Confirming the theoretical structure of the McGill pain questionnaire in acute clinical pain

Pain, 46 (1991) 53-60 0 1991 Elsevier Science Publishers ADONIS 030439599100153P

53 B.V. 0304-3959/91/$03.50

PAIN 01806

Confirming the theoretical structure of the McGill Pain Questionnaire in acute clinical pain Nancy K. Lowe ‘, Susan Noble Walker ’ Dept.

’ and Robert

C. MacCallum

b

of Life Span Process, College of Nursing, and ’ Dept. of Psychology, Ohio State I/nicer&y, Columbus, OH 43210-1289 (U.S.A.), and ’ College of Nursing, Unic’ersity of Nebraska Medical Center, Omaha, NE 68198-5330 (U.S.A.) (Received

1 December

1989, revision

received

26 November

1990, accepted

17 December

1990)

Based upon a tripartite theoretical model of pain, the Pain Rating Index (PRI) of the McGill Pain Summary Questionnaire (MPQ) continues to be one of the most frequently used instruments to measure clinical pain. Although a number of exploratory factor analytic studies have failed to consistently support the theoretical structure of the instrument, one previous confirmatory factor analytic study of chronic pain did statistically support the a priori model. Because it has been suggested that acute pain may not involve the same dimensions as chronic pain, this study provided a direct test of the theoretical structure of the MPQ through multi-sample confirmatory factor analysis (CFA) using data provided by women experiencing pain during labor (n = 185) and women experiencing acute postoperative pain (n = 192). Results of the LISREL CFA analysis indicated that the a priori, 3-factor, oblique model originally proposed by Melzack provided the most parsimonious representation of the data across the 2 samples of acute pain. Key words: Pain; Labor pain; Post-operative

pain; McGill Pain Questionnaire;

Introduction

For the clinical researcher who embraces a multidimensional conceptualization of pain, the measurement of acute pain during experiences such as labor, trauma, or postoperative recovery continues to be an unresolved methodological dilemma. Investigation of the subjective phenomenon of pain, its accompanying psychological and physiological aspects, and a multitude of care/treatment related variables within the context of acute situations demands measurement strategies which provide the most information regarding the phenomenon of pain with the least possible intrusion and subject demand. The Pain Rating Index (PRI) of the McGill Pain Questionnaire (MPQ) [131 is the most well known instrument that provides a quantification of pain on more than an intensity dimension. Although it can be administered in 5 min or less by interview, even

Correspondence to: Nancy K. Lowe, Ph.D., R.N., College of Nursing, Ohio State University, 1585 Neil Avenue, Columbus, OH 43210-1289, U.S.A.

Confirmatory

factor analysis

5 min may be burdensome to study participants in situations such as the transitional stage of labor [121. Theoretically linked to the Gate Control Theory of Pain [14] in which pain is conceptualized as a sensorydiscriminative, motivational-affective, and cognitiveevaluative phenomenon, the MPQ consists of the 6level Present Pain Intensity Scale (PPII and the PRI. Twenty subclasses of qualitatively and quantitatively ordered verbal descriptors comprise the PRI, 16 of which are designed to measure the 3 interrelated yet distinct dimensions of pain: sensory, affective, and evaluative [13]. The remaining 4 sets of descriptors, which are only used in the calculation of the total PRI score, are classified as miscellaneous. Although the total PRI score can be used as an overall measure of pain, the use of the sensory, affective, and evaluative subscales provides a mechanism to study a variety of variables differentially in relationship to these 3 dimensions of pain. The validity of any such investigation, however, is initially dependent on the construct validity of the subscales themselves. One approach to the study of the validity of multidimensional instruments is the investigation of the underlying structure through factor

analysis. Although a number of’ factor analytic studies of the MPQ have appeared in the Iiteraturc. all but one have taken an exploratory approach and support for the theoretical structure underlying the PRI has been equivocal. The purpose of this study is 10 pi-ovidc a direct empirical test of the thcorctical model of the PRI through multi-sample confirmatory factor analysis in 2 snmplcs of women expcricncing acute clinical pain. In a mixed clinical-cxpcrimcntal pain sample of 214. C’rockct et al. [h] identified 5 factors from the 30 PRI items using ;I principal component analysis (PC-A) with varimax rotation. Since none of the factors idcntificd fit the a priori classification of the pain descriptors, the factors wcrc named according to their component items: Immediate Anxiety factor. Perception of Harm factor. Somcsthctic Pressure dimension. (‘utancous Sensitivity dimension, and Sensory Information factor. Each of these factors wcrc combinations of items previously defined as sensory. affective. or evaluative by Melzack and Torgerson [l5]. The hctcrogeneity of the sample used in (‘rocket et at.‘s study as well as differences in administration of the MPQ within the sample cast some doubt on the reliability of the factor structure identified for those data. Using a modified format of the MPQ in which the verbal descriptors were administered to 13 I patients with back pain in a randomly ordcrcd sequcncc and employing a PCA with varimax rc>tation. Leavitt et al. [IO] idcntificd 7 factors underlying the subjects’ rcsponscs. Sensory descriptors constituted 5 of the factors derived. while combinations of \cnsory and affcctivc descriptors defined the final 2 factors. Although Lcavitt et al.‘s data provided some support for the structure of the MPQ. the modification made in the form of the instrument prohibits the inference that the extracted factors represent the pain dimensions theoretically represented by the MPQ. A similar analysis was used by Reading [IX] to study the MPQ responses elicited from 166 women experiencing dysmenorrhea. Although 4 factors accounting for 7Y.65 of the total variance wcrc derived, only I I of the 20 MPQ subclasses contributed to the factor solufactors tion with 2 sensory and 2 affective reaction described. Neither Leavitt et al. [IO] nor Reading [IX] found empirical support for the evaluative dimension described by Melzack. When responses to the MPQ of 10X patients with low back pain were factor analyzed by Prieto et al. 1171. 4 dimensions which more closely resembled the original classification of the instrument’s items were extractcd by the principal factor method with varimax rotation. The factors identified explained 51% of the total variance and wcrc described as sensory pressure. affective-sensory. and punishing affect. A evaluative. I982 report attempted to cross-validate Prieto ct al.‘\ results in ;I similar population of W patients with low

back pain [3]. Four somewhat similar tactor\ \cerc identified explaining 55 ‘? of the total variance: scnsorh pressure, evaluative-affective-sensory. sensory prcs\urc-punishing affect, and \cnsory t hcl-mal-misccllancous affcctivc. No “put-c” eialuativc or affcctivc Lrctars were identified in the data. The primary sensory factor identified in each of those 2 studies of low back pain patients were composed of similar items and wcrc rclatcd 0.83 by a coefficient of congruence. A final exploratory factor analytic study of the MPQ was rcportcd by Reading [ IY] using a sample of Y5 postpartum women with acute post-episiotomy pain. Analysis of those data by PCA (no rotation specified) extracted h poorly differentiated factors which cxplaincd 65% of the total variance and reflected specific sensory qualities and combined emotional-sensory dimcnsions of pain. The author suggested that acute pain may bc conceptually different from chronic pain “involving less differentiation of sensory, affective and evaluative tanguagc dimensions” (p. 185). It should bc noted that in each of these exploratory factor analytic studies orthogonal methods of rotation were used. These procedures do not allow the factors to correlate which is inconsistent with the original conceptualization of the PRI subscales. In contrast to the mixed findings using exploratory factor analysis. a confirmatory factor analytic study of 2 chronic pain populations supported statistically the theoretical tripartite factor structure of the MPQ [‘I]. That investigation independently tested the MPQ model in the 2 samples and secondarily examined the invariance of the factor structure across the 2 samples. Turk and associates reported that the MPQ data were adequately explained by the thcorctical model in both samples and the structure of the factors was statistically equal across the 2 groups. The strength of these findings is limited, however. by the number of subjects in each sample (n = 70 and n = YX) which fell far short of ;I minimum of 10 times as many observations as variables for confirmatory factor analysis (C’FAJ rccommended by Nunnally [If)], or of Boomsma’s [2] recommendation of at least 200 observations for structural modcling. Additionally, the lack of uniqueness identified for the covariance matrices when the 3 samples were compared raises a question as to the appropriatcncss of multi-sample CFA. Contrary to the authors’ interpretation, multi-sample CFA rests on the testing of the theoretical model across two or more distinct samples [S]. In other words, equality of covariancc matrices indicates that the 2 samples arc not distinct and that the data should bc pooled and analyzed by sin&-sample methods. In response to the inconsistency of results rcportcrl in the exploratory factor analytic studies of the MPQ. to the potential conceptual difference between chronic and acute pain. and to the limitations described for the

55

Turk et al. report, this study tested the theoretical model of the MPQ in 2 distinct samples experiencing acute clinical pain by means of CFA.

Methods Subjects

Two separate and distinct acute pain populations were sampled in a convenience fashion for this methodological investigation. The first (labor) sample, obtained from the labor and delivery suite of a community hospital in a suburb of a large U.S. midwestern city, consisted of 185 parturient women at term with a singleton, obstetrically normal pregnancy. The women had a mean age of 28.2 years (range l&42), were predominantly Caucasian (95%) and well educated with 74.5% reporting a least some college. Forty percent of the women (n = 74) were delivering their first child, 43.2% (n = SO>their second, and 16.8% (n = 31) their third to eighth child. In order to maintain homogeneity of gender, the second (postoperative) sample was obtained from the postpartum and gynecological surgical units of a comprehensive university hospital in another midwestern U.S. city. This sample consisted of 192 women who were experiencing postoperative pain 12-76 h (X = 45.5) after abdominal surgical procedures such as cesarean section (n = 136) or gynecologic procedures for non-malignant diagnoses (n = 56). These women ranged in age from 18 to 56 (X = 30.6), were also predominantly Caucasian (66.7%) and well educated with 105 subjects (54.6%) having at least some college education.

For both samples, informed consent was obtained prior to data collection. As in the Turk et al. [211 study, only the first 16 subclasses of the PRI were included in the CFA. Subclasses 17 through 20 were excluded from analysis since they are labeled as miscellaneous items [13] and are not classified according to the theoretical conceptualization of pain as a sensory, affective, and evaluative phenomenon. Although no definitive recommendation for sample size critical to the conduct of CFA has been identified in the literature, each independent sample in this study meets the minimal standard of 10 subjects per variable suggested by Nunnally [16] for CFA by the maximum-likelihood method and closely approximates the 200 minimum suggested by Boomsma

Dl. Results Descriptit,e statistics

In the CFA, the 16 items of the PRI were considered separate observed variables from which the latent (sensory, affective, and evaluative) variables were estimated. Table I presents the descriptive statistics for each of the 16 items in the labor and postoperative samples. Coefficient alpha reliability estimates calculated for the 2 samples were 0.68 and 0.70 for the sensory subscale and 0.71 and 0.81 for the affective subscale in the labor and postoperative samples respectively. Since only 1 item comprises the evaluative subscale, no alpha coefficient could be calculated. Covariante matrices computed from the 16 items for each independent sample were used to conduct the CFA via maximum-likelihood (ML) LISREL.

Procedure

For the labor sample, responses to the MPQ were obtained from the subjects at a mean cervical dilatation of 5 cm (range 2-9). The women responded to the MPQ administered by interview by a registered nurse research assistant in the intervals between uterine contractions. On the PPI scale of the MPQ, 18 (9.7%) of the subjects described their current contraction related pain as “mild,” 65 (35.1%) as “discomforting,” 58 (31.4%) as “distressing,” 29 (15.7%) as “horrible,” and 15 (8.1 %o)as “excruciating.” Data also were collected by interview by a registered nurse research assistant from the women experiencing postoperative pain. Postoperative pain was measured after the discontinuation of any patient-controlled intravenous or epidural analgesia and at least 2 h after the administration of any other analgesic medication CX = 5.4 h, range 2-45 h). On the PPI, 60 (31.3%) of the subjects described their pain as “mild,” 94 (49%) as “discomforting,” 21 (10.9%) as “distressing,” 11 (5.7%) as “horrible,” and 6 (3.1%) as “excruciating.”

Multi-sample confirmatory factor analysis

The CFA model evaluated by LlSREL can be best illustrated by a path diagram (Fig. 1) for the theoretical model of the MPQ. According to the model, the sensory dimension (a latent variable) is measured by the first 10 PRI subclasses (observed variables), the affective dimension by the next 5 subclasses, and the evaluative dimension by the 16th subclass. Each of these relationships is illustrated by an arrow linking the observed variable to the latent variable and is represented statistically by a corresponding factor loading to be reported later. As indicated by the double-headed arrows in the figure and statistically represented by correlation coefficients among the latent variables, the 3 dimensions of pain are theoretically intercorrelated. The single headed arrows to the observed variables in the diagram represent the “unique” (error) portion of each measured variable, i.e., that portion not accounted for by the corresponding latent variable. The variance of each of these unique terms is a parameter

1

1

Thermal

diagram

for theoretical

structure

1+--

Brlohtness I+-Dullness

Fig. I. Path

ante matrices indicated that the sample data could not be pooled and multi-sample CFA was appropriate. Subsequent steps in the analysis were conducted by employing the mechanism of “equality constraint\” on model parameters, i.e.. each of the model parameters defined in the path diagram can be estimated under the constraint that its value be equivalent in the 17 groups. By testing a sequence of models that impose successively more constraints on the parameters. one can evaluate not only the degree to which the model holds in each group, but also the possibility that certain parameters have the same value in each group. The goal of the analysis is to find the most parsimonious or constrained model that fits the data adequateiy well. Thus, subsequent steps in the analysis provided tests ot the theoretical model of the MPQ by placing increasingly greater constraints on the LISREL parameter estimates through successive or nested hypotheses. For each successive model. the hypothesis tested was whether the designated parameters were invariant across the 2 samples. Each hypothesis was evaluated through several indicators of goodness of fit of the proposed model to the data. This evaluation consisted of 6 indicators as appropriate: ( 1) A chi-square statistic that assesses the probability that the model defined holds exactly in the populations with a non-significant x’ indicating that the model is plausible. It must be recognized, however. that the statistical hypothesis tested by the x’ statistic is a very rigid one and is commonly rejected in practice, especially when sample sizes are large [ 11or when assumptions of multivariate normality of the distribution of the observed variables is violated ICI].

of the

McGill

+---

Pain

questionnaire.

which is also estimated by LISREL. In the modet, the observed variable “evaluative” does not have a unique or error term since it is the only item designed to measure the evaluative dimension. All of its variance is, therefore, accounted for by the latent variable of the evaluative factor. In order to evaluate the degree to which this model holds in the 2 separate samples, a technique and strategy proposed by Jgreskog 181were employed. The first step in the analysis was to determine whether the data from the 2 samples needed to be kept separate or could be combined. This goal was accomplished by testing the equality of the covariance matrices of the 2 samples. As indicated by the x2 for the first entry in Table Ii, the hypothesis was rejected establishing the independence and distinctiveness of the 2 pain samples. The difference identified between the 2 covari-

TABLE DATA

I SUMMARY

FOR

I6 PAIN RATING

INDEX

(PRI)

SUBCLASSES

IN TWO ACUTE

PAIN

SAMPLES

Post-op

sample fN = 192)

Possible range

Labor sample (N = 185) Range

M

SD.

Range

M

S.D.

Temporal

Q-h

O-h

7.63

2.04

Spatial

O-3

Ok 3

1 Sh

I .M

O-6 11-3

Punctate pressure incisive pressure Constrictive pressure Traction pressure Thermal Brightness Dullness Sensory miscellaneous Tension Autonomic Fear Punishment Affective misceltaneous Evaluative

O-S o-3 o-s o-3 o-4 o-4 o-5 U-4 U-2 U-2 o-3 (f-5 o-2 o-5

o-s O-3 o-5 O-3 U-4 U-4 U-5 U-4 U-2 O-7 U-3 o-s o-2 O-S

2.30 0.92 3..V

I .82

o-5

0.70 1.18

o-s

2.3s I .20 1.X6 0.89 2.86 1.57 0.85 2.20 2.89 1.34 I .Oh 0.42 0.48 0.51 U.25 2.08

I .x5 I .26 1.73 0.77 1.45 O.YO I .0-l 1.h4 1.20 (1.YI (1.70 o.fit U.84 1.12 0.53 I .31

Subclass

158

1.3

O.hU I .4x 3.73 I.66 1.U8 0.40 0.74 KY7 0.33 3.15

O.Yh l.h.4

1.33 I .41 0.75 0.57 (I.83

1.39 U.61 I .47

II-3 o-3 C-4 o-4 o-5 O--4 O-2 Ok2 O-3 o-5 O-2 (l-5

_-

TABLE II SUMMARY OF MULTI-SAMPLE Hypothesis: equality of

Covariance matrices Three factor pattern Factor loading matrices Factor correlation matrices Residual error matrices

CONFIRMATORY

X2

268.48 299.09 327.19 330.33 442.10

df

136 204 220 223 238

Ratio x2/df

P

< < < < <

FACTOR ANALYSIS

0.001 0.001 0.001 0.001 0.001

1.47 1.49 1.48 1.86

GFI a

0.903 0.898 0.896 0.859

p b

0.917 d - 0.001 0.000 - 0.067

x2 change test X2

df

change

change

28.10 3.14 111.77

16 3 15

a Goodness of fit index. b Rho statistic (estimate of increment in fit obtained by successive models). ’ Overall decision based upon evaluation of multiple indicators of fit. d This rho statistic is the increment of fit obtained by using 3 factors compared to a null model of independence * Non-significant, P > 0.025. ** Non-significant, P > 0.05.

(2)A chi-square to degrees of freedom ratio. Ratios in the range of 2 or 3 to 1 or smaller are indicative of an acceptable fit between the hypothetical model and the observed data [5]. (3) A goodness of fit index (GFI) [20] which should range between 0 and 1 with high values (greater than 0.9) being associated with good fit of the model. (4) A rho statistic (p) [l], a non-normed index which also assesses the relative fit of the model (range O-l with high values indicative of good fit) and can also be compared between models to represent the increment of change in fit of successive nested models. The rho statistic is less sensitive to sample size than the GFI. (5) Inspection of the squared multiple correlation @MC) for each observed variable. SMCs indicate how well an observed variable serves as a measurement instrument for the latent variable with large values (range O-1.0) being associated with good fit [9]. (6) Inspection of the matrix of standardized residual covariances for values in excess of k2.0. If only random errors remain, less than about 5% of the standardized residuals should fall outside of this range [7]. The initial test of the theoretical model across the 2 samples is the null hypbthesis of a 3-factor pattern with no equality constraints on parameters (Table II). It should be noted that the overall x2 obtained for this hypothesis is the sum of the 2 x’s that would be generated if the 2 groups were analyzed separately by single-sample CFA. All indicators of fit for this hypothesis indicate that the 3-factor pattern is plausible across the 2 samples. Assuming that the factor pattern is invariant across the 2 samples, the next hypothesis constrains the model further by testing equality of the factor loading matrices. Since this model is nested in the preceding model, the appropriate evaluation of the null hypothesis is no longer the overall chi-square but rather the chi-square

P

N.S. * N.S. ** < 0.01

Overall decision ’

Reject Accept Accept Accept Reject

with no conimon factors.

difference [l]. If the difference between the x2 for this hypothesis and the preceding hypothesis does not exceed the critical value for the chi-square distribution with the difference df, it can be assumed that the additional constraints imposed on the model by the second h~othesis can be accepted as plausible. The chi-square difference test is non-significant supporting plausibility of the null hypothesis of equality of factor loading matrices. The additional indices of x2/df, GFI and p also support invariance of the factor loadings across the 2 samples. Since invariance of the factor loadings was supported, the next hypothesis tested equality of the factor correlation matrices as also shown in Table II. On the basis of the multiple indicators of fit (x2 difference test, GFI, p), the decision was to accept this equality. Assuming equality of the factor pattern, factor loadings and factor correlations, the final hypothesis tested invariance of the residual error matrices across the 2 samples. This hypothesis was not supported as indicated by the significant chi-square change test, the substantialIy decreased GFI, and reduction in fit estimated by rho, as shown in Table II. Further evidence of the deterioration in fit was found in 3 negative SMCs across the 2 samples. Negative SMCs suggest inappropriateness of the model being tested since such values indicate that the unique variance being estimated by LISREL for a given variable is greater than the original variance of that variable. In addition, examination of the standardized residual error matrices showed that greater than 5% of the entries exceeded the criteria of +2 in both samples (12 and 7 of 136 for the labor and postoperative data respectively). The results of the multi-sample CFA indicated, therefore, that the most parsimonious model is a 3-factor, oblique model with invariant factor loadings and factor correlations. Factor loadings estimated by LIS-

‘rABf_f:

III

I ,\PlL.L:

STANDARDIZED FACTOR

MULTI-SAMPLE:

I_OADfNGS

FOR

f_ISREI.

3 THEORETI(:Af_

ESTIMATED MPQ

t:STIMATED

FACTORS

-I‘tfNl-

CORRELATIONS HY

I-IRMATORY Itern

Sensory

Affective

Evaluative

Temporal

0.671

0.0 I’

0.0

Spatial

IIYI’o’f’f

UNOBSERVED ,ZN,\f

0.0

0.0

I.152

0.0

0.0

Incisive pressure

0.3h9

0.0

Il.11

I .o

(‘onstrictivc

0.215

0.0

0.0

0.477

prc\surc

Iraction

pressure

prosure

0532

0.0

0.0

0.189

0.0

0.0

ISI-ightnc\\

0.700

0.0

00

Duffne\~

0.376

0.0

I) (I

O.SSI;

0.0

0.0

Tension

0.0

0.356

I).0

/\utonomlc

0.0

0.27x

I).(1

Fcal

0.0

tl.h71

0.0

Punishment

0.0

O.XYO

II.0

Aftectivc

0.1)

11.1I.3

0.1)

0.0

0.0 I .lh7

parameters

fixed

tmiscelfaneouc)

tmisceflaneous)

f:vafuati~c ,’ Loadings

of 0.0 indicate

theowticaf

MPQ

at zero

according

lo

structure.

LL~I~: All loadings significantly

different

)N-

I

Thermal

Scnwly

I. (

\‘SIS

0.71

I’unctate

fESf%.I>l)

MU1

than Nero at P s_0.01.

REL presented in III. As by the model of MPQ, each is significantly from zero the 2 for this Inspection of matrix of (error) variance each observed and correSMCs suggests, that for the labor postoperative data variance reunexplained by model, particularly the sensory The SMCs the sensory range from to 0.34 from 0.04 0.47 for labor and data respectively, SMCs for affective items from 0.21 0.51 and 0.28 to (The SMC the evaluative is by 1.0 since is the indicator of evaluative factor.) standardized residual matrices supadequate fit the model less than of the in the for either is greater 2 standard from zero. indicated by estimated factor presented in IV, the factors are cantly intercorrelated with the model

proposed by Melzack [ 131 and the findings of Turk ct al. [311. Therefore. the multi-sample CFA supported invariance of the factor pattern, factor loadings, and factor correlations of the MPQ within the 2 acute pain samples consistent with the theoretical model proposed by Melzack. A possible concern with the analyses just described involves the facts that la) the PRI items represent ordinal rather than interval measurement scales, and (b) the measures on the items may not satisfy the assumption of multivariate normality. While there is evidence that maximum likelihood estimation is robust to such violations of assumptions [9], it was considered desirable to conduct alternative analyses using confirmatory factor analysis methods more appropriate fog ordinal, non-normal data. Such analyses were carried out by obtaining matrices of polychoric correlations l’o~ the PRI items, and fitting the confirmatory factor modcls to those correlations using weighted least squares estimation via LISREL VII [91. The results, shown in Table V, were quite consistent with findings from the ML analyses of the covariancc matrices in that the chi-square for each hypothesis was non-significant, supporting acceptance of the specified equalities of the MPQ model across the 2 samples. Given this consistency, detailed results will not be presented here. Regardless, our preference is to focus on the ML analyses for another reason. Multi-sample problems require the analysis of covariance matrices rather than correlation matrices [9]. While the SCCondary analysis just described overcomes problems ot

‘I’ABLE SUMMARY WEIGHTED

OF MULTI-SAMPLE LEAST

SQUARES

tlypothesis.

CONFIRMATORY

FACTOR

ANALYSIS

USING

POLY<‘HORI(’

CORRELATION

MATRIC‘ES

X2

c!f

r

GFI

,’

Overall decision ”

equality of

I x2.05

204

0.863

0.972

Factor loading matrices

‘17-. h8 _.

220

0.2h6

0.Oh-l

Acccpl

Fac(or correlation

741.47

223

0.189

0.962

Accept

241.17

238

0.425

0.w

Accept

Three

factor pattern matrices

Residual error matrices ” Cioodnes~ of fit index. ” Overall

AND

ESTIMATION

decision for the specific hypothesis.

Accepr

_-

-.

59 TABLE

VI

PEARSON

CORRELATIONS

Subscales

Sensory Affective Evaluative

*

Labor

AMONG

PRI SUBSCALES

IN LABOR

AND POSTOPERATIVE Postoperative

sample

Sensory

Affective

1.0 0.59 * 0.36 i

1.0 0.42 *

PAIN SAMPLES sample

Evaluative

Sensory

Affective

Evaluative

1.0

1.0 0.65 * 0.46 *

1.0 0.49 *

1.0

0.001.

ordinal measurement and non-normal distributions, it creates a new problem through the analysis of polychoric correlation matrices rather than covariance matrices. Discriminant Llalidity of the PRI subscales In response to the conclusions reached by Turk et al. [21] who inferred lack of discriminant validity of the MPQ subscales on the basis of factor correlations from the CFA analysis, the magnitude of the estimated correlations among the factors in the current study was examined. It is critical to note at this point that the correlations among the 3 factors estimated by LISREL are estimates of correlations among latent variables with error of measurement removed from the corresponding measured (observed) variables. Therefore, the factor correlations estimated by LISREL are considerably higher than the correlations among the actual subscaZes of the instrument for each sample presented in Table VI. Although the subscale intercorrelations are all statistically significant, the correlations are in a low to moderate range and in each case are lower than the reliability estimates for the corresponding subscales. These relationships indicate that the observed subscale scores, although significantly correlated as originally hypothesized by Melzack, do exhibit an acceptable degree of discriminance.

Discussion The primary goal of this study was to evaluate the theoretical structure of the McGill Pain Questionnaire in the measurement of acute clinical pain through multi-sample confirmato~ factor analysis. The results of the investigation provided statistical support for the a priori model on which the sensory, affective and evaluative subscales of the PRI were based as validated in 2 distinct pain populations represented by women experiencing labor and postoperative pain. Unlike exploratory factor analytic results reported previously for acute pain [10,18], the study findings support the tripartite discrimination of acute pain into sensory, affeclive, and evaluative dimensions. An additional strength of this study was the confir-

mation of the ML CFA findings through a secondary analysis using data matrices (polychoric correlation and asymptotic covariance matrices) and an estimation method (weighted least squares) more appropriate for ordinal measures such as the individual items of the PRI. However, this secondary analysis is not without its own limitations since the technique of multi-sample confirmatory factor analysis is intended to analyze covariance matrices rather than correlation matrices [91. Nevertheless, the similarity in results of the 2 analytic approaches provides additional credence to the conclusion of statistical support for the tripartite model of the PRI. One caution, however, concerns some difficulties in effectively assessing the reliability of the MPQ and its subscales. The study of the reliability of measures of pain is affected by the same limitations affecting the study of reliability for all measures of dynamic subjective phenomena (e.g., the inappropriateness of test-retest methods). Despite these limitations, however, the issue of reliability cannot be ignored since reliability is a necessary condition of validity. Internal consistency of the PRI and its subscales can be estimated, but only recently have such reports begun to appear in the literature [11,12,21]. Although these initial estimates of internal consistency provide some statistical evidence for the reliability of the PRI and the sensory and affective subscales, alpha coefficients consistently greater than 0.80 would be more reassuring for this frequently used instrument [Xl. A secondary item analysis of the subscale items not reported in this paper suggested that the constrictive pressure and dullness subclasses of the sensory subscale may be detracting from the reliability of this subscale score. Further, because the evaluative subscale is based on one item, the ability to evaluate its reliability by an estimate of internal consistency is prohibited. From a psychometric standpoint, the basing of a subscale on one item is one of the most severe limitations of the MPQ and may be a primary reason why the evaluative dimension has received equivocal support through exploratory factor analysis. The MPQ and the theoretical perspective it represents is a classic contribution to the understanding and research of pain. Although limited in generalizability

60

by the one gender sample, the study findings have supported the tripartite theoretical structure originally conceptualized for the MPQ in the study of acute clinical pain through multi-sample confirmatory factor analysis. Continued psychometric study and perhaps modification of the instrument is indicated, however, because of the less than optimal internal consistency estimates for the sensory and affective subscales and the composition of the evaluative subscale of only one ordinal item. In order for the measurement of pain to become more theoretically and psychometrically sound, researchers must respond to the challenge presented by Melzack in his original article describing the MPQ. In that landmark article, Melzack [13] concluded “the questionnaire presented here, it is hoped, will eventually be refined by investigators in other laboratories and clinics. Ultimately, they may lead to universal tools for the measurement and assessment of pain that will permit rapid exchange of data among all investigators of clinical pain phenomena” (p. 296).

4

5

6

7 8 9

10

I1 12

Acknowledgements

This study was supported by grants to the first author from the American Nurses’ Foundation and the Center for Nursing Research, The Ohio State University, College of Nursing. The assistance of Betty Chilton, R.N., M.S., Patricia Sikorski, R.N., MS., and Barbara Mims, R.N., M.Ed., in the collection of the study data is gratefully acknowledged. The authors also express thanks to Dr. Ronald Melzack for his willing and helpful review of an earlier version of this manuscript.

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References

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Bentler, P.M. and Bonett, D.G., Significance tests and goodness of fit in the analysis of covariance structures, Psychol. Bull., X8 (1980) 588-606. Boomsma, A., On the Robustness of LISREL (maximum likelihood estimation) Against Small Sample Size and Nonnormality. Sociometric Research Foundation, Amsterdam, 1983. Bryne. M., Troy. A.. Bradley, L.A., Marchisello, P.J., Geisinger,

21

K.F., Van drr Heide. L.11. and Prieto. E.J.. (‘r~)s\-val~dat~~)~~ 01 the factor structure of the McGill Pain Questionnaire. Pain. I.? (1982) 193-201. Campbell. D.T. and Fiske, D.W.. Convergent and dlacrimlnant validation by the multitrait-multimethod matrix, Psychol. Null.. ih (1959) 81-10s. (‘armines. E.G. and Mclver. J.P., Analyzing models with unob\erved variables: Analysis of covariance structures. In: G.W. Bohrnstedt and E.F. Borgatta (Eds.). Social Measurement: <‘urrent Issues, Sage, Beverly Hills. CA, 1981, pp. 65--l 15. Crockett. D.J.. Prkachin. K.M. and Craig. K.D.. Factors ot the language of pain in patient and volunteer group,. Pain. .1 (1077) 175-182. Hayduk. L.A., Structural Equation Modeling with LISREL, John Hopkins University Press, Baltimore, MD, 1987. Jiireskog, K.G., Simultaneous factor analysis in several popul+ tions. Psychometrika, 57 (1971) 409-426. Jiireakog, K.G. and Sijrbom, D.. LISREL VII: A Ciulde to the Program and Applications, SPSS. Chicago, IL, 198X. Leavitt. F.. Garron, D.C.. Whisler, W.W. and Sheinkop, M.B.. Affective and sensory dimensions of back pain. Pain. 4 (197X) 773-2x1. Lowe. N.K., Explaining the pain of active labor: the importance of maternal confidence, Res. Nurs. Hlth. I2 (19X9) 237-245. Lowe, N.K. and Roberts, J.E.. The convergence between in-labor report and postpartum recall of parturition pain. Res. Nurs. Hlth. 1 I (1988) 11-21. Melzack. R., The McGill Pain Questionnaire: major propertics and scoring methods. Pain. I (1975) 277-299. Melzack. R. and Casey, K.L.. Sensory, motivational and central control determinants of pain: a new conceptual model. In: D. Kenshalo (Ed.). The Skin Senses, Thomas, Springfield, IL, 196X. pp. 423-444. Melzack. R. and Torgerson, W.S.. On the language of pain. Anesthesiology, 34 (1971)50-59. Nunnally. J.C.. Psychometric Theory. McGraw-Hill. New York. 197X. Prieto. E.J.. Hopson, L., Bradley. L.A., Byrne, M., Gelsinger. K.F.. Midax, D. and Marchisello, P.J., The language of low back pain: factor structure of the McGill Pain Questionnaire, Pain, X (1980) 11-19. Reading, A.E., The internal structure of the McGill Pain Questionnaire in dysmenorrhoea patients, Pain, 7 (1979) 353-358. Reading, A.E.. A comparison of the McGill Pain Questionnaire in chronic and acute pain, Pain, I3 (1982) 185-192. Tanaka, J.S. and Huba, G.J., A fit index for covariance structure models under arbitrary GLS estimation. Br. J. Math. Stat. Psychol., 3X (1985) 197-201. Turk. D.C.. Rudy. T.E. and Salovey, P.. The McGill Pain Questionnaire reconsidered: confirming the factor structure and examining appropriate uses. Pain. 21 (19X5) 385-397.