Factor structure of the Menstrual Distress Questionnaire (MDQ): Exploratory and LISREL analyses

Transcription

1 Bond University From the SelectedWorks of Gregory J. Boyle 1992 Factor structure of the Menstrual Distress Questionnaire (MDQ): Exploratory and LISREL analyses Gregory J. Boyle, University of Queensland Available at:

2 1 Factor Structure of the Menstrual Distress Questionnaire (MDQ): Exploratory and LISREL analyses Gregory J. Boyle University of Queensland Address correspondence to Gregory J. Boyle PhD, Department of Psychology, University of Queensland, St Lucia, Queensland 4067, Australia.

3 2 Abstract The Menstrual Distress Questionnaire (MDQ) is the most frequently used self-report instrument for measuring menstrual cycle symptomatology. However, its internal structure has been criticized. In a review of the psychometric properties of the MDQ (covering more than 50 studies from 1968 onwards), Spalding and Oei concluded that "the MDQ appears to measure constructs unrelated to the menstrual cycle, [that] its definition of the premenstrual syndrome may be inaccurate and the factor structure of this instrument may be unreliable." In order to clarify the structural dimensionality of the MDQ, exploratory, congeneric and confirmatory factor analyses of the MDQ item intercorrelations were undertaken on a sample of 369 Australian tertiary college students. Congeneric analyses suggested that a number of the MDQ subscale items were not functioning efficiently and might best be removed from subsequent versions of the instrument. Nevertheless, results using the full LISREL 7 model generally supported the claimed structure of the MDQ instrument, with acceptable adjusted goodness of fit index and root mean square residual estimates being obtained.

4 3 INTRODUCTION Much of the extant psychological literature which is based on research with female subjects has failed to take into account possible interactions with menstrual cycle symptoms and mood states. There is an extensive body of literature documenting the association of increased negative affectivity with the premenstruum (e.g. Moos & Leiderman, 1978; Reichlin, Abplanalp, Labrum, Schwartz, Sommer & Taymore, 1979; Friedman, Hurt, Arnoff & Clarkin, 1980; Foresti, Ferraro, Reithaar, Berlanda, Volpi, Drago & Cerutti, 1981; Sampson & Prescott, 1981; DeLeon-Janes, Val & Herts, 1982; Backstrom, Sanders, Leask, Davidson, Warner & Bancroft, 1983; Haskett, Steiner & Carroll, 1984). In an investigation ofparamenstrual (comprising Day 1 of menstruation together with the three immediately preceding "premenstrual" days) susceptibility to depressogenic stimuli, Boyle (1985b) reported that normal young women in the late luteal phase of the monthly cycle who were exposed to such stimuli [via the Velten (1968) mood-induction procedure] exhibited significantly more negative post-induction mood states [namely, Sadness, Shame, Fear and Hostility-as measured via the Differential Emotions Scale (Boyle, 1984)] than did women subjected only to emotionally neutral stimuli. Cyclic variations in female reproductive hormones (estrogen and progesterone) have been related to the etiology of menstrual symptoms (including primary dysmenorrhoea). Fluctuations in progesterone levels have been implicated in the alteration of physical symptoms and concomitant mood states (Lahmeyer, Miller & DeLeon-Janes, 1982; Sanders, Warner, Backstrom & Bancroft, 1983). Given the apparent complexity of the interactions between menstrual cycle phase, physical symptoms and psychological mood states, it is

5 4 evident that multidimensional measurement of menstrual cycle symptoms and moods is required. Univariate measurement is simply unable to document the multiplicity of changes across the menstrual cycle. The need for multivariate measurement has been addressed most forcefully by the extremely prodigious Cattellian school of psychology, which has produced a large number of multifactor instruments designed to measure both normal and psychopathological personality traits, motivational dynamic traits, transitory mood states, as well as intellectual abilities. The Menstrual Distress Questionnaire (MDQ) follows in this multivariate Cattellian tradition, enabling a more realistic assessment of changes across the normal menstrual cycle. The MDQ (Moos, 1985) is a multivariate self-report inventory designed to index a number of such clinically important symptoms and psychological changes across the menstrual cycle (Boyle, 1991b). Moos (1968) conducted an initial principal components analysis (together with Varimax orthogonal rotation) of the intercorrelational data derived from responses to 47 items (relating to symptoms such as anxiety, depression, insomnia, tension, headache, etc.) from a sample of 839 young, healthy women (mean age 25.2 years, SD = 3.9 yrs; mean education 15.2 years, SD = 1.7 yrs). This "little Jiffy" approach to exploratory factor analysis (EFA) is known to produce only approximate factor solutions (Boyle, 1988, pp ). Separate factor analyses were carried out for menstrual, premenstrual and intermenstrual phases, as well as for the responses obtained in relation to the women's "worst menstrual cycle". The resultant instrument is comprised of eight-factor analytically derived subscales (three somatic symptom subscales, three subscales related to mood and behavioral changes and two additional subscales) labelled respectively: Pain, Water

6 5 Retention, Autonomic Reactions, Negative Affect, Impaired Concentration, Behavior Change, Arousal and Control. It was reported that the MDQ subscale dimensions were replicated across all four analyses. The eighth subscale (Control) was comprised of items taken from the Blatt Menopausal Index and was used to verify that item responses from the first seven subscales reflected menstrual cycle changes rather than general feelings of dysphoria and tendency to complain. According to Moos (1985, p. 3): "The Pain scale is composed of symptoms usually associated with dysmenorrhea, while the Water Retention and Negative Affect scales include symptoms that are generally associated with premenstrual syndromes. The Autonomic Reactions and Impaired Concentration scales are composed of symptoms that are not described quite as frequently in the literature... Behavior Change is made up of a familiar set of reactions that some women report in conjunction with their menstrual cycle. The Arousal scale taps the 'positive' reactions that several investigators have noted in relation to the menstrual cycle... The Control scale is composed of items that are endorsed quite infrequently." Moos (1985, p. 6) reported moderate to high "internal consistency" estimates for the various MDQ subscales. Item homogeneity (KR 20 ) coefficients were found to range from 0.56 to 0.94 for the Today (T) form of the instrument (a prospective state version of the MDQ), with the mean alpha coefficient being Previously, Markum (1976) had reported split-half estimates of item homogeneity ranging from 0.82 to 0.98 for the respective MDQ subscales.

7 6 Unfortunately, despite the continued promulgation in the AERA I A PA I NCME Standards for Educational and Psychological Testing (1985) of classical test opinion regarding the issue of "internal consistency", item homogeneity estimates > 0.7 actually may suggest significant item redundancy, with narrow breadth of measurement of the particular factor, due to overlapping items (Boyle, 1985a, 1987, 1991a; Cattell, 1973, pp ). Fewer items would therefore provide more efficient measurement, with little reduction in the actual subscale reliabilities. Use of LISREL congeneric factor analyses would suggest which items should be removed from the various MDQ subscales (Jöreskog & Sörbom, 1989a,b). The test-retest reliability of the MDQ subscales has been investigated in a number of studies (e.g. Moos, Kopell, Melges, Yalon, Lunde, Clayton & Hamburg, 1969; Lahmeyer et al., 1982; Markum, 1976). In general, the results have been supportive of the reliability of the respective subscales, although a number of the subscale stability estimates reported by Moos et al. (1969) were below 0.40, suggesting some lack of cohesion among the menstrual cycle symptoms measured in the MDQ. Given the dynamic fluctuations in mood states, physical symptoms and behavioral changes across the menstrual cycle, the failure to differentiate between immediate test-retest (dependability) and longer-term test-retest (stability) coefficients in such studies leaves the question as to the reliability of the MDQ subscales somewhat unresolved (Boyle, 1983; Cattell, 1973, pp ; 1978). The reported stability estimates cannot be taken to indicate lack of reliability, since the MDQ was designed to be sensitive to dynamic fluctuations related to menstrual cycle changes. Indeed, if the MDQ is truly sensitive to such changes, then one would expect rather low

8 7 stability coefficients, but appreciably higher dependability coefficients, if the instrument is reliable and truly sensitive to situational fluctuations in symptoms and moods. The utility of the MDQ has been examined in a wide variety of applied settings [see Moos (1985) for an extensive summary of these studies]. Indeed, the MDQ has been the most extensively used self-report instrument for documenting changes in menstrual cycle symptoms and moods so that the definition of premenstrual syndrome (PMS) has been restricted unduly to the MDQ conceptualization. Furthermore, information regarding menstrual cycle changes in symptoms and moods (especially concerning the premenstrual phase) remains somewhat fragmentary and therefore inadequately understood (Haskett & Abplanalp, 1983; Siegal, Myers & Dineen, 1987). According to Spalding and Oei (in press), "The major causes of this lack of understanding are those of inaccurate conceptualization of the syndrome and resulting measurement problems... research has been plagued by the use of assessment devices that have not been validated." Given that the MDQ definition of menstrual cycle symptomatology has dominated clinical practice and research for over the past 20 yr (Moos, 1968), evaluation of the psychometric properties, and especially the construct validity of the MDQ is needed urgently. It would seem germane to investigate further the factor structure of the MDQ in order to verify its structural dimensionality. Only partial factor analytic support for the purported structure of the MDQ has

9 8 been obtained in studies by Woods, Dery and Most (1982), Voda (1980), Clare and Wiggins (1979), Siegal et al. (1987) and van der Akker and Steptoe (1985). More complete replication of the purported eight-factor structure of the MDQ was reported in studies by Brooks-Gunn and Ruble (1979), and by Silbergeld, Brast and Noble (1971). On the other hand, Cullberg (1972) failed to verify the claimed MDQ factor structure using a Swedish version of the instrument. Cullberg reported that "the factor analysis was blurred due to several items being non-specific in regard to the menstrual cycle and to subjects interpreting the questions differently." (Spalding & Oei, in press) Englander-Golden, Whitmore and Dienstbier (1978) suggested that the eight-factor structure of the MDQ may have largely resulted from the cultural stereotypes and exaggerated symptoms associated with retrospective reports (the present study avoids this difficulty since only prospective data is examined). A priori, it is unlikely that Swedish and U.S.A. stereotypes of menstrual cycle moods and symptoms are significantly different from each other. Of the studies which have investigated the factor structure of the MDQ, only two have replicated all purported MDQ factors (i.e. Brooks-Gunn & Ruble, 1979; Silbergeld et al., 1971). Differences in the application of factor analytic methodology in the various studies may account for most of the discrepant findings. While the factor analyses were inadequately reported in every instance, it is clear however, that the respective investigators utilized the 'little Jiffy" approach for factor analysis. Thus principal components were extracted on the basis of the Kaiser-Guttman (K-G) eigenvalues greater than unity, together with orthogonal Varimax rotation. This method produces spurious results and

10 9 cannot be recommended. This leaves the factorial validity of the MDQ instrument open to question. The present study sought to clarify this issue by carrying out a methodologically sound exploratory factor analysis of the MDQ item intercorrelations, together with LISREL congeneric and confirmatory factor analyses (Jöreskog & Sörbom, 1989a). Age and education characteristics of the present sample were similar to those of the standardization sample so that the obtained factor analytic results should be directly comparable with those reported by Moos (1985). A number of issues need to be addressed regarding the various factor analytic studies of the MDQ item intercorrelations. First, the sample size in many of the studies has been insufficient to derive stable factors. Empirical research (e.g. Baggeley, 1982) has shown that at minimum, there should be at least 10 subjects per variable, and for maximum-likelihood estimation, a minimum of 20 subjects per variable has been recommended (Nunnally, 1978, p. 402). Therefore, even on the less stringent criterion, no fewer than 470 subjects would be required to obtain reliable factors. According to Cuttance (1987, p. 243), "MacCallum (1985) investigated the process of the exploratory fitting of models in simulated data, that is, data for which the true model was known. He found that only about half of the exploratory searches located the true model... in samples of 300 observations... his success rate in smaller samples (n = 100) was zero... An exploratory analysis of data thus entails the risk of inducing an interpretation founded on the idiosyncrasies of individual samples." Indeed, only the study by Brooks-Gunn and Ruble (1982) satisfied the minimum number of subjects requirement (their sample comprised 639 public school girls). Apart from the initial exploratory factoring of the MDQ item

11 10 intercorrelations by Moos (1968), it appears that all subsequent factor analyses can be dismissed as methodologically unsound. Second it is important that the appropriate number of factors is extracted, determined by various tests. Only the K-G criterion was employed in the several previous factor analyses of the MDQ instrument. The K-G criterion has been shown to be unreliable in many instances (cf. Zwick & Velicer, 1982). This method underestimates the appropriate number of factors when the number of variables in the analysis is fewer than about 20, and overestimates the number when there are more than variables (Cattell & Vogelmann, 1977; Horn & Engstrom, 1979; Hakstian, Rogers & Cattell, 1982). One difficulty with the Scree test (Cattell, 1966) has been the subjectivity of the decision as to the appropriate break in the Scree line. Accordingly, Gorsuch and Nelson (1981), as well as Barrett and Kline (1982a, b) have developed computer algorithms for the objective application of the Scree test (as has the SPSSX statistical package- SPSS Inc., 1988). Other objective methods for determining the number of factors to extract have been developed, including Velicer's (1976) MAP test, and Revelle and Rocklin's (1979) Very Simple Structure (VSS) methods. In order to assess the accuracy of the factor extraction number, it is essential to examine the ± 0.10 hyperplane count of the factor pattern solution, and to compare this quantitative index of the degree of achievement of simple structure with the corresponding hyperplane counts for solutions with both one fewer and one more factor extracted. If the factor pattern is satisfactory, the hyperplane count should lie within the 65-85% range. Third, use of an iterative procedure is desirable, given the common factor model (cf. Harman, 1976), whereby the proportion of common factor variance to

12 11 unique variance is ascertained more precisely, thereby enabling more accurate factor loadings to be obtained. Iteration of the factor matrix is especially important when the number of variables included in the analysis is small (cf. Boyle, 1988). Communality estimates should converge after only a relatively small number of iterations. Indeed, when a large number of iterations is required, this suggests that the resultant factor pattern solution is unstable. Fourth, to achieve simple structure, usually it is necessary to carry out an oblique rotation, systematically varying the obliquity of the factor axes. Examination of the corresponding ±0.10 hyperplane counts should indicate the most appropriate level of obliquity, for the given sample. Use of orthogonal Varimax rotation in the previous studies of the factor structure of the MDQ necessarily precluded attainment of maximum simple structure. There is extensive evidence that orthogonal rotation (which allows only a special resolution of the whole range of possible solutions) fails to achieve simple structure in almost every instance (cf. Cattell, 1973, 1978; Bolton, I 977; Loo, 1979; Kline, 1987; Gorsuch, 1983). If the factors are truly orthogonal, then an oblique solution will produce factors whose correlations are non-significant. Only then is Varimax rotation justified. Previous factor analytic studies of the MDQ have not even acknowledged this critical methodological issue. Indeed, for the greatest degree of accuracy in rotation, it is also advantageous to carry out rotation using a topological program such as Rotoplot (Cattell, 1978, pp ), over and above analytical rotation alone (cf. Boyle & Stanley, 1986). In addition, it is desirable to check the statistical significance of derived factors using the (conservative) Sine-Kameoka tables provided in Cattell's (1978) book, as well as checking the congruence of factor patterns. These are

13 12 the major considerations in conducting a valid EFA investigation (cf. Boyle, 1988, pp ). Failure to consider these issues in the MDQ studies cited above leaves the actual factor structure of the MDQ uncertain-thereby providing the rationale for the present study. Hence, the purpose of the present study was to test further the purported factor structure of the MDQ on a sample of healthy, young women. This was particularly important, given that one of the major criticisms of the instrument has been the general lack of applicability of the norms developed from the original standardization sample. In addition, the psychometric properties of the instrument (both its reliability and validity) have been seriously challenged. According to Spalding and Oei (in press), "A marked lack of adequate research into the MDQ's reliability, problems associated with the normative sample and uncertain validity were found." They concluded that the MDQ is an inadequate measure of menstrual cycle symptoms and moods. Part of the difficulty appears to reside in the finding (e.g. Abplanalp, Rose, Donnelly & Livingston-Vaughan, 1979; Golub & Harrington, 1981; Kirstein, Rosenberg & Smith, 1981; Slade, 1984) that the MDQ largely measures cultural stereotypes and public perceptions of menstrual cycle distress, since most of the negative effects are noted retrospectively, but not prospectively. Apparently, retrospective responses are influenced by social desirability stereotypes (Gannon, 1981). Rubinow and Roy-Byrne (1984) reported that even though negative symptoms may be distributed throughout the menstrual cycle, these are often attributed to the menstrual and premenstrual phases. Alternatively, the MDQ may measure something other than cultural stereotypes, and still be an inadequate measure of menstrual cycle symptoms

14 13 and moods (Pazy, Yedlin & Lomranz, 1989). In order to minimize these possible sources of bias, the present study is based on prospective data only. The factor structure of the MDQ is investigated comprehensively using a combination of exploratory, congeneric and confirmatory factor analytic methods. Method Subjects and procedure The sample included 369 female undergraduates, most of whom were enrolled in nursing degree programs in institutions in Melbourne, Australia. Ss ranged from 18 to 48 years of age, although the mean age was years (SD = 4.72 yrs). Some 35% of the sample was on the contraceptive pill. Clinical pathology was not a predominant feature of the menstrual cycle functioning of this sample of healthy, young women. Most were from a middle-class socioeconomic background, and virtually all were Australian-born. Form T (Today form) of the MDQ was used, enabling prospective responses to the questionnaire items. Subjects rated their symptoms and moods at the actual time of responding to the MDQ instrument. They appeared cooperative and willing to participate in the study and to respond to the MDQ items conscientiously. Data analyses Using the SPSSX statistical package (SPSS Inc., 1988), the intercorrelation matrix for the MDQ item responses was subjected to an exploratory factor analysis using an iterative maximum likelihood estimation procedure, together with extraction of factors on the basis of the Scree test (Cattell, 1966) and oblique (direct Oblimin) rotation to simple structure. The

15 14 detailed factor analytic guidelines recommended by Cattell (1978), and by Gorsuch (1983) were followed closely. The problem with exploratory factor analysis (EFA) methods, however, is that the results are data-driven, rather than being conceptualy-driven as in hypothesis testing confirmatory (CFA) methods. According to Rowe and Rowe (in press), the use of exploratory/unrestricted factor analytic methods more often than not results in arbitrary solutions which serve only to conflate theory, amounting to little more than "an undisciplined romp through a correlation matrix" (cf. Hendrickson & Jones, 1987, p. 105). Claims of substantive insights from exploratory factor analyses (which pervade the extant psychological and social sciences literature) are often misplaced since the reported factor analytic findings are frequently based on statistical artifact. As Boyle (1988) has pointed out, use of exploratory factor analytic methodology is often applied inadequately, involving inappropriate strategies. In contrast to exploratory methods of analysis, use of LISREL congeneric and confirmatory methods enables testing of hypothesized models. With regard to the congeneric factor analyses for each of the eight MDQ subscales, the matrix ofpolychoric correlation coefficients was computed using Jöreskog and Sörbom's (1988) PRELIS program (cf. Olsson, 1979; Poon & Lee, 1987). The intercorrelation matrices obtained for each of the eight separate subscales were then subjected to congeneric factor analysis via SIMPLIS-a two-stage least squares plus maximum-likelihood estimation procedure (Jöreskog & Sörbom, 1989b). Separate congeneric analyses allowed examination of the contribution of each item to the respective MDQ subscales. The confirmatory factor analysis (CFA) was based on the productmoment correlations for the 'best' 24 MDQ items, using the full LISREL 7

16 15 program (Jöreskog & Sörbom, 1989a). There were two peculiarities of this analysis. First, the various goodness of fit indicators obtained when all 47 MDQ items were included in the analysis were less than adequate. However, this is not unique to the MDQ, but is characteristic of confirmatory factor analyses in general. Confirmatory methods do not appear to handle large numbers of variables very well. Indeed, the examples provided in the LISREL User's Guide are limited to analyses of small numbers of variables only. On the basis of the standardized regression equations for the respective MDQ items, only the best three items per subscale (having the highest regression loadings) were included in the subsequent CFA. Second, when the polychoric correlations were employed, the goodness of fit indicators were significantly poorer than when the productmoment correlations served as the starting point. SIMPLIS and LISREL 7 analyses were therefore undertaken on the basis of the Pearson product-moment correlation coefficients. This not only enabled a more comprehensive test of the purported MDQ factor structure, but also allowed comparison of the efficacy of the simplified version as compared with the full LISREL 7 program. The measurement model for the CFA can be expressed algebraically as: X= Axξ + δ, where the MDQ items are presented by the xs, and the subscales (latent variables) are signified by the (s, respectively. Measurement errors relating to the x-variables are represented by the 6 terms. The concomitant covariance matrix is expressed as:

17 16 where represents the matrix of loadings for the MDQ subscales, Φ represents the matrix of latent trait covariances and represents the matrix of error variances/covariances related to the x variables (MDQ items) (cf. Cuttance & Ecob, 1987). Results and Discussion A. Descriptive statistics Since the MDQ items were scored on a five-point self-rating scale (0-4), the data were highly positively skewed with most subjects reporting little discomfort in terms of their menstrual cycle symptoms and associated moods. This was expected, given that the present sample comprised predominantly healthy, young women. The 47 x 47 matrix of product-moment correlations for the MDQ item responses, indicated that in general, the magnitude of most coefficients was low to moderate only.* This attenuation was almost certainly due to the restriction in variance associated with the significant skewness of the data. It is likely that several of the MDQ items would be more highly intercorrelated among normally distributed data (perhaps even exhibiting some degree of multicollinearity), than was evident in the present study. B. Exploratory factor analysis The Scree test suggested that 9 factors should be extracted, while the K-G criterion suggested that 10 factors should be extracted. The Kaiser-Meyer-Olkin measure of sampling adequacy (an index of the observed vs partial correlations) was 0.922, indicating that it was appropriate to subject the matrix of MDQ item intercorrelations to factor analysis. An algebraic definition of the KM

18 17 Table 1 Oblique factor pattern solution Factor No MDQ item h2 Negative Affect Water Retention Control Factor Arousal Factor Impaired Concentration Pain Factor Autonomic Reactions Pseudospecific Factor Behavior Change Variance (%): Eigenvalue: Hyperplane count (±0.10): Notes. Only significant factor loadings ( 0.30) are shown. Factor loadings are reported to two decimal places only.

19 18 index was given by Norušis (1985, p. 129). Likewise, Bartlett's Test of Sphericity was (P < ), indicating that the intercorrelation matrix was not an identity matrix (cf. Norušis, p. 128). Following the recommendations of Cattell (1978), the final rotated factor pattern solutions associated with extraction of eight, and 10 factors were also examined for their approximation to simple structure. The ±0.10 hyperplane counts for the eight-, nine- and ten-factor solutions were 64.63, and 65.11%, respectively. Nevertheless, inspection of the derived factor pattern solutions indicated that the nine-factor solution was the most appropriate for the present data set. In the eight-factor solution, Factor 7 was a pseudospecific factor having only a single Table 2 Intercorrelations of derived factors Factor I O.D O.Q Notes. Correlations are shown to two decimal places only. loading For the 10-factor solution, Factor 3 loaded a combination of items for both the Behavioral Change and Impairment Concentration subscales. Additionally, the Negative Affect dimension was split into two separate factors (Factors 1 and 10), as was the Behavioral Change dimension (Factors 3 and 9), indicating that over-extraction of factors had occurred. The nine-factor pattern solution is presented in Table 1 (showing only those factor loadings of 0.30 or greater). Although nine factors were extracted, requiring 33 iterations for convergence of the direct Oblimin solution, Factor 8 did not exhibit any

20 19 practically (as opposed to statistically) significant loadings. The remaining eight factors corresponded closely with the eight MDQ subscales proposed by Moos (1968, 1977, 1985). Factor 1 accounted for 29.0% of the variance associated with the unrotated principal components. This factor loaded on all eight of the Negative Affect items (Loneliness, Anxiety, Mood Swings, Crying, Irritability, Tension, Feeling sad or blue, Restlessness), thereby clearly defining the Negative Affect dimension. Also there were significant loadings on Item 23 (Insomnia ), as well as on Item 31 (Confusion ), both of which may be inappropriate items for the Impaired Concentration subscale, within which they were located by Moos (1968). However, as they had the lowest loadings for Negative Affect, it is apparent that they are "vestigial only" and should not be considered for relocation into this particular MDQ subscale. Factor 2 (accounting for 5.8% of the variance) loaded highly on Items 11 and 15 (0.68 and 0.82) of the Water Retention subscale, along with Item 30 (Cramps) and Item 47 (Increased appetite). The loading on Item 2 (Weight gain) was 0.21, whereas that for Item 6 (Skin blemish/disorder) was only While clearly supporting the Water Retention dimension as defined by Moos (1968), since the factor did not load at all on Item 6, it is likely that this item was incorrectly placed within the Water Retention subscale. Nevertheless, on the basis of the present evidence, with only two items defining the Water Retention dimension, it would seem that this MDQ subscale should be revamped with inclusion of additional new items. Factor 3 (accounting for 5.2% of the variance) loaded significantly on all six of the Control items (Feelings of suffocation, Chest pains, Ringing in the

21 20 ears, Heart pounding, Numbness/tingling, Blind spots/fuzzy vision) providing strong confirmation of the validity of this MDQ subscale. In addition, there were significant loadings on Item 16 (Hot flushes ), Item 44 (Minor accidents--0.44) and Item 46 (Poor motor coordination ). It is likely that these last three items were inappropriately included in the Autonomic Reactions and Impaired Concentration subscales, respectively. Factor 4 (involving 4.2% of the variance) likewise loaded on all of the designated Arousal subscale items (Affectionate, Orderliness, Excitement, Feelings of well-being, Bursts of energy/ activity). This finding strongly supports the construct validity of the Arousal dimension as measured in the MDQ instrument. However, the subscale might be relabelled to indicate the S's positive enthusiasm. Factor 5 (3.6% of the variance) loaded significantly on only three of the designated Impaired Concentration items (Forgetfulness, Difficulty concentrating and Distractible). Since no fewer than eight items had been allocated to the Impaired Concentration subscale, it is clear that this factor dimension was not well defined among the MDQ responses of the present sample. Impaired Concentration may only be a major concomitant of menstrual cycle functioning in cases with pronounced clinical symptoms. As for the remaining factors, Factor 6 (3.0% of variance) loaded significantly on three of the six items comprising the MDQ Pain subscale (Muscle stiffness, Backache, General aches and pains). Therefore, among the present healthy, young sample, pain was a noticeable menstrual cycle feature (sufficient to be represented by a single factor). Factor 7 (2.7% of variance) loaded significantly on three of the four Autonomic Reactions items, namely

22 21 (Dizziness/faintness, Cold sweats and Nausea/vomiting), providing good support for this MDQ subscale. It also loaded significantly on Item 5 (Headache). Factor 8 (2.6% of variance) did not exhibit any conceptually significant loadings on MDQ items. Factor 9 (2.3% of variance) loaded significantly on four of the five Behavior Change items, providing good support for this MDQ subscale dimension. The intercorrelations of the nine derived factors are presented in Table 2. As is evident, Factor 1 (Negative Affect) correlated significantly with no fewer than seven other factors. On the other hand, Factor 4 (Arousal) exhibited no significant correlations with any of the remaining factors. The finding that the MDQ subscales were somewhat correlated is consistent with the view that menstrual cycle moods and symptoms rise and fall concurrently. C. Congeneric factor analyses In view of the frequent doubts raised about factors derived from EFA (cf. Anderson, 1987; Bentler, 1985; McDonald, 1980; Muthén, 1984) congeneric factor analyses for the items in each of the separate MDQ subscales were carried out using the LISREL program (Jöreskog & Sörbom, 1989a). In effect, the congeneric factor analyses amounted to single-dimension confirmatory factor analyses. An initial PRELIS analysis (Jöreskog & Sörbom, 1988) was undertaken in order to compute the 47 x 47 matrix of polychoric correlation coefficients. When the data are ordinal, polychoric coefficients generally have less bias associated with them than do product-moment correlation coefficients (cf. Jöreskog & Sörbom, 1988; Olsson, 1979; Poon & Lee, 1987). Since MDQ item responses were measured on a five-point Likert-type ordinal scale, polychoric

23 22 correlation coefficients were computed, followed by use of the LISREL maximum-likelihood estimation procedure to calculate the various goodness of fit parameters, including chi-square, the goodness of fit index (GFI), the adjusted goodness of fit index (AGFI)-adjusted for the number of degrees of freedom, and the root mean square residual (RMR)-an index of the degree to which the initial correlation matrix is not reproduced by the estimated factor model. While the Χ 2 statistic is directly influenced by sample size, the AGFI index (which is independent of sample size) together with the RMR (due to differences between the obtained and predicted covariance matrices) are the important statistics. According to Cuttance (1987, p. 260), "models with an AGFI of less than 0.8 are inadequate... most acceptable models would appear to have an AGFI index of greater than 0.9." The congeneric factor analytic results for each of the eight MDQ subscales are shown in Table 3. For the Pain subscale, initial estimates were provided by the two-stage least squares solution. The corresponding maximum likelihood GFI and AGFI estimates were both quite high, being and respectively, while the RMR was The total coefficient of determination was 0.964, suggesting that most of the communality in the Pain subscale had been accounted for adequately. Given these results, it is apparent that the construct validity of the Pain subscale is supported. Perusal of the standardized regression coefficients, along with the associated error variances and multiple R 2 estimates, indicated that a certain amount of measurement "noise" was being contributed to the Pain subscale by MDQ Items 5, 14 and 19. Accordingly, it was decided to remove these three items from the subsequent CFA (see below).

24 23 The congeneric estimates of the Water Retention subscale were also quite good, although somewhat less so than were those for the Pain subscale. The maximum likelihood GFI, AGFI and RMR estimates were 0.964, and 0.075, respectively. In this instance, the Likelihood Ratio Test (LRT) statistic (Χ 2 ) decreased from to (2 df) in going from the two-stage least squares to the maximum likelihood solution, suggesting a qualitative improvement in the fit of the congeneric model. The total coefficient of determination was 0.793, indicating that the communality in the Water Retention subscale had been accounted for moderately. Taken together though, the Water Retention subscale would appear to be a valid dimension, even among healthy, young women, although the fit to the congeneric factor model was somewhat imperfect. From the standardized regression equations, it was clear that Item 6 was contributing least to the measurement of the Water Retention subscale. The Autonomic Reactions subscale also exhibited a less than optimal fit of the congeneric model. The maximum likelihood GFI, AGFI and RMR estimates were 0.958, and 0.041, respectively. The total coefficient of determination was Perusal of the standardized regression equations indicated that Item 16 contributed least to the measurement of this subscale.

25 24 Table 3 Congeneric factor models for MDQ subscales Standardized LISREL estimates (ML) (AX) MDQ items (x variables) Parameter value Standard error Significance t-value R> Pain I 0.68 O.Ql < < < < < < Coefficient of determination for x variables is Goodness of fit statistics: GFI = 0.968; AGFI = 0.925; RMR = Water Retention O.Dl < < II < < Coefficient of determination for x variables is Goodness of fit statistics: GFI = 0.964; AGFI = 0.821; RMR = Autonomic Reactions O.Ql < < < < Coefficient of determination for x variables is Goodness of fit statistics: GFI = 0.958; AGFI = 0.791; RMR = Negative Affect < < < < < < < ,07 < Coefficient of determination for x variables is Goodness of fit statistics: GFI = 0.884; AGFI = 0.791; RMR = Impaired Concentration < < < < < < < < Coefficient of determination for x variables is Goodness of fit statistics: GFI = 0.901; AGFI = 0.822; RMR = Behavior Change < ,07 < O.o? < O.o? < < Coefficient of determination for x variables is Goodness of fit statistics: GFI = 0.926; AGFI = 0.778; RMR Arousal O.Dl < < < < < Coefficient of determination for x variables is Goodness of fit statistics: GFI = 0.974; AGFI = 0.923; RMR = Control O.QJ < < < < < < Coefficient of determination for x variables is Goodness of fit statistics: GFI ; AGFI = 0.911; RMR = Notes. The 'statistical significance' of a t -value refers to the significance of the ratio of the unstandardized parameter estimate to its standard error. GFI =goodness of fit index; AGFI- adjusted goodness of fit index; RMR =root mean square residual.

26 25 Factor structure of the MDQ 11 Table 4. Confirmatory factor analysis of MDQ items Standardized LISREL maximum likelihood estimates (.U) Fact MDQ or items hl Pain Water Retention II Autonomic Reactions Negative Affect ! Impaired Concentration Behavior Change Arousal Control Notes. Coefficient of determination = I.000; GFI =0.906; AGFI =0.874; RMR= As for the Negative Affect subscale, the various model-fit estimates were also less than optimal. The maximum likelihood GFI was 0.884, the AGFI was 0.791, while the RMR was Nevertheless, the LRT Χ 2 statistic declined in magnitude from to (20 df) in going from the two-stage least squares to the maximum likelihood solutions, thereby suggesting at least a qualitative improvement in the fit of the congeneric factor model. The total coefficient of determination was 0.913, which was an encouraging result. From the standardized regression equations, Items 8, 13, 17 and 22 contributed least toward measurement of the Negative Affect subscale.

27 26 The Impaired Concentration subscale also exhibited less than optimal goodness-of-fit estimates. The GFI and AGFI were and 0.822, respectively, while the RMR was The total coefficient of determination was Examination of the standardized regression equations suggested that Items 23, 27, 36 and 44 provided the least contribution toward measurement of the Impaired Concentration subscale. These items would need to be removed from the subsequent CFA. Likewise, the maximum likelihood estimates for the Behavior Change subscale were not entirely satisfactory. The GFI, AGFI and RMR estimates were 0.926, and 0.054, respectively, indicating an imperfect fit of the congeneric model. The Total Coefficient of Determination was Perusal of the standardized regression equations revealed that Items 28 and 23 contributed the least communality to the Behavior Change subscale. As for the Arousal subscale, the various goodness-of-fit estimates were quite satisfactory. The maximum likelihood GFI, AGFI and RMR estimates were 0.974, and 0.041, respectively, indicating an excellent fit of the congeneric model, and good construct validity for this subscale. The total coefficient determination was Even so, the standardized regression equations indicated that Items 25 and 29 should be removed from the subsequent CFA, since they were contributing mostly 'noise' to the measurement of the Arousal dimension.

28 27 Table 5 Covarianccs between exogenous latent traits ( Φmatrix) MDQ subscale PA WR AR NA JC BC AL co Pain Water Retention 0.59 Autonomic Reactions Negative Affect lmpaired Concentration Behavior Change Arousal Control Notes. Coefficients are shown to two decimal places only. Pain, PA; Water Retention, WR; Autonomic Reactions, AR; Negative Affect, NA; Impaired Concentration, JC; Behavior Change, BC; Arousal, AL; Control, CO. Finally, with regard to the Control subscale, the maximum likelihood estimates were also very good. The GFI was 0.962, the AGFI was 0.911, while the RMR was 0.037, indicating an excellent fit to the congeneric model. The total coefficient of determination was However, Items 26, 39 and 43 exhibited excessively high error variances, suggesting they should be removed prior to carrying out the subsequent CFA. In the main, the results of the congeneric factor analyses for the MDQ subscales support the construct validity of the eight separate dimensions among normal, healthy women, although the Autonomic Reactions, Negative Affect, and Behavior Change dimensions were less clearly defined in terms of fit to the congeneric model. Furthermore, in every instance, it was shown from the associated standardized regression equations that a number of the MDQ items were not contributing adequately to measurement of common variance in the respective subscales. These 'noisy' items are therefore removed from the subsequent CFA in order to test the overall MDQ model more efficiently. D. Confirmatory factor analysis Only the product-moment intercorrelations of the 'best' 24 MDQ items

29 28 (three per subscale), as determined from the standardized regression equations associated with the congeneric factor analyses, were utilized for the CFA, using both the SIMPLIS (Jöreskog & Sörbom, 1989b) and also the full LISREL 7 program (Jöreskog & Sörbom), 1989a), as outlined in the Data Analysis section above. Results of the maximum likelihood estimation via SIMPLIS were not entirely satisfactory. In this instance, the GFI was 0.829, the AGFI was 0.771, while the RMR was Importantly the total coefficient of determination was unable to be estimated, and negative variance estimates (Heywood cases-a serious problem in parameter estimation) occurred for two of the variables, indicating that the SIMPLIS program was producing an erroneous result (cf. Cuttance & Ecob, 1987, p. 174). In order to avoid this computational limitation of the SIMPLIS program, the power of the full LISREL 7 program was invoked. The results from this more sophisticated analysis were quite supportive of the purported structural dimensionality of the MDQ instrument. In this instance, the GFI was 0.906, the AGFI was 0.874, while the RMR was a very satisfactory All of these goodness of fit results suggested that the eight-factor MDQ model is a viable one. Moreover, the total coefficient of determination was now indicating that the common factor variance had been accounted for very well (see Table 4). Likewise, the covariances between the exogenous latent traits/mdq subscales ( ) are shown in Table 5. Interestingly, Table 5 indicates that the covariances between the MDQ subscale dimensions were quite moderate to very low, suggesting that the eight MDQ factors represent essentially separate dimensions, as purported by Moos (1968, 1985).

30 29 Conclusions Overall, despite having certain limitations psychometrically, it is evident from the above findings that the MDQ is a reasonably reliable and valid instrument. Results from the EFA indicated that no fewer than seven of the eight MDQ subscale dimensions emerged as separate, but correlated factors. Only the Impaired Concentration dimension failed to emerge as a distinct factor, Had the sample been comprised of a clinical sample of women with premenstrual syndrome, it is likely that this dimension would have emerged as a separate entity also. Using congeneric analyses, it was found that the eight MDQ subscale dimensions exhibited adequate goodness of fit estimates. As for the Impaired Concentration subscale (which did not emerge from the EFA), the AGFI was (RMR = 0.055), suggesting the structural unity of this subscale dimension. Perusal of the standardized regression equations for each congeneric analysis indicated that in most instances, a number of items might be removed from the various MDQ subscales in order to reduce the amount of "measurement noise". Finally, an overall CFA based on item intercorrelations, using the full LISREL 7 procedures, showed that the AGFI was (RMR = 0.056), and supported the purported eight-factor structure of the MDQ instrument. Therefore, while further research might profitably be devoted to refining the item content of the MDQ, the structural dimensionality of the existing version of the MDQ appears reasonably well confirmed. Acknowledgement Preparation of this paper was assisted by receipt of a research grant (No. A791949) from the Australian Research Council.

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