Analysing Couple Data-2

7/30/2019 Analysing Couple Data-2

1/11

Beyond Wives' Family Sociology: A Method for Analyzing Couple Data

Author(s): Elizabeth Thomson and Richard WilliamsSource: Journal of Marriage and Family, Vol. 44, No. 4, Methodology: The Other Side ofCaring (Nov., 1982), pp. 999-1008Published by: National Council on Family RelationsStable URL: http://www.jstor.org/stable/351459 .Accessed: 16/10/2011 12:50

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of

content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected].

National Council on Family Relations is collaborating with JSTOR to digitize, preserve and extend access to

Journal of Marriage and Family.

http://www.jstor.org
http://www.jstor.org/action/showPublisher?publisherCode=ncfrhttp://www.jstor.org/stable/351459?origin=JSTOR-pdfhttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/stable/351459?origin=JSTOR-pdfhttp://www.jstor.org/action/showPublisher?publisherCode=ncfr


2/11

B e y o n d W i v e s ' F a m i l y Socio logy:A M e t h o d f o r Analyz ing C o u p l e D a t a

ELIZABETH THOMSONRICHARD WILLIAMSUniversityof Wisconsin

In this paper, we examine the measurement properties and variable structure ofwives' and husbands' reports of family life. We demonstrate the use ofJ6reskog's (1973, 1977) maximum-likelihood methods to estimate measurementand structural parameters in a series of models of couple childbearing expecta-tions. We ind that wives'and husbands' responses about the utility of children arebest represented by a model specifying wife's child utility to be distinct from hus-band's child utility. Contraryto our hypotheses, wefind that wives and husbandsare equally reliable reporters of this experience.

In 1969 Constantina Safilios-Rothschild criti-cized "wives' family sociology," i.e., thestudy of family life based on information pro-vided by wives. Since the role of women hadhistoricallybeen centered in the family, manyresearchers assumed that wives were appro-priate informants about family life. Further-more, women were more likely to be found athome and to respond to questions about thefamily. However, Safilios-Rothschild ques-tioned the assumption that wives alone pro-vided adequate information about family life,leaving practical considerations to standalone as not very strong justification for sur-veying only wives.Many family researchers have taken up thechallenge of asking both wives and husbands(and in some cases, children) about familylife. The "extra" data generated have beenused either to improve measurement or torespecify explanatory models of family phe-nomena. For example, Bagozzi and Van Loo

(1981) averaged wife's response and hus-band's response to create a measure of acouple's desire for children. On the otherhand, Fried and Udry (1979) used the re-sponses of husbands to respecify models ofwife's pregnancy as a function of bothpartners' subjective child utilities.In this paper we argue that it is not alwaysclear which of these uses of wife-husbanddata is appropriate, and we demonstrate amethod for determining which strategyto fol-low. Several hypotheses are developed aboutthe measurement properties of wives' andhusbands' reports of family life, and ques-tions are raised about the variable structureunderlying reports. Although we drawprimarily from research on marital fer-tility and illustrate our hypotheses with dataon childbearing expectations, their methodcould be applied to wife-husband data deal-ing with almost any aspect of family life.

MEASUREMENT ERROR ANDVARIABLE STRUCTUREIn past research investigators have tendedto assume that certain characteristics or be-haviors have a single true value for thecouple, while other characteristics or behav-iors may be different for wife and husband.In the case of so-called "couple" variables,

This research was supported by grants from the Centerfor Population Research, NICHD. We would like tothank Andrew R. Davidson, under whose direction thedata were collected, and Robert M. Hauser, whoprovided helpful comments and suggestions.

Department of Sociology, University of Wisconsin,Madison, WI 53706November 1982 JOURNAL OF MARRIAGE AND THE FAMILY 999


3/11

wife-husband discrepancies are interpretedasevidence of measurement error, while dis-crepancies between partners' reports about"individual" variables are assumed to repre-sent "real" differences. For example, Nealand Groat (1976) suggested that wife-husbanddiscrepancies in reports of contraceptive usereflected low levels of communication andconsequent measurement error;however, dis-crepancies in reports of desired family sizewereviewed as differences of opinion betweenwife and husband. Thompson and Walker(1981) provided a classification of variousphenomena as characteristics of the couple'srelationship (e.g., frequency of interaction,norms) or as characteristics of the individualpartners (e.g., needs, autonomy).It is not clear that hard-and-fast rules fordefining couple or individual phenomena arealwaystheoretically justified. If we carryBer-nard's (1972) "his" and "hers" marriage toits extreme, wife's and husband's reportsabout family life-even about such seeminglyobjective circumstances as which contracep-tive method the couple uses-must be treatedas separate indicators of each partner'sunique experience in the marriage. From thispoint of view, the couple's common experi-ence would have to be measured in some wayother than through each partner's indepen-dent report of the experience.On the other extreme, symbolic interactiontheorists have argued that wives and hus-bands construct, through their interaction,shared definitions of the marriage and otherrealities (Berger and Kellner, 1964). Bagozziand Van Loo (1978, 1981) asserted thatcouple utility of children is constructedthrough the exchange of reinforcements be-tween wife and husband. From this view dis-crepancies between partners' reports are dueentirely to error in measurement of the truecouple experience.In empirical research greater discrepancieshave been found between partners' reports ofassumed "individual" characteristics and be-haviors than between their reports ofassumed "couple" characteristics and behav-iors (e.g., Card, 1978; Neal and Groat, 1976).These findings are consistent with the suppo-sition that the smaller discrepancies are dueentirely to random measurement error, whilethe larger discrepancies comprise both errorsin measurement and "real" differences be-tween wife and husband. However, the same

results also could arise solely from errors inmeasurement. For example, reports of con-traceptive use might have less randommeasurement error than reports of desiredfamily size. This would result in greater dis-crepancies, on the average, between partners'reports of desired family size than betweentheir reports of contraceptive use, even ifthere were a single true value of desired fam-ily size for each couple. Another possibility isthat wives and husbands interpret items mea-suring contraceptive use in the same way, butplace different interpretations on items usedto measure desired family size. For example,suppose that the term "large family" meansfour children to wives and six children to hus-bands. Among couples desiring four children,the reported desire for a "large family" woulddifferfor wife and husband, even though thereexists a single true value of desired family sizefor each couple.The assessment of measurement error is im-portant not only to explain wife-husband dis-crepancies in reports of family life, but also tocorrect potential biases in estimates ofvariable relationships based on wives' andhusbands' responses. For example, severalstudies have reported that significantly morevariance in couple fertility behavior isexplained by models respecified to includehusbands' views of childbearin2 than bymodels using wives' views alone (Beckman,1979, 1980; Fried and Udry, 1979; Fried etal., 1980; Townes et al., 1980), suggestingthat the latter models had been misspecified.However, these findings also are consistentwith a measurement error explanation:husbands' reports partially correct theestimated correlation coefficient for attenua-tion due to measurement error; the "in-crease" in explained variance simply reflectsthe addition of a second, error-riddenmeasure of the same true variable reflected inwives' responses.On the other hand, the studies cited abovealso reportedthat wives' views of childbearingwere more strongly related to couple fertilitybehavior than were husbands' views, sug-gesting that wives have more influence oncouple fertility behavior than do husbands.These findings also could be explained bymeasurement error. Suppose that oneassumption of "wives' family sociology" iscorrect: wives' reports of family life are moreaccurate (contain less measurement error)

1000 JOURNAL OF MARRIAGE AND THE FAMILY November 1982


4/11

than those of husbands. If this is true,estimates of variable relations involving thewives' responses may be biased upward inrelation to estimates of variable relations in-volving the husbands' responses. Thus, thereare two opposite effects of measurement erroron estimates of wives' versus husbands'influence on couple behavior: on the onehand, measurement error in husbands'responses may cause us to underestimatetheir unique influence; and on the otherhand, measurement error in wives' responsesmay create the appearance of uniquehusbands' influence, even where it does notexist.In summary, we believe that the variablestructureunderlyingmarital partners'reportsabout family life remains an open question,and that measurement error must be in-corporated into our analyses before we canattempt to answerit. In the following section,we develop models incorporating differentassumptions about the measurement proper-ties and variable structure underlying maritalpartners' responses about additional child-bearing. We demonstrate a method forestimating the parameters of such modelsand testing their absolute and relativegoodness-of-fit to the observed covariancesamong those responses. Although theseanalyses are meant to illustrate a method fordealing with the structural and measurementquestions discussed above, the findings arealso of substantive interest for research onmarital fertility.

METHODSFor purposes of illustration, we begin witha simple causal model in which the utility ofanother child to a married couple is a directcause of the couple's expectations for anotherchild. We compare this "couple" model to amodel in which wife's child utility andhusband's child utility independently affectthe couple's expectations for another child.For clarity of presentation, we assume thatthe dependent variable, childbearing expec-tations, is a "couple" variable. Expectationsshould take into account the resolution of anyconflicts between wife and husband in in-dividual desires for children and, therefore,are likely to be represented by a single truevalue for the couple. (It is also possible toestimate models in which wife's expectation

and husband's expectation are specified asindividual variables; we deal with thispossibility in our summary and discussion.)It is assumed that child utility and child ex-pectations are unobserved (latent) variables,reflected more or less accurately by theobserved responses (indicators) of wives andhusbands. In order to estimate and controlfor measurement error in the analyses,multiple indicators of each latent variable areused. It is possible, therefore, to estimate thestatistical behavior of the "true score" of eachindicator. The true score is the value anobserved response would have if (a) itmeasured only the variable of interest, and(b) it were measured without error. Thedifference between an observed response andits true score estimate is an approximateestimate of measurement error.' If thereexists a single true score for the couple, thenthe true scores of the wives should beperfectlycorrelated with the true scores of thehusbands.2We use Jdreskog's (1973, 1.977)methods-specifically, JSreskog's and Si5rbom's(1978)LISREL IV program-to estimate theparameters of our models. A completetechnical discussion of these methods isbeyond the scope of this paper, but we willbriefly discuss their key features andadvantages for addressing the substantiveand methodological questions we have raised.Under the assumption that observed re-sponses or indicators have a joint multivariatenormal distribution, LISRELgives maximumlikelihood estimates for the parametersof twolinear models-the measurement model andthe structural model. The measurementmodel specifies the relations between latentvariables and observed indicators, while thestructural model specifies relations amonglatent variables. Using the LISREL program,it is possible to estimate the parameters andstandard errorsof both models simultaneous-ly.An important advantage of the LISRELprogram is that it provides a likelihood-ratiostatistic, L2, which tests the fit betweenobserved indicator covariances and those esti-mated under a hypothesized measurementand structural model. In large samples, L2follows the chi-square distribution withdegrees of freedom equal to the number ofobservedmoments (variancesor covariances),less the number of independent parameters in

November 1982 JOURNAL OF MARRIAGE AND THE FAMILY 1001


5/11

the model (Sobrbomand J6reskog, 1981). Ahigh value of L2 relative to the degrees offreedom indicates a low probability that thehypothesized model could have generated theobserved covariances; a low value of L2suggests that the model fits the observationsfairlywell. In practice, because the likelihood-ratio test is rather powerful, models rarely fitat conventional significance levels (i.e., .05 or.01), particularly when the sample size islarge. The ratio of L2 o its degrees of freedomis sometimes used as an alternative criterion,with ratios from 2 to 1 up to 10 to 1 oftenregarded as satisfactory. The theoreticaljustification of a model and the plausibility ofthe estimates also must be considered whenassessingthe fit of a model. For example, it isoften possible to improvethe fit of a model byincluding parametersthat have little theoreti-cal justification or whose estimated valuesrun counter to sound theory; in such casesone may well be better off to accept a poorerfit. Examination of the residual covariances(that is, the difference between the observedmoments and the estimated moments) alsocan be useful, since this may help identify thecauses of the lack of fit.Another desirable aspect of the likelihood-ratiochi-square statistic is that it can be usedto compare the relative fit of "nested"models. A model, M1, is said to be nested ina second model, M2, if M1 can be specifiedby placing restrictions on one or more of theparametersthat are estimated ("free") in M2(Long, 1976). Typical restrictions includefixing a parameter at zero (such as when oneis hypothesizing that one variable does nothave a direct effect on another), fixing aparameter at some value other than zero, orspecifying that two or more parameters areequal. When models are nested, thedifference between the L2 values also has achi-square distribution, with degrees offreedom equal to the number of restrictionsin M1 that are not in M2 (or, equivalently,the difference between the degrees of freedomof the two models). A small L2value relativeto the degrees of freedom suggests that therestrictions are justified, while a large L2value suggests that the more general model(M2) provides a better fit to the data.There are, of course, other ways ofcontrolling for measurement error. Forinstance, simple averaging of responses issometimes used. However, this approach

does not take into account the possiblydifferent amounts of measurement error indifferent responses, nor does it test theassumption that the responses actually domeasure the same latent variable. Moresophisticated approaches may avoid orminimize these problems, and we do notclaim that JSreskog'smethods are necessarilysuperior to all of them in all cases. However,these methods do strike us as being extremelyflexible means by which researchers can (a)control for measurement error, (b) estimatestructural equation models, and (c) test thevalidity of the measurement and structuralassumptions being made.The present data come from a 1977 surveyof 349 white, married couples living in thegreater Seattle area. Each of the wives hadborne her first or second child during1973-1976 and was between the ages of 26and 36 at the time of the survey. Couples wereexcluded from the surveyif either partnerhadbeen surgically sterilized.Wives and husbands simultaneously andindependently completed structured writtenquestionnaires at home in the presence of aninterviewer.No exchange of information tookplace while the questionnaires were adminis-tered, and no information was subsequentlyprovided to either partner about theresponses of the other. The followingmeasures were derived from the completedquestionnaires, separately for wives and forhusbands:1. Utility of another child. Respondentswere presented with several possible conse-quences of having another child within twentymonths. Products of their subjective proba-bility of each consequence (0 = no chanceS. . 10 = certain) and their evaluation of theconsequence (-3 = extremely bad ... +3= extremely good) were constructed to form"subjective expected utilities" (Edwards,1961) of another child. For these analyses thesubjective expected utilities, "a fulfilledfamily life" (W1 and H1) and "watchinganother child grow and develop" (W2 andH2) were used as multiple indicators of childutility.2. Childbearing expectations. Respondentswere asked to estimate the likelihood that thecouple would have another child withintwenty months (1 = extremely unlikely...7 = extremely likely). Responses of bothpartners (W3 and H3) were used as multiple



6/11

TABLE 1. CORRELATION MATRIX, MEANS, AND STANDARD DEVIATIONSW1 W2 H1 H2 W3 H3

W1 1.000W2 .470 1.000H1 .460 .270 1.000H2 .312 .223 .495 1.000W3 .628 .421 .498 .381 1.000H3 .596 .347 .586 .422 .816 1.000Means 11.36 22.34 9.75 18.50 3.64 3.66SD 11.45 10.89 10.73 10.30 2.66 2.60

Note: WI = Wife's "fulfilled family life" SEU; W2 = Wife's "watching another child grow and develop" SEU;W3 = Wife's childbearing expectations; H1 = Husband's "fulfilled family life" SEU; H2 = Husband's "watchinganother child grow and develop" SEU; H3 = Husband's childbearing expectations.

indicators of couple childbearing expecta-tions.In our specifications models are mean-independent: only the variances and co-variances of the indicators are used inestimating parameters, and the means of theindicators are ignored. As in ordinary leastsquares regression, this has no effect onparameterestimates, except that no interceptcan be estimated. One consequence is thatany constant differences between wives andhusbands (such as all wiveswanting one morechild than their husbands) are ignored, sinceadding a constant to a variable does not affectits variance. Models which do incorporatepossible constant differences also can be esti-mated with LISREL.

Our analyses were based on the observedcovariances among wives' and husbands'responses on each of the three indicators(total of six observedvariables), using couplesfor whom all of the responses wereascertained (n - 340). The correlationmatrix and vectors of means and standarddeviations correspondingto these covariancesare presented in Table 1.RESULTS

First, we estimated a "couple" model,diagrammed in Figure 1. According to thismodel (Model 1), the wife's responses andhusband's responses about the utility ofanother child are all imperfectly measuredindicators of a single latent variable, thecouple's child utility. The indicator co-efficients-that is, the effects of the latentvariable on its indicators (Wi, Hi)-are rep-resented by the Greek letter lambda (X).Theerror component of each indicator is repre-sented by the Greek letter epsilon (e). The

structural effect is denoted by the symbolbeta (0), while zeta (?) stands for the residualin couple expectations. Under the assump-tions of this model, the "true scores" of thefour responses are all perfectly correlated,differing at most by some scale factor (e.g.,true score of W2 is alwaystwice the true scoreof W1). If this model holds, responses fromboth wife and husband may improvemeasurement of couple child utility, but thereare no distinct effects of wife's child utilityand husband's child utility on couple child-bearing expectations.As shown in Table 2, the goodness of fitstatistic for Model 1 yields an L2 value of58.70 with eight degrees of freedom. Clearly,this model provides a poor fit to the data.Since this model places no restrictions on therelativequality of the indicators but still doesnot provide a very good fit to the observedcovariances, it may be that the couplestructure is misspecified. That is, there maybe two distinct latent variables underlyingtheobserved covariances, wife's child utility andhusband's child utility.Figure 2 illustrates our wife-husbandmodel of the relation between partners' childutilities and couple childbearing expecta-tions. The symbols for the parameters of thismodel are the same as those in Figure 1, withthe addition of phi(+i), he covariancebetweenwife's child utility and husband's child utility.In this model each partner's child utilityhas aseparate effect on their joint expectations,implying that responses from both partnerswill improve model specification.When the model shown in Figure 2 is esti-mated without restrictions on the relativequality of the indicators (i.e., model 2A), anL2value of 20.00 with six degrees of freedomis obtained (Table 2). This is clearly a much



7/11

FIGURE 1. COUPLE'S UTILITY OF ANOTHER CHILD AND COUPLE'S CHILDBEARING EXPECTION(VARIABLE LABELS DEFINED IN TABLE 1)

W-X1=1

E2 2 2 5=1W35 f 2W2 X2 Couple's Couple's F5, 5X3 Child Expectation X6C3 1X43Utility- H3 6e3 -'--H1

H

e4 -H2

better fit than the similarly unrestrictedcouple model, although there is still room forimprovement. Although it may not beimmediately obvious, Model 1 is "nested" inModel 2A. Restricting the wife's child utilityand the husband's child utility to be perfectlycorrelated and to have identical effects oncouple expectations is equivalentto specifyingthat there is a single couple child utilitymeasured by all four responses. Intuitivelythis makes sense; if two variables areperfectly correlated with each other, theireffects are indistinguishable, which is justwhat Model 1 says.Even if we are willing to accept the fit ofModel 2A, there are at least three modifica-tions of the model that should be tested, andseveral other modifications that may be ofinterest. The first modification (Model 2B)restricts the effects of the latent child utility

variables on counterpart responses of wifeand husband (i.e., the indicator coefficients)to be equal.3 This does not mean that thewife's true variance is equal to the husband'strue variance on a particular response, sincethere is no requirement that the two latentvariables have the same variance. What thisrestrictiondoes mean is that the scales for thetwo husband indicators are in the same ratioto each other as the scales for the two wifeindicators.Why is this restriction reasonable? Sup-pose there were two measures of income, oneof which expressed earnings in dollars, theother in quarters. If women said that forevery dollar they earned they made fourquarters, while men claimed that each dollarwas worth six quarters, there would be reasonto believe that for some reason thesequestions were being interpreted differently

TABLE 2. COUPLE AND WIFE-HUSBAND MODELSModels df L21. Couple model 8 58.70262. Wife-Husband models2A. Least restrictive 6 20.00032B. Counterpart indicator coefficients equal 7 21.07342C. Counterpart indicator coefficients equal,counterpart error variances equal 9 23.23482D. Counterpart indicator coefficients equal,counterpart error variances equal,latent variances equal 10 24.7405



8/11

FIGURE 2. WIFE'S AND HUSBAND'S UTILITY OF ANOTHER CHILD AND COUPLE'S CHILDBEARINGEXPECTATION (VARIABLE LABELS DEFINED IN TABLE 1)

Wife'sX2 ChildC2- W2 tility

Couple's X5 W3- 5Expectation 6H 3-----E6

3 "H,1

"=1Husband's2H36

X4 Childe4 - H2 Utility

by men and women. Similarly, although theconcepts are a bit more abstract, differentindicator coefficients for the questionsdealing with the child value might indicatethat men and women interpreted these itemsdifferentlyand that they would have differentrelationships with each other. ContrastingModel 2B with Model 2A yields an L2difference of only 1.07 with one degree offreedom; this strongly suggests that theseitems do have the same meaning for both menand women.Also of interest are possible differences inthe amount of measurement error con-taminating the responses of wives and ofhusbands. When assessing how contaminatedwith error a response is, one is generallyinterested in either the error variance of theresponse or else its reliability (wherereliability is defined as the true variancedivided by the total variance). Thus, in Model2C we add a further restriction that the errorvariances of partners' responses to the sameitems are equal.4 This model yields an L2value of 23.23 with nine degrees of freedom.The contrast of Model 2C with Model 2B isnot significant (L2 = 2.16 with two degreesof freedom). However, this does not meanthat counterpart husband and wife variablesare equally reliable, since the latent variablevariances have not been constrained to be

equal; that is, although the error variancesare equal, the truevariances are free to differ,thus producing different reliabilities. Whenthat constraint is applied in Model 2D,the contrast with Model 2C also isinsignificant (L2 = 1.51 with one degree offreedom). Thus, regardless of whether onelooks at total error variance or reliability, itmust be concluded that wives' and husbands'responses are equally contaminated withmeasurement error, at least in this sampleand for these measures.5

Although our discussion has focused on themeasurement and structure of the in-dependent variable(s), child utility, the esti-mates of structural effects in our preferredmodel are also of interest. When these esti-mates are allowed to differ, the path co-efficient for the wife's effect is slightly largerthan that for the husband's effect. However,when these coefficients are specified to beequal, the increment in L2is not statisticallysignificant (L2 = 1.12 with one degree offreedom). Therefore, in this sample wife andhusband are equally reliable reporters oftheir own child utilities, and the two childutilities have equal and additive effects on thecouple's childbearing expectations. Due tothe relatively strong correlation betweenwife's and husband's child utilities (r = .63),the respecification to allow for both partners'



9/11

effects explains 10% more variance in coupleexpectations than that explained by a singlepartner's child utility alone. In all, 70% ofthe variance in couple expectations can be ac-counted for by the two child utility variables.DISCUSSIONWe have demonstrated a method for simul-taneously estimating the measurement andstructure underlying wives' and husbands'reports about family life. Under theassumption of our basic model that couplechildbearing expectations exist, it was foundthat marital partners' responses about theutility of another child were best representedby a model specifying wife's utility to bedistinct from husband's utility. In addition,wives and husbands appeared to be equallyreliable reporters of their utilities. Whenmeasurement effects on response werecontrolled, the structuraleffect of wife's childutility on couple childbearing expectationswas equal to but separate from that ofhusband's utility.These findings are somewhat inconsistentwith the findings of previous fertility researchusing wife-husband responses. When wives'and husbands' responses are equally reliable,estimates of wife and husband effects in-

corporating measurement error should beproportional to estimates based on theassumption of perfect measurement. Givenour finding that wives and husbands areequally reliable reporters of child utility, wemight have expected the same results as thosefrom analyses assuming perfect measure-ment-that the effect of wife's child utility oncouple child expectations was stronger thanthe effect of husband's child utility. Instead,we found that estimates of the two effectswere equal.We do not wish to overemphasize this dif-ference between our results and those ofprevious research. As we stated earlier, theanalyses presented here are intended todemonstrate the use of JbSreskog's(1973,1977) methods for estimating the structuraland measurement properties of wife-husbandresponses about family life. We have notperformed exhaustive analyses of all possiblecouple or wife-husband models of child-bearing expectations or behaviors. In supportof our findings of equal effects of partners, wefind that the results hold even when equality

restrictions are placed on the indicator co-efficients and error variances of partners'responses about childbearing expectations(L2 = 27.40 with 13 dJ). Given the power ofthe likelihood ratio statistic, this is not an un-reasonable fit to the data. Furthermore, it isnot clear from reports of most of the studiescited whether differences between wife'seffect and husband's effect were statisticallysignificant; it is possible that they fell withinstatistical confidence limits for those esti-mates.Of course, our models' assumption thatchildbearing expectations constitute a "cou-ple" experience may be faulty; if childbearingexpectations are individual experiences,equality of effects of wife and husband on"couple" experience is meaningless. We alsoestimated a model in which two separatedependent variables, wife's childbearingexpectations and husband's childbearingexpectations, were each a function of wife'schild utility and husband's child utility. Sincethere was only one response from eachpartner regarding childbearing expectations,it was necessary to assume in this model thatthe dependent variables were measured with-out error. In this model our findingsregarding the structure and measurement ofthe independent variables, child utility, wereupheld. However, equality of wife's and hus-band's effects was accepted only for hus-band's childbearing expectations; wife'schildbearing expectations were more stronglyaffected by wife's child utility than by hus-band's child utility. These results are some-what more consistent with prior research. Asnoted below, however, measurement error inthe dependent variables could account forthese findings.As we would expect, our estimates ofstructural effects are larger than estimatesbased on assumptions of perfect measure-ment. The percentage of variance in couplechildbearing expectations explained by wife'sand husband's child utilities is similar tomeasurement-corrected estimates for therelation between wife's child utility and wife'schildbearing expectations (Davidson andJaccard, 1975).Using the methods illustrated here, it ispossible to consider more elaborate specifica-tions of measurement error than thosepresented in this paper. In particular, wehave not presented models that specify corre-



10/11

lations among response errors due to non-random sources of measurement error or dueto common causes not included in ourmodels. If such correlations exist and are notestimated, estimates of structural relationsamong latent variables will be biased. Forexample, our finding that wife's child utilityhas a stronger effect on wife's childbearingexpectation than does husband's child utilitymight not hold if there were a positive corre-lation between errors in the wife's responsesto child utility and childbearing expecta-tions.6These are just a few of the possiblemeasurement and structural variations thatmay be estimated and tested with Jb*reskog's(1973, 1977) methods. As we have discussedabove, the capability of these methods toprovide simultaneous estimates of structuraland measurement parameters is particularlyuseful in the analysis of wife-husband data.We hope that this paper will stimulate similarresearch using other data sets and focusingon other aspects of family life. Theaccumulation of such research shouldincrease substantially our understanding offamily life and marital partners' responses toit.

FOOTNOTES1. Alwinand Jackson(1979)do not includethe firstcriterian theirdefinitionof true score andmake adistinction etween truescoremodelanda commonfactormodel.Also, underour definitionerrorvari-ance sgenerated othbyrandommeasurementrrorandby uniqueeffectsof variables xcluded romthemodel. Alwin and Jacksontreat these componentsseparately. In practice these distinctions arefrequentlygnored,althoughheresearcherhouldbeawareof theirimplications.2. It is possible orthere o be scaledifferences,uch asthe wife's ruescorebeingtwice as largeas the hus-band's true score. We show how these can be

examined. As we explain later, however,constantdifferences suchas wivesalwaysscoringone pointhigher than husbands)are not addressedby ouranalysis.3. In this and subsequentmodels, similarrestrictionsarenotplacedonthemeasurementarametersf thedependentvariable ndictors.As mentioned n thesummaryand discussion,these restrictionsdo notaffectanyof the findingsreportedhere.4. Noteagain hatthe estimateof "error" ariancemayincludespecificvariancedue to variablesexcludedfromthe model.

5. Similar restrictions also could be considered for the"couple" model. The meaning of such restrictionswould be essentiallythe same, except that the equalityrestrictions on indicator coefficients would imply thatthe "true scores" of wives' and husbands' responsestothe same items were identical. These modificationsare not discussed here, because the less restrictedcouple model provided a relativelypoor fit.

6. For the models specified here, correlated errorbetween indicators of a latent variable produces thesame covariance estimates as allowing the responsesto be indicators of two separate variables. That is,allowing for correlations among the "error" terms ofeach partners' responses (El and E2, E and E4) inModel 1 produces exactly the same estimated covari-ance matrix as Model 2A. When additionalrestrictionsare applied, the two specifications providevery similar, though not identical estimates. Withoutadditional information, it is not possible todistinguish empirically between a respondent'sunique error tendencies and her or his true scores.However, in the model with a single "couple"variable, our estimates of error variances andcovariances were extremely high relative to theestimated "true score" variances. If the errorvariances and covariances had arisen from uniqueerror tendencies of each partner, we think they shouldhave been small in relation to the "true" variances.Therefore, we think that the wife-husband models aremore plausible than couple models with respondenterror correlations.

REFERENCESAlwin, D. F. and Jackson, D. J.1979 "Measurement models for response errors insurveys:issues and applications." pp. 68-119 inK. F. Schuessler (Ed.), Sociological Methodol-ogy 1980. San Francisco:Jossey-Bass.Bagozzi, R. P. and Van Loo, M. F.1978 "Towards a general theory of fertility: a causalmodeling approach." Demography 15(August):301-320.1981 "Decision-making and fertility: a theory of ex-change in the family." Pp. 91-124 in T. K.Burch (Ed.), Demographic Behavior: Inter-disciplinary Perspectives on Decision-Making.Boulder, CO:Westview Press.Beckman, L. J.1979 "The process of couples' fertility decision-making." Paper presented at the annualmeeting of the American PsychologicalAssocia-tion, New York (September).1980 "Married couples' fertility decision-making."Paper presented at the annual meeting of theWestern Psychological Association, Honolulu,HI (May).Berger, P. L. and Kellner, H.1964 "Marriage and the construction of reality."Diogenes 46:1-23.



11/11

Bernard, J.1972 The Future of Marriage. New York:World.Card, J. J.1978 "The correspondence of data gathered fromhusband and wife: implications for familyplanning studies." Social Biology 25(Fall):196-204.Davidson, A. R. and Jaccard, J. J.1975 "Population psychology: a new look at an oldproblem." Journal of Personality and SocialPsychology 31:1073-1082.Edwards, W.1961 "Behavioral decisiontheory." Annual ReviewofPsychology 12:473-498.Fried, E. S., Hofferth, S. L. and Udry, J. R.1980 "Parity-specific and two-sex utility models ofreproductive intentions." Demography 17(February):1-11.Fried, E. S. and Udry, J. R.1979 "Wives' and husbands' expected costs andbenefits of childbearing as predictors ofpregnancy." Social Biology 26:265-274.Jireskog, K. G.1973 "A general method for estimating a linearstructural equation system." Pp. 85-112 in A. S.Goldberger and 0. D. Duncan (Eds.),Structural Equation Models in the SocialSciences. New York:Seminar Press.1977 "Structural equation models in the socialsciences:specification, estimation and testing."Pp. 265-287 in P. R. Krishnaiah(Ed.), Applica-tion of Statistics. Amsterdam:North-Holland.Jioreskog,K. G. and So5rbom,D.1978 LISREL: Analysis of Linear Structural Rela-tionships by the Method of Maximum Likeli-hood. User's Guide. Chicago, IL:InternationalEducational Services.

Long, J. S.1976 "Estimation and hypothesis testing in linearmodels containing measurementerror: a reviewof Jdreskog's model for the analysis of covari-ance structures." Sociological Methods andResearch 5(November):157-206.Neal, A. G. and Groat, H. T.1976 "Consensus in the marital dyad: couples' per-ceptions of contraception, communication, andfamily life." Sociological Focus 9(October):317-329.Safilios-Rothschild, C.1969 "Family sociology or wives' family sociology: across-cultural examination of decision

making." Journal of Marriage and the Family31:290-301.S*rbom, D. and Jdreskog, K. G.1981 "The use of LISREL in sociological modelbuilding." Pp. 179-199 in D. J. Jackson and E.F. Borgatta (Eds.), Factor Analysis andMeasurement in Sociological Research. BeverlyHills, CA:Sage.Thompson, L. and Walker, A.1981 "The dyad as the unit of analysis: theoreticaland methodological issues." Paper presented atthe Pre-conference Workshop on Theory Con-struction and Research Methodology, NationalCouncil of Family Relations, Milwaukee, WI(October).Townes, B. D., Beach, L. R., Campbell, F. L. and Wood,R. L.1980 "Family building: a social psychological studyof fertility decisions." Population and Environ-ment 3(Fall/Winter):210-220.


Date post:	14-Apr-2018
Category:	Documents
Upload:	roxana-tudorache
View:	219 times
Download:	0 times

Analysing Couple Data-2

Documents