1986; Rolland, 1987). For example, qualities of the family that support good control of adolescent diabetes may be differentfrom qualities that support caregiving for the elderly, or good self-esteem and general well-being.

SELECTING FAMILY VARIABLESWith our global theoretical preferences and our awareness of the problems facing the field in the background, we turned

to midrange theories and dimensional frameworks to select family variables with documented associations to health/illnessfor inclusion in our research. The reviews of family and health research cited earlier, together with primary sources, wereexplored. We arrived at what we thought was a consensus among family investigators about the domains of family life thathave the greatest relevance to health, although different terms often were applied to similar dimensions by differentinvestigators. We established four criteria for including what we call a "domain of family variables" in our framework: (1) ithad to reflect apparent conceptual uniqueness; (2) it had to be widely described and clearly documented in the literature;(3) it had to display empirically documented links with various health indicators; and (4) it had to be assessable withlogistically feasible methods. Given these criteria, the following four family variable domains were identified, whicheventually included a total of 73 family variables.

World View (8 variables) refers to a spectrum of beliefs, values, sentiments, and expectations, some incorporated fromthe surrounding culture and some derived from the unique culture of each family. These constructs define the approach tolife supported by family members. Examples include views of God, personal determinism, the centrality of children in thefamily, and the sense of the world as inviting or frightening.

Emotion Management (36 variables) describes how emotion is expressed, acknowledged, and managed by the marital

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partners. Variables reflect the expression of sadness, love, and anger, and the ability of husbands and wives to tolerate suchemotions in each other's presence, along with the degree to which and how expressions of emotions are encouraged andresponded to.

Structure/Organization (13 variables) refers to those variables that reflect the style and pattern of family organization,the architecture of the family, and the structural frame within which the family operates. Structural components include rolerelationships, allocation of power, orderliness, cohesiveness, sharing, and other variables that provide the overarchingoutline of family operation.

Problem Solving (16 variables) reflects the ways in which the family conducts itself to resolve a shared problem.Variables include the family's effectiveness and style in solving the task, clarity of communication, flexibility, andcoordination, together with broader qualities such as leadership style and affiliative tone.

We employed the same criteria for the selection of specific family variables as we did for the selection of the variabledomains themselves: conceptual uniqueness, documentation in the literature, empirical links with health, and ease ofassessment. We do not propose that these family domains are mutually exclusive and/or exhaustive. Some variablescategorized in one domain might be viewed as more appropriately placed in another domain. Further, we do not claim thatthe four domains, and the respective variables within each domain, comprise the only selection possible. Rather, we usedthe literature and our clinical experience to establish a framework for building a comprehensive and practical familyassessment protocol that could be used for investigating meaningful linkages with health in a relatively large sample offamilies.2

METHODS

RecruitmentA sample of 225 families was recruited from a central California community of 500,000, with an urban core of 275,000,

using a random-digit dial telephone screening technique. Residential telephone number prefixes were combined with acomputer-generated list of random, four-digit suffixes. During a 5-minute anonymous interview, respondents were askedabout family composition, ethnic identification, and the current health status of all family members. Families were acceptedfor a home visit if the interview indicated (1) a heterosexual couple cohabiting in the home for at least 3 years; (2) at leastone adolescent between ages 13 and 18 residing in the home3; (3) Anglo or Hispanic ethnicity; (4) adults born in theUnited States or who had migrated prior to age 6; and (5) no family member having left home or returned during theprevious 3 months. Respectively, these criteria assured (1) some degree of family continuity, (2) relatively comparablefamily developmental levels, (3) restricted ethnicity, (4) significant time and education in the United States, and (5) relativestability of family membership. These restrictions were applied to permit variations in family structure and function toemerge, given the size of the sample, and to limit the effects of other factors attributable to ethnic, developmental, sexualpreference, and single-parenthood status.

Out of 30,187 telephone calls made, 13,289 households were contacted and 10,289 telephone interviews werecompleted (76.6%). A total of 746 eligible families were identified, of which 225 agreed to a home visit, representing a30.5% acceptance rate.

We reviewed two sets of data to explore the differences between families who accepted and families who refused toparticipate. First, acceptors and refusers were compared using data collected during the telephone screening interview. Nodifferences were found between these two groups in family size, social class, number and type of health problems, locationof residence in the County, ethnicity, or place of birth. The primary reason given by refusers was the time commitmentrequired and the difficulties in arranging family schedules. Second, no differences were found between acceptors' responsesto self-report scales and published normative data (for example, means and scale ranges). All family and health scalesdisplayed good score distributions that did not suggest a restricted or selective sample. In addition, discussions withacceptors suggested that the $100 participation fee paid to families at the completion of the protocol did not influence theirdecision to participate.

The reliability of anonymous telephone-screening data, however, is uncertain, and some families may have chosen not toparticipate because of other life stresses or because of a particular family characteristic. For example, we suspect thatchaotic families, families that were suspicious of outsiders, and families whose members were emotionally detached, wouldnot be likely to participate. Further, other research has suggested that time-consuming, assessment-intensive studies usingcommunity-based samples often face reduced rates of acceptance because subjects cannot commit to the demanding timerequirements (Matarazzo, Conner, Fey, et al., 1981). Considering all of these issues, we conclude that the generalizabilityof the data is limited by (1) the previously described inclusion and exclusion criteria, (2) the importance of "the family" inmembers' lives, (3) a willingness and ability to make a significant time commitment to the project, and (4) a willingness toreveal somewhat personal matters about family life to "outsiders."

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SampleCharacteristics of the sample are presented in Table 1. This is a sample of families with two heterosexual adults (not

necessarily dual biological parents); the average length of marriage was 17 years; about one-third of the spouses had beenmarried previously; average family income was $44,350; and about 75% of adult women worked at least part-time outsidethe home. All but two couples were legally married. Thirty-three families identified themselves as Hispanic.

Table 1Sample Characteristics (N = 225)

FAMILIES

Household size: Mean = 4.22 (SD = 1.09)

Ethnicity: Anglo = 192 Hispanic = 33

All offspring: N = 468 (N/family = 2.08)

Males = 261 (1.16)

Females = 207 (.92)

Adolescents: N = 278 (1.23)

Males = 141 (.62)

Females = 137 (.60)

Family constellations

Blended families: N = 41 (18.2%)

(children from previous marriages in home)

Adult heterosexual couple plus N

their biological offspring only: 161

their offspring + mother's offspring: 12

their offspring + father's offspring: 3

their offspring + mo.'s & fa.'s offspring: 2

mother's offspring only: 17

father's offspring only: 1

mother's and father's offspring: 2

their offspring + adopted children: 5

mother's and father's adopted children: 7

mother's or father's adopted children: 2

offspring + relatives and other adults: 13

ADULTS Mean SD

Length of present marriage (years): 17.37 7.16

Number of previous marriages:

Females .35 .48

Males .24 .33

Social class:*

Females 2.27 1.11

Males 2.09 .97

Family 2.67 1.15

Age:

Females 40.72 5.56

Males 43.05 6.67

Years of schooling:

Females 11.52 1.86

Males 12.37 2.01

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Family Income ($) reported by males: 45,500 20,900

reported by females: 43,200 13,780

Income by gender Males Females

10,000 or less (incl. unemployed) 7 119

11,000 - 20,000 45 60

21,000 - 30,000 64 31

31,500 - 40,000 50 10

41,000 - 50,000 29 1

51,000 - 24 0

missing data 6 4* Social class (Hollingshead & Redlich, I to V) calculated separately from data supplied by males and females, instead of "head of

household."

ProcedureFollowing the telephone screening, a letter of introduction was sent to eligible families along with a packet of letters of

support from local schools, churches, and health care agencies. A followup phone call then was made to schedule a homevisit, with all family members present, to explain the project, ask for their participation, and obtain informed consent.Direct recruitment was made in the home, rather than by telephone, to assure access to all family members. Familymembers were told that the goal of the project was "to learn more about family life and how aspects of the family wererelated to the health of its members." All families accepting a home visit agreed to participate, and no family dropped out ofthe protocol once acceptance was given. Members completed several self-report forms and the RAND HealthQuestionnaire (Ware, Brook, Davies-Avery, et al., 1984) was left for completion at home prior to the first laboratorysession, scheduled one week later. All individuals residing within the home attended the first laboratory session(approximately 3 hours) during which several self-report scales were completed. Also, the family participated in SIMFAMII, a 30-minute, video-taped family problem-solving game, adapted from the original procedure developed by Straus (1968)and modified by Straus and Tallman (1971). One week later, the adult couple returned to the laboratory for approximately 3hours to complete a final set of self-report forms. They also participated in three, 10-minute video-taped EmotionManagement Interaction Tasks (EMIT) designed to elicit themes of loss, intimacy, and conflict resolution. (The SIMFAMII and EMIT procedures are described in detail in later articles in this series.) Upon completion of the protocol, a check for$100 was sent to each family.

Health MeasuresHealth is generally described in the literature as a multidimensional construct that includes physical health, mental

health, well-being, role functioning, functional deficit, and so on (Ware et al., 1984). Three questions arose as we preparedto select measures to assess the health of this community-based sample. First, we asked what meaning could be ascribed toextreme scores on health scales when administered to a nonclinical sample. For example, does a score of 9 on a 10-pointscale of depression mean the same thing when the respondent is "randomly" selected from the community as it does whenthe respondent is selected from patients with a clinical diagnosis of depression? We reasoned that, although the scores areidentical, differences in context imply differences in meaning. Although a high score on a depression scale for acommunity-based respondent reflects a substantially negative feeling state, we could not assume a diagnosis of depressionbased on this score alone. This index is only a single measure of mood assessed outside a clinical context.

Second, we asked how many facets of health should be included in our battery, and how intensively these should beassessed. Third, given the nature and size of our sample, we wondered what method of assessment was the best choice:health diaries, self-reports, physical examinations, frequency of doctors visits, medical record reviews, and so on.Following the work of Ware (1986), we reasoned that the frequency of any single health problem or functional deficitwould be relatively low in a community-based sample of this age range. Further, we had no a priori reason to suspect thatcharacteristics of family life would be associated with a particular aspect of health, which would mandate an intensiveassessment of one or two components. Consequently, we decided to assess the health/illness continuum broadly in order toincrease the probability of detecting variations in family functioning across patterns of health and well-being scores. Wemade no effort to undertake an intensive assessment in any one area, but instead we sampled the most frequently describedfoci of health. Ware and colleagues (1984) employed this approach in a large health insurance study with considerablesuccess; and we chose to use his extensive RAND Health Assessment Questionnaire, supplemented by other instruments.In total, 14 "health scales" were employed (see Table 2 for scale descriptions, sample items, and internal consistency

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coefficients). Because of the size and diversity of the sample, the relatively high assessment costs, the probable smallvariation in scores, and the logistical difficulties involved, we did not employ physician examinations, medical chartreviews, or laboratory studies.

Table 2Adult Health Variables

Scale No. Items Description & Sample Items Lambda Max.a

Anxiety 10 Amount of anxiety experienced in the last month: .892

1. How much of the time do you feel relaxed and free of tension?

2. During the last month, how much of the time have you felttense or "high-strung?"

Mistrust of Physicians 10 Belief in helpfulness of physicians for health care: .685

1. Doctors almost always help you feel better.

2. If you wait long enough, you can get over any illness withoutseeing a doctor.

Depression 11 Amount of depression experienced during the last month: .875

1. How much of the time during the past month have you feltdownhearted and blue?

2. How often during the past month have you felt that nothingturned out the way you wanted?

Weight Problems 1* Attention given to gaining or losing weight: NA

1. Do you, a doctor, or nurse think you weigh too much or toolittle?

General Well-being 11 General life satisfaction and emotional well-being during the lastmonth:

.901

1. During the last month, how much of the time have yougenerally enjoyed the things you do?

2. How much of the time during the last month have you feltemotionally stable?

Health Evaluation 3 Global evaluation of health status, functional limitations, andconcerns about health:

.759

1. During the past three months, how much has your healthworried or concerned you?

2. In general, would you say your health is excellent, good, fair, orpoor?

Health Perceptions 16 Evaluation of past, current, and future health and attitude aboutillness:

.867

1. I will probably be sick a lot in the future.

2. My body seems to resist illness well.

Preventive Behaviors 7 Frequency of visits to dentist and physician, and for health-caremaintenance:

.528

1. Have you recently had your eyesight tested by a doctor?

2. When have you had a blood cholesterol test?

Problem Drinking 5 Concerns, problems, and limitations caused by drinking: .640

1. Has a doctor ever told you to stop or cut down on yourdrinking?

2. Has your drinking ever worried or concerned you?

Self-esteem 10 Rosenberg Self-Esteem Scale: .847

1. I feel that I am worth as much as other people.

2. I wish that I could have more respect for myself.

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Smoking 1* Amount and duration of smoking if smoking now NA

Social Relationships 1* A derived score reflecting the number of contacts per week withpeople "with whom you feel close"

NA

Somatic Symptoms 13 Number of somatic symptoms occurring during the last month: .624

1. Backaches or sciatica

2. Acid indigestion or heartburn after many meals

WorkSatisfaction/Productiveness b

1* An index of amount of time at work, subjective rating ofperformance, and satisfaction with work

NA

a Lambda Max. refers to the greatest of the six Guttman coefficients; a coefficient of .50 is considered acceptable.b Ratings of work performance and satisfaction were weighted by the percent time at work. Ratings of housework also were weighted

by percent time to reach 100%.* Scales with an asterisk are single-item indicators composed of 5 or more contingent items.

The RAND Health Assessment Questionnaire assesses previous health, functional capacity, and illness, as well ascurrent health and expectations for future health. From the subscales available, we selected Anxiety, Depression, GeneralWell-Being, Health Perceptions, and Somatic Symptoms for use in the study reported here. To establish the psychometriccharacteristics of these scales with our sample, each RAND scale underwent a principal components analysis (PCA) and anassessment of reliability for men and women separately. PCA was used to confirm the presence of a prepotent generalfactor for each scale and Guttman (1954) lambda coefficients, rather than alpha, were employed to better reflect a greaterlower boundary for internal consistency. In some cases, one or more of the original items were dropped if they failed tomeet the PCA and analytic inclusion criteria.4

Six additional scales were constructed using selected RAND items: Mistrust of Physicians, Weight Problems, HealthEvaluation, Preventive Behavior, Problem Drinking, and Smoking. An item pool was selected for each scale by a team ofprimary care physicians; PCA and reliability analyses were undertaken for men and women separately.5

Also included were the Rosenberg Self-Esteem Scale (1965), and a measure of the quality and frequency of SocialRelations (Fischer & Phillips, 1982). Finally, from a brief scale of current work activities developed for this research, anindex of Work Satisfaction and Productiveness was derived.

Strategy of Data AnalysisIn this section, we outline the uniform sequence of analyses undertaken between each of the four domains of family

variables and the set of 14 health variables. We first selected a method of assessment for each family domain that wethought would provide a clear and appropriate window of observation, and would be logistically feasible, given the size ofthe sample. The domains of family Structure/Organization and World View, and member Health were assessed byself-report scales; the domain of family Problem Solving was assessed by ratings of family interaction; and the domain offamily Emotion Management was assessed by ratings of adult couple interaction. In the data analysis plan outlined below,each position of observation or type of data was considered unique, and no attempt was made to combine data acrossmethods or to scale data into an "index" that "represented" the whole family.6 We will discuss the role of method ofassessment in the sixth and seventh articles in this series.

Examining Family and Health DataOur plans called for heavily sampling each family and health construct with multiple scales and measures (Fisher, Terry,

& Ransom, 1990). This led us to consider the appropriate use of data reduction techniques in family and health research.We were concerned that the automatic and exclusive use of procedures such as principal components analysis might lead toa poor representation of the data set when it was analyzed as a whole. We considered that data reduction could beparticularly problematic when the goal of our research was to explore patterns of family and health relationships. When agroup of scales is reduced to a smaller number of dimensions, data-reduction statistical procedures will almost alwaysprovide a "solution." Without an analysis of both the metric and nonmetric characteristics of the variables, however, webelieve that the reductive process itself may defeat the purpose that standard multivariate techniques were designed toachieve in the first place.

We therefore decided to include both principal components analysis (PCA) and multidimensional scaling analysis(MDS) as complementary procedures for viewing variable groups and justifying the inclusion or exclusion of reduced or"dimensionalized" variable sets. MDS displays both the nonmetric linear and nonlinear (monotone) aspects of scoreconfigurations geometrically as points in N-dimensional space. Graphically, the distances between pairs of pointscorrespond to the magnitude (rank order) of the association between them; thus, the higher the association between twovariables, the closer they appear in graphic representation (Canter, 1985; Kruskal & Wish, 1978). As we shall see, the use

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of both procedures sometimes argues for including all the variables in a variable domain in subsequent analysis, whereas inother cases it suggests that a reductive approach is sensible.

The next issue we faced was deciding what techniques to use to evaluate patterns of family and health relationships,given the number of family and health variables we chose to assess. The most common strategy we found is to reduce thesets of family and health variables independently of each other and then to associate the two reduced sets of variables. Thisstrategy stems from a traditional model of quantitative analysis in which the "independent" and "dependent" variable groupsare reduced and fixed separately, prior to hypothesis testing. We find it difficult, however, to assign the terms "independent"and "dependent" to groups of family and health variables because we have no clear rationale for causal ordering amongthese variables. That is, we assume that mutual and reciprocal effects are common: families influence health and healthinfluences families.

Huberty and Morris (1989) suggest an alternative: to examine the two sets of variables jointly and simultaneously. Theirpresentation of three underlying reasons for conducting multivariate analysis in the first place formed the basis for our dataanalytic design. The first reason addresses the goal of variable selection: to identify one or more subsets of variables withineach of the two variable groups that are related and other subsets that are not. The second addresses the goal of variableordering: to determine if some variables in one variable set are more highly associated with variables in the secondvariable set than are others, and to determine if this ordering of association is similar or dissimilar for different subgroups ofsubjects (husbands vs. wives, adults vs. children). The third goal addresses the discovery of system interdependence, one ofspecific interest to family systems researchers: to identify a construct that underlies both the independent and dependent(family and health) variables. After considering Huberty and Morris' discussion closely, we concluded that all three goalswere central to our study. Therefore, we sought a statistical procedure that would permit this kind of simultaneous analysisof family and health variables.7

Following this line of reasoning, we chose canonical correlation analysis (CA) to compare each domain of familyvariables with health (Levine, 1977; Marascuilo & Levin, 1983; McLaughlin & Otto, 1981; Thompson, 1984). Thisprocedure simultaneously fits a set of weights to the variables in each group so that the resulting correlation between themis maximized. The resulting composites are called "canonical variates," and the correlation between them reflectsone part ofthe relationship between the two groups of variables. Using the remaining variance, the procedure then selects a second setof weights to maximize the association between a second pair of canonical variates. Each successive pair of canonicalvariates is uncorrelated with previous pairs. Several pairs of canonical variates can be created by the procedure. Anoverview of the relationships between each variable in each variable group and each canonical variate shows its"importance" in the association and provides a basis for observing patterns of family and health associations.

Because the assignment of weights is based on the variables in both sets taken together, the pattern of scores in the healthdomain will vary, depending on which domain of family variables it is correlated with. We find this characteristic ofcanonical correlation useful because it permits the pattern of health scores to fluctuate in relation to different familydomains.

Interpretation of patterns of association using canonical correlation can be problematic. Some researchers (McLaughlin& Otto, 1981) have suggested a review of the matrix of weights that represents the relative contribution of each variable inthe prediction of their respective canonical variate, similar to beta weights used in multiple regression analysis. Variableweights, however, can be unstable and are subject to multicolinearity and enhancer and suppresser effects. Others (Levine,1977; Meredith, 1964) have suggested reviewing the structure matrix, the matrix of correlations between the originalvariables and their respective canonical variates. This matrix reflects the direct association of each variable with thecanonical variate (like the loading of a variable on a factor), and it does not suffer from the limitations of the weight matrix.But because of the number of correlations involved, the likelihood of an isolated high correlation due to chance isincreased. A third approach is to review what we call the two "complementary" structure matrices. Each is a matrix ofcorrelations between a variable and the canonical variate derived from the other set of variables. For example, thecomplementary structure matrices for the family Structure/Organization and health analysis would consist of correlationsbetween (1) each family Structure/Organization variable and the health canonical variate, and (2) the correlations betweeneach health variable and the family Structure/ Organization canonical variate. Again, because these matrices are composedof a large number of correlations, isolated high correlations due to chance are possible.

Each of these three approaches has its strengths and drawbacks. We reasoned that a truly conservative approach calledfor a review of all three matrices. To facilitate presentation, we provide the structure matrix for each analysis in the reportsto follow, with codes to indicate the "importance" of the variable in the other two matrices. In the Tables presented in eacharticle, an underlined variable indicates a weight of .40 or greater in the weight matrix, and an asterisk indicates acorrelation of .25 or greater in the "complementary" structure matrix. By and large, a variable with a structure matrixcorrelation of .40 or greater also meets criteria for the other two matrices as well. This, in part, reflects the stability(non-chance) of the findings.8

Each canonical correlation analysis was undertaken in two ways for the two domains assessed by self-report methods:

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family World View and family Structure/ Organization. First, self-reports of family were correlated with self-reports ofhealth for each spouse. Second, we also wanted to create a "relational level" (couple) index comprised of self-report scores(Fisher, Kokes, Ransom, et al., 1985; Ransom, Fisher, Phillips, et al., 1990). This score represents a unit index based oneach spouse's appraisal, providing a second vantage point in the analysis of self-report family variables and spouse health.To construct this score, we entered each set of husband and wife self-report scores into an inter-battery factor analysis(Cudek, 1982; Tucker, 1973). This procedure extracts joint descriptors or factors between the separate "batteries" orgroups of scores. (In this case, a "battery" is the set of husband or wife self-report scores.) Because inter-battery factoranalysis is a correlational procedure, husband and wife scores that covary (positively or negatively) together will tend toemerge within a single factor attributable to a couple. Each factor demonstrates the salience of the self-report variable forthe couples in the sample. The derived "couple" factors then were entered into separate canonical correlation analyses forassociation with each spouse's health data.9

The data analytic strategy, then, consisted of both descriptive and associational techniques. In the descriptive portion, wewill report analyses of each of the four family domains and the health domain separately, using both PCA and MDS. Eachscale also was correlated with a standard set of 20 personal and family demographic variables (for example, social class,years married, household size, blended family status, ethnicity, age). In the associational portion, we will report analysesbetween each family domain and health using CA. The complete sequence of analysis is reflected by the order ofpresentation in each report, and is outlined in Figure 1.

Figure 1.Outline of data analysis.

RESULTS

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Below, we present correlations between the health and demographic variables, followed by the PCA and MDS resultsfor the 14 health variables.10

Demographic DataCorrelations were run between the 14 health scores and 20 demographic variables by gender. Of the 420 correlations,

only 11 were greater than .20, and all were below .30. No pattern among these correlations was apparent, and, consideringthe number of correlations run, a decision was made not to include controls for demographic variables in subsequentanalyses with the health variables.11

Principal Components AnalysesThe 14 health scales underwent a PCA by gender to explore the possibility of reducing the number of scores while

maintaining the variance explained (zero-order correlation matrices are available from the first author). The PCAs for bothhusbands and wives did not account for enough of the observed variance without including several factors composed ofonly one or two health scores. In most cases, the doublets did not have a significant pairwise correlation, nor did theydemonstrate a parallel pattern of association with the other variables. We concluded that most of the components composedof single scores or doublets were algebraic rotational artifacts, and that scale reduction using PCA was not sufficientlymeaningful to proceed with this approach.

Multidimensional Scaling AnalysesThe 14 health scores next were submitted to a standard nonmetric MDS procedure by gender. A two-dimensional

solution was selected both for husbands and for wives (see Figure 2), based on the geometric organization of the scores andthe reproduced R2 (husbands: R2 = .83, stress = .18; wives: R2 = .83, stress = .17).12 Each solution depicted a roughlycircular shape. The location of points suggested that the health domain was reasonably well sampled by the 14 scores, butsome gaps in the circle occurred. Health indices next to one another were more highly correlated than those that werefarther apart, and the variables opposite to one another on the grid ranged from neutral to varying degrees of an inverserelationship. This explained, in part, why the outcome of the PCA was not a good representation of the organization amongthe health scores. For a good PCA representation to occur, we would have expected distinct subgroupings of variables onthe MDS grid.

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Figure 2.MDS configuration for husband and wife health data.

The MDS displays for husbands and wives were very similar. The major difference was in the location of only twovariables, Mistrust of Physicians and Work Satisfaction/Productiveness, and these were based on only modest differences intheir zero-order correlations.

Overall, the MDS display indicated that health, as reflected by these variables in this sample, was not represented by (1)a single, prepotent general factor that reflected a "health-illness" dimension; (2) a group of indices that were part of aprepotent general factor, as is found in research on intelligence; or (3) a series of relatively uncorrelated clusters of scores.Although there were local clusters of scores within regions on the grid, they appeared somewhat arbitrary; that is, no clearmental or physical health partitioning emerged. Instead, we found health to be a complex construct, represented in our databy a profile of variables with no special status for any scale. We therefore decided to include all 14 health scores in theanalyses of the four domains of family variables.

The lack of simple structure among the health variables was surprising, given the volume of literature on components ofhealth and illness. Upon reflection, however, we were reminded that we made three initial decisions that contributed to alack of simple structure. First, when groups of health scores occur in data reduction procedures, they tend to arrangethemselves differently for ill and well samples. For example, for patients currently suffering from severe physical illnesses,anxiety, depression, and somatic symptoms may be highly intercorrelated and grouped into a single component. No suchrelationship may be present in randomly selected community samples, and we might expect these same scales to displaylow to moderate intercorrelations. In this sense, the disorder, or the subject's state of health, strongly influences thearrangement of scores.

The second and third strategic decisions we made refer to the number and type of health scores we selected for our healthassessment battery. We chose to use a relatively large number of variables from across the health spectrum withoutassessing any one area intensively. Considering a correlation-based procedure such as PCA, it follows that the larger thesize of the battery and the fewer the number of scales selected to assess any one aspect or area of health, the lower thechances of obtaining well-defined components or factors. In this sense, the PCA and MDS results reflected our strategy ofhealth assessment and reinforced our ability to observe patterns of health scores in the battery.13

CONCLUSIONIn this article, we have presented a rationale for our approach to the questions addressed in this program of research, a

description of the sample and its recruitment, a review of our approach to both descriptive and associational multivariatedata analysis with this data set, and a presentation of the descriptive data for the set of 14 adult health variables.

In the two articles that follow in this issue of Family Process, we present both descriptive and associative datacomparing two of the four family domainsfamily World View and Emotion Managementwith adult health. In tworeports in a subsequent issue, we compare the remaining two family domainsfamily Structure/Organization and familyProblem Solvingwith adult health. Our goals are to map the terrain of each of these four family domains with relativelylarge numbers of variables, to describe the patterns of their association with health, and to observe differences in family andhealth associations by gender, keeping in mind the three purposes of multivariate research suggested by Huberty andMorris (1989).

In the sixth article, presented in a third issue, we report the changes that occur in the configuration of family variables foreach domain when that domain is first viewed as a separate subsystem of family variables, and when it is then viewed, incontrast, in the context of the other three domains. This analysis suggests how the shape of the four family domains changesas a function of family context. Also in the sixth article, extending this analytic strategy, we report changes that occur in theconfiguration of family and health variables for each domain with health when each domain is viewed separately from theothers, and when the variables from all four family domains are then combined and viewed simultaneously with health. Thisanalysis reveals how the larger family context, as represented by four integrated domains, alters the relationships betweensubsystems of family variables and health. A preliminary family model, based on these analyses, is described. In the seventharticle, we summarize the results of this series of reports, review the limitations of the data, and discuss the implications fortheory and practice.

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Manuscript received September 10, 1990; Revisions submitted August 14, 1991; Accepted April 27, 1992.

1Related to this observation, an important question for the field is whether further study will support a general "good/bad"dichotomy in which a few family qualities or areas of functioning predict a wide variety of health and illness indices, or whether theissue is more complex than this, and specific family patterns or, perhaps, family types will be needed to account for certain illnessesand disorders or dimensions of health.

2We see our task as identifying interdependent subsets of family and adult health variables from which family and healthrelations can be modeled empirically (see Reiss, 1991). This primarily configurational (descriptive) approach poses some risk ofobtaining chance and sample-specific findings, considering the number of family and health variables assessed. We view disparities,ambiguities, unexpected results, and post hoc interpretations with caution, and urge the use of hypothesis-driven studies forclarification and expansion of the patterns of relationships described in this research.

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3In no instance was it necessary to exclude a family because a child was attending a residential school.

4Criteria included: nontrivial loading for each item on the first factor; item correlates highly with total test score with that itemexcluded; and item contributes to the internal consistency of the scale. A complete list of items, loadings, etc. for all scales isavailable from the first author.

5Because the entire 350-item questionnaire was administered to each family member over age 12 (n = 733), we were also able tocompute the frequency and type of functional limitations or deficits, current and previously diagnosed acute and chronic disease,medications taken regularly, and frequency of health care visits. As expected, the variance of each of these scores was so low thatthey were not included in the final battery of health variables.

6Although the family can be defined in several ways, we assume that it can never be directly measured. The chief problem is thatthe family is a construct, both multidimensional and multileveled. We further propose that the multiple subunits of the family (forexample, person, couple) are not hierarchical levels in the sense that one subserves or is superordinate in relation to another.Instead, we view person and family, or family appraisals and family behavior, as different types of data in the logical sense.Consequently, these classes of data are neither added together nor substituted for one another in the analyses to follow. Suchmeasures may interact with each other in the statistical sense, but we prefer not to collapse them as falling along a single dimensioncalled "the family."

7Huberty and Morris (1989) cautioned that the results of analyses that address all three questions are to some degree sample- andsystem-specific, and that, as new subjects or variables are added, the salience and order of a given variable may change.

8As in any multivariate research, we were concerned about the possibility of interactions and nonlinear effects among familyvariables in their association with health. Consequently, we rationally selected several linear and quadratic terms for inclusion in thecanonical correlations. The correlations between the linear and quadratic terms for a given variable, however, were consistentlyhigh, with none lower than .75, and none reached the criteria for interpretation outlined above. We concluded that this high level ofassociation, plus an insignificant increment in explained variance by the quadratic terms, did not warrant their inclusion in theanalyses.

9This procedure was not designed to explore patterns of agreement and disagreement between spouses on self-report variables,as was Fitzpatrick's (1988) use of clustering and contrast scores.

10List-wise deletion of subjects was used in these analyses and those in the reports to follow. Therefore, the number of subjectsin each analysis varies as a function of missing data, a troublesome and perennial problem in assessment-intensive research. Areview was undertaken to determine if missing data were systematic in each set of analyses. Most missing data were the result offailure to complete more than half the items of a self-report scale, or to videotape or other equipment problems, especially in theearly stages of the research. Comparisons of family and health scores between families recruited in the first and last year of datacollection, however, yielded no significant findings.

11It is possible that interactions among subgroups of demographic variables or the cumulative effects of several demographicvariables taken together could be associated with both family and health scores. The number and type of analyses required todetermine what interactions or combinations of demographic variables should be considered in each set of family and health studiesare considerable, and we decided to explore them by testing specific hypotheses. This task will be undertaken once a generalmapping of family and health relationships has been completed.

12R2 refers to the proportion of the variance of the scaled data (correlations) in the partitioned matrix, which is accounted for bythe corresponding distances among the scores on the grid. The distances are represented by the score coordinates.

13It can be argued that our strategy of health assessment is suspect because a more intensive assessment in particular areas mightreveal "deficits" undetected by our "broad-net" approach. Given the nature of the sample and the limited time to assess a verycomplex construct, we offer two arguments in support of our position. First, given the nature of the sample, we have very littlejustification for selecting one area of health over another for more detailed assessment. Second, although admittedly not sensitive tonuance, we have a relatively better opportunity to observe patterns of health with a broad-net strategy than we would with"narrow-net" approach.

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