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Feeding difficulty in elderly patients with dementia: Confirmatory factor analysis
int J. Nurs. Srud.,,Vol. 34, No. 6. pp. 405414, 1997 q~ 1997 Elsewer Saence Ltd. All rights reserved Printed III Great Britam 002@~7489197 $17.00+0.00
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Feeding difficulty in elderly patients with dementia : Confirmatory factor analysis Roger Watsona* and Ian J. Dearyb dDepartment of Nursing Studies, The University of Edinburgh, Edinburgh EH8 9LL, Scotland, U.K. ‘Department of Psychology, The University of Edinburgh, Edinburgh EH8 9JZ, Scotland, U.K. (Received 12 May 1997;accepted 21 July 1997) Abstract
The latent structure of feeding difficulty in elderly patients with dementia was investigated using multivariate statistical techniques including exploratory and confirmatory factor analysis. A survey design of 345 elderly patients with the diagnosis of dementia using a questionnaire completed by key workers and primary nurses, was used in local psychogeriatric and continuing care of the elderly facilities. Feeding difficulty and nursing intervention were estimated followed by fitting of latent variable models of feeding difficulty to the data using structural equation modelling. Three models of feeding difficulty in elderly patients with dementia, with 2, 3 and 4 factor structures respectively were compared. All three models showed a good fit to the data as assessedby several standard criteria. The 3 and 4 factor models, however, showed significantly better fit than the 2 factor model. The 4 factor model introduced a latent variable of “oral difficulty” with feeding which merits further investigation. This study demonstrates the possibility of developing reliable and validated scales for the assessmentof feeding difficulty in elderly patients with dementia. ‘c 1997Elsevier Science Ltd. All rights reserved. Ker~;ords: Dementia ; feeding ; factor analysis.
Introduction Elderly people with dementia undergo an inevitable decline in their ability to care for themselves and this decline affects all activities of daily living including the ability to feed (Volicer et al., 1987). The rate at which self care abilities decline is individual but, with regard to feeding, the end-point, which is usually indicative of the terminal stages of dementia, is characterized by a complete inability to feed (Norberg et al., 1980).
One of the consequences of reduced ability to feed in elderly patients with dementia is that physical help by others is required in order to ensure that feeding is enabled (Osborn and Marshall, 1993). Even when such help is rendered, nutritional problems may ensue (Sandman et al., 1987). In order to compensate for the reduced ability to feed and in an attempt to obviate nutritional problems a considerable amount of help is required. For example, in nursing care settings it has been estimated that 25% of nurses’ work with elderly
*To whom all correspondence should be addressed: Telephone: 0131 650 3901; Fax: 0131 650 3891; e-mail: R.Watson@ ed.ac.uk 405
dementing patients is spent on helping with feeding (Siebens et al., 1986). The feeding problems of elderly people with dementia have received increasing attention in the past 20 years and a considerable literature has been established and this has been extensively reviewed (Watson, 1993). Much of the research into the feeding problems of elderly people with dementia has been concerned with the interaction between the person feeding the patient (usually a nurse) and the patient. The aspects of this interaction which have been studied include the ethical dimensions (Akerlund and Norberg, 1985), interpretation of feeding difficulty (Athlin and Norberg, 1987) interventions (Michaelsson et al., 1987) withdrawal of feeding (Norberg et al., 1987), description of feeding problems (Volicer et al., 1989) and the influence of feeding difficulty on nutritional status (Du ef al., 1993). A variety of methods has been employed in order to study feeding difficulty in elderly people in the studies referred to above. These methods include interview (Norberg and Hirschfield, 1987), phenomenology (Athlin et ul., 1989) and observation with recording of frequency of events (Backstrom et al., 1987). With very few exceptions, these studies have involved small numbers of subjects and due to this inadequacy rig-
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orous statistical analysis, for example using multivariate techniques, has not been possible. As a result the utility of the results for the detailed description and, indeed, measurement of feeding difficulty in elderly people with dementia has been limited. It has been indicated (Watson, 1993) that there is a need for continuing research which was aimed at assessing more formally, through detailed description and measurement, the feeding difficulty of elderly people with dementia. The justification was that improving the assessment of feeding difficulty would improve the understanding of the problems faced by elderly people with dementia. This, in turn, could lay a foundation for studies which were aimed at alleviating feeding difficulty through nursing intervention or, at least, investigating appropriate nursing interventions which would help to alleviate nutritional problems
Table la
Questionnaire administered to nurses in the present study Does the patient require close supervision while feeding? Does the patient require physical help with feeding? Is there spillage while feeding? Does the patient tend to leave food on the plate at the end of a meal? Does the patient ever refuse to eat? Does the patient turn his head away while being fed? Does the patient refuse to open his mouth? Does the patient spit out his food? Does the patient leave his mouth open allowing food to drop out? Does the patient refuse to swallow? Indicate the appropriate level of care for feeding required by patient : supportive-educative partly compensatory wholly compensatory Possible responseto questions l-10: A = never; B = sometimes ; C = often.
and maximize patient dignity and comfort. Unless the principal domains of feeding difficulty are identified and rendered quantifiable useful research along these lines will be prevented. Previous work carried out by Watson and Deary (1994) using the 11 item Edinburgh Feeding Evaluation in Dementia Questionnaire (EdFED-Q ; Table la) on feeding difficulty in elderly people with dementia has shown, using the technique of exploratory factor analysis, that there is a case for separating out problems related to feeding difficulty into a 3 factor structure. The factors were labelled respectively Factor 1, Patient obstinacy or passivity (e.g. turning the head away and spitting) ; Factor 2, Nursing intervention (e.g. supervision and physical help) ; Factor 3, Indicator of feeding difficulty (e.g. spillage and leaving food on the plate). The possibility also exists that the latent structure revealed by exploratory factor analysis (Watson and Deary, 1994) is deficient and that an alternative underlying structure may exist. This cannot be tested rigorously using exploratory factor analysis ; therefore, using the technique of structural equation modelling, a larger data set was analysed by confirmatory factor analysis. This is a set of statistical techniques which allow a formal, competitive comparison of different models of association between variables in a multivariate data set. In this study these techniques were applied to feeding difficulty data from elderly people with dementia to test models of the latent structure of feeding problems with more rigour than has been possible previously. Therefore, using the EdFED-Q mentioned above the aim of the present study was to test a number of models of feeding difficulty in elderly people with dementia.
Methods Charge nurses in six wards in two psychogeriatric units were asked to identify elderly patients in their care who had the diagnosis of dementia. Once patients
Table lb Correlation matrix of variables from the EdFED-Q showing Pearson’s correlation to two decimal places with P i 0.001 Q1
Q2
Q3
44
QS
Q6
Q7
QS
Q9
QlO
Qll
1.00 0.39 0.20 0.24 0.54 0.51 0.30 0.38 0.34 0.90
1.00 0.31 0.09 0.29 0.28 0.25 0.27 0.18 0.36
1.00 *0.61 0.33 0.41 0.33 0.23 0.30 0.18
1.00 0.51 0.55 0.49 0.32 0.41 0.23
1.00 0.8 1 0.56 0.46 0.48 0.52
1.00 0.66 0.46 0.60 0.50
1.oo 0.53 0.64 0.27
1.oo 0.62 0.35
1.00 0.32
1.00
1.00 :: 43 :: ;:
QS Q9 QlO
Qll *P
0.76 0.41 0.32 0.30 0.46 0.47 0.26 0.31 0.32 0.72
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difficulty in elderly patients with dementia
had been identified copies of the EdFED-Q, the development of which has been described previously (Watson, 1994) were sent to the wards and these were completed by the team leaders of the nurses caring for the patients. Data were collected over 24 months, and 345 subjects were entered into the study. Exploratory factor analysis, using principal components analysis, was carried out on a networked version of SPSS (Statistical Package for the Social Sciences)/PC’ version 4.0.1. for the IBM PC. Confirmatory factor analyses were run on an Apple Macintosh computer using the EQS structural equations package of Bentler (Bentler, 1989).
Exploratory factor analysis Factor analysis is a set of multivariate statistical techniques, the purpose of which is to summarize and reduce data by analysing the interrelationships among a large number of variables (Hair et al., 1993). By this means, a set of dimensions (sometimes called latent traits, factors or components) can be identified which are not otherwise easily observed in a large set of variables. Principal components analysis (PCA) was used here in order to “explain as much of the total variation in the data as possible with as few of these factors as possible” (Dillon and Goldstein, 1984). Strictly speaking PCA does not identify factors, and there are statistical differences between PCA and factor analysis, narrowly defined. Nevertheless, the term factor is commonly used to refer to the components identified by PCA and this convention will be followed here. In order to characterize factors a rotational procedure was used (Child, 1990).
Confirmatory ,fl?ctor analysis : model construction and testing Confirmatory factor analysis allows hypothetical models to be set up before the data are analysed and subsequently tested for their fit, by a number of criteria, to the data. In addition, the testing of alternative, competing models is possible (Deary et al., 1991). The starting points for the construction of models to be tested by the methods outlined in this paper were the exploratory factor analysis previously published (Watson and Deary, 1994) and the exploratory factor analysis published here. In addition, a review of the literature led to the conclusion that there may be different components to the feeding difficulty observed in elderly people with dementia (Watson, 1993). The possibilities from these analyses were either 2 or 3 factor solutions. For instance, the previously published 3 factor solution identified factors related to patient difficulty, indicators of feeding difficulty and nursing intervention. The aim of the present work was to construct models along these lines, looking for features which would enhance the fit of the model to
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the criteria outlined below, and to look for good fitting alternative models. Structural equation modelling (S.E.M.) as used here allows single items concerning feeding difficulty to be investigated in terms of models of their association (covariance). The starting point for the analysis (and for the exploratory PCA) is the fact that most of the items correlate significantly with each other, some more so than others. The most economical hypothesis to explain this promiscuous inter-correlation is that the items are indicators of some powerful latent traits related to feeding difficulty. The aim of the present study is to discover the number and nature of these In latent factors, and their inter-relationships. addition to having loadings on latent factors (it is quite possible that any one item on the questionnaire is an indicator of more than one latent trait) single items will also typically contain a substantial amount of unique/error variance that is not shared with other items. In summary, then, having done much preliminary research on a questionnaire that collects information about most feeding difficulties mentioned in the literature, it should be possible to use S.E.M. to provide an economical model of the principal domains of feeding difficulty and their interactions. The statistical criteria used to decide which model best described the data collected here were : (i) average of the off-diagonal absolute standardized residuals ; (ii) x2 ; (iii) the Bentler-Bonnet fit indices ; (iv) the Lagrange multiplier test. These criteria, respectively, tell the investigator : (i) how much residual correlation a particular model leaves after accounting for the specified pathways (Biddle and Martin ,1987). Lower values, therefore, are sought ; (ii) The x2 test measures the residual correlation and shows, at a predetermined level, whether the observed and predicted values differ from zero (Biddle and Martin, 1987 ; Anderson, 1987). Good models by this criterion have low x’ values and high, non-significant p levels ; (iii) The Bentler-Bonnet fit indices, which should range from r&l, result from a comparison of a “baseline” model, where independence between the variables is assumed, with the observed variances and covariances. A good model has fit indices close to unity and acceptable models have values greater than 0.9 (Anderson. 1987): (iv) The Lagrange multiplier test adds parameters in order to see if these offer any improvement to the specified models (Bentler, 1989). In other words, this test may be used to suggest alternative, better fitting models to the data. Procedure Structural equations, i.e. “equations which stipulate the structure of hypothesized relationships in a model” (Bryman and Cramer, 1990) were constructed for a variety of possible models (13 in total) containing 1, 2, 3, and 4 latent factors. Those models which best fitted the data by the criteria outlined above are discussed in the remainder of this paper.
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Table 2
cipal component suggest, as observed in a previous study, that there is a general factor of feeding difficulty of the data obtained from the EdFED-Q showing loadings underlying the data. The variables with high loadings of variableson factorswhich aregreaterthan 0.400 in bold on the first rotated factor are all related to the feeding difficulty of the patients (e.g. refusing to swallow) First whereas the variables loading on the second and third unrotated Derived solution factors are those related to nursing action (e.g. physiprincipal pattern matrix factors* cal help) and indicators of feeding difficulty (e.g. spill1 Q Communality component 2 3 age), respectively. The values for communality which 10 0.73 1 0.698 0.873 0.038 -0.010 are shown in Table 2 show how much of the variance 8 0.731 0.704 0.825 0.099 0.157 in each variable is accounted for across the factors 9 0.632 0.653 0.801 -0.077 -0.142 which have been retained. 7 0.755 0.848 0.621 -0.240 0.242 Following structural equation modelling three 6 0.664 0.809 0.541 -0.315 0.185 models were shown, by the criteria outlined above, to 2 0.890 0.751 0.117 -0.923 -0.137 fit the data well and the EQS results for these models 0.861 0.722 0.098 -0.919 -0.155 11 are shown in Table 3. The three models (1, 2 and 3) 1 0.774 0.711 -0.020 -0.862 0.081 are, respectively, 2, 3 and 4 factor models and are 3 0.360 0.475 -0.085 -0.555 0.187 represented diagrammatically in Figs 1, 2 and 3. A 3 4 0.833 0.525 -0.059 -0.076 0.909 5 0.751 0.619 0.296 0.061 0.740 factor model has already been proposed (Watson and Deary, 1994)and is supported by the exploratory fac* The solution was obtained using the oblimin procedure on tor analysis presented here. However, as indicated SPSS PC’. above, the casefor a 2 factor model could be made on the basis that much of the variance in the data is explained by the first 2 factors. A 2 factor model could also be supported by examining the relative sizes of Results the Eigenvalues. If thesehad been compared using the The correlation matrix for the present data is shown scree slope method (Child, 1990) instead of Eigin Table lb. Apart from the low correlation between envalues greater than 1, given that the Eigenvalue for one pair of variables (Q3 and Q5) all the remaining Factor 3 is very close to unity, then a 2 factor model correlations are highly significant. The majority of the could have emerged.The previously published 3 factor correlations are modest in size but there are some model (Watson and Deary, 1994) included a factor large correlations between variables 47, Q8 and QIO ; labelled “indicators of feeding difficulty” (e.g. spillage likewise there is a high correlation between variables and leaving food on the plate) as thesewere viewed as Q4 and Ql. In addition, there are very high cordifferent from behavioural problems such as spitting relations between variables Ql, Q2 and Ql 1. Thereand turning the head away. However, this model may fore, while there is much shared variance among all be too liberal in its interpretation of the data on feedof the variables, someof theserelationships appear to ing difficulty and nurses, indeed, may not differentiate be stronger than others. Greater clarity, in terms of between indicators of feeding difficulty and actual any underlying dimensions in the correlation matrix, difficulties which elderly patients with dementia could be obtained by means of factor analysis. display. The inclusion of the 4 factor model will be The results of the exploratory factor analyses are shown in Table 2. Principal components analysis, using the criterion of Eigenvalues greater than 1, reduced the 11 variables to 3 factors (an Eigenvalue Table 3 “corresponds to the percentageof variance explained EQS output for structural equation modelling of the best fitting 2, 3 and 4 factor models by the equivalent number of variables” (Walsh, 1990)). However, the Eigenvalues were 5.26, 1.67 and No. of latent Model 3 Model 1 Model 2 1.06, respectively, for the three factors. As the first variables 4 3 2 two factors explain 47.8% and 15.1% (total 62.9%) 0.019 0.019 0.024 of the variance in the data, respectively, it could be AODASD* 60 78 60 argued that there are only two substantial factors. x2 Principalcomponentsanalysisfollowed by oblique rotation
This conservatism is supported size which is well beyond the
by the large sample
minimum 1 : 5 ratio of variables to subjects suggested by Child (1990) and the 1 : 10 suggestedby Kline (Kline, 1994).With such large sample sizes the possibility of finding variables with Eigenvalues greater than 1 is increased. For a fuller account of the interpretation of the factor matrix in relation to EdFED-Q seeWatson and Deary (1994). The loadings
of variables
on the first unrotated
prin-
Significance (W** BBNFIt BBNNFIf
0.001 (33)
0.005 (31)
0.005 (31)
0.969 0.969
0.976 0.979
0.979 0.988
* AODASD = Average off-diagonal absolute standardized residuals. p BBNFII = Ben&-Bonnet normed fit index. $ BBNNFI = Ben&-Bonnet non-normed fit index. ** df = degrees of freedom.
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difficulty in elderly patients with dementia
409
,240 c
s .272
,812 C ,769
,925
-
,621
).467
/
Fig. 1. Diagrammatic representation of the structural equations specifying the 2 factor model of feeding difficulty in elderly people with dementia. Rectangular boxes represent measuredvariables, circles represent latent variables and arrows without origins represent residual variance specific to the respective variables. Straight lines represent causal pathways, with arrows indicating the direction of causation and curved lines represent variables permitted to intercorrelate. Numbers adjacent to arrows represent parameter estimates for optimal model fitting from the EQS programme. Ql to Qll are explained in Table la.
explained below. The three models can be considered to be competing models for the representation of the data from the study. All three models are presented because of their superior fit, according to the criteria described above, compared with other possible models and because of their similarity according to these same criteria. From Table 3 it can be seen that the average offdiagonal absolute standardized residuals are small and very similar indicating that most of the covariance among the items has been accounted for by all of the models. The x2 values are significant but this is a function of the large sample size used in this study. Even very small differences between the predicted and observed residual correlations will be significant. Bentler-Bonnet fit indices of greater than 0.9 are taken to indicate an adequate fit of the model to the data and it can be seen from Table 3 that the fit indices for all three models are more than adequate. The
Lagrange multiplier test did not indicate any parameters with fixed values which might have been freed in order to improve the fit of the model to the data. Because all fixed parameters were those fixed at zero, the Lagrange multiplier test indicates that no additional pathways would improve the fit of the models. Goodness of fit between the models and the data has been achieved, generally speaking, by allowing items to load on more than one latent variable, and/or correlation between error terms. With large subject samples it is rarely the case that behavioural items will be so pure as to load on only one latent variable and have error variance entirely uncorrelated with that of any other item. Cross loading by variables on factors simply means that the variance of each item is explained by a combination of the variance from more than one factor. In cases where error terms have been allowed to correlate this recognises that variance
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,324
.384
Fig. 2. Diagrammaticrepresentationof the structuralequationsspecifyingthe 3 factor modelof feeding difficulty in elderlypeoplewith dementia.For conventionsseelegendto Fig. 1.
assumedto be specific to one item may be similar to that of another item. This expedient measure combines variables with large amounts of unexplained covariance. Correlation between latent variables indicatesthat they are considered to be oblique, i.e. intercorrelated, for the purposes of the model. Model 1 (Eg. 1)
This is a 2 factor model where the 2 factors are allowed to correlate with one another. Variables Q4, QS, Q6, Q7, Q8 and Q9 load highly on Factor 1 and variables Ql and Q3 at very low levels. The variables loading on this factor describe aspects of patient behaviour (e.g. spitting) and incidents which indicate that a patient is having difficulty (e.g. spillage). Ql, Q2 and Ql 1 and to a lesser extent Q3 load highly on Factor 2. These variables describe nursing interventions (e.g. physical help). The intercorrelations between error terms in this model are between 43, Q4 and QS two of which (43 and 44) were previously described as indicators of
feeding difficulty and one of which (QS) is a general descriptor of patient refusal to eat. Variables Q8, Q9 and QlO which have universally intercorrelated error terms describe aspectsof patient difficulty which are oral in nature (e.g. spitting and refusing to swallow). Therefore, there is good reason to suppose that they might share some specific variance. It is a moot point whether the shared variance among these 3 items should be considered as correlated error variance or an identifiable additional latent variable. Model 2 (Fig. 2)
This is a 3 factor model in which all of the factors are correlated. Variables Q6, 47, Q8 and QlO and to a lesser extent Q9 load highly on Factor 1 and these are variables (e.g. spitting) which describe aspects of patient behaviour in feeding difficulty. Ql, 42 and Qll load highly on Factor 2, and these variables describe nursing intervention (e.g. physical help), and 4 other variables load moderately on this factor. Variables Q4 and Q5 load highly on Factor 3 and these
R. Watson and I. J. Deary/Feeding
difficulty in elderly patients with dementia
(c
411
1
,324
,269
1 ,527
Fig. 3. Diagrammatic representation of the structural equations specifying the 4 factor model of feeding difficulty in elderly people with dementia. For conventions see legend to Fig. 1.
variables are indicators of feeding difficulty and a general descriptor of feeding difficulty respectively. Intercorrelations between error terms in this model are between 43 and Q4 both of which are indicators of feeding difficulty and between Q8, Q9 and QlO all of which describe oral aspects of feeding difficulty (e.g. spitting and refusing to swallow).
ways of construing the same model, but the significant improvement of Models 2 and 3 over Model 1 can be gauged by comparing the difference in the value of x2 (reduced by 18) and the degrees of freedom (reduced by 2). Otherwise, for all 3 models the average of the off-diagonal absolute standardized residuals are low and, likewise, the fit indices are all acceptable.
Model 3 (Fig. 3)
Discussion
This is a 4 factor model in which the first three factors are as described above in Model 2. The assumptions underlying this model, however, are that the variance shared by QS, Q9 and QlO represent an additional latent variable or factor and that this factor is unrelated to the other 3 factors. QS, Q9 and QlO load moderately on Factor 4 and these variables all describe oral aspects of feeding difficulty (e.g. spitting and refusing to swallow). Inter-correlations between error terms in this model are between 43 and 44 both of which are indicators of feeding difficulty. Models 2 and 3 are really just theoretically different
As described in the introduction, previous analysis of data from elderly people with dementia, using exploratory factor analysis, has shown that a 3 factor structure may account for the variance in the data from the present study (Watson and Deary, 1994). A second exploratory factor analysis, reported here, pointed to the possibility that a 2 factor solution may also be possible. This conclusion is based on the Eigenvalues obtained in the present study and on the distribution of variables across the factors. The loading of variables on Factors 2 and 3 in the present exploratory factor analysis was different from the pre-
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vious study. However, the case for a 3 factor model is very convincing from the loadings of variables on putative factors following oblique rotation. The present study was carried out in order rigorously to test competing models for the data and the technique which was applied was confirmatory factor analysis. The present study also had the advantage, while including data from a previous analysis, of a much enhanced data set. The models tested all fitted the data well and a model with one factor less (i.e. 2 factors) than the results of the previously reported exploratory factor analysis was tested in addition to a 3 factor and a 4 factor model. The relative merits of these models will be discussed. While cross loading between factors was evident in all of the models, the factors were discriminated on the basis of high loadings on individual variables. In Model 1 (with 2 factors) the variables on which factors load highly are clearly divided between those which are related to the feeding difficulty of the patient (Factor 1) and those which are related to nursing actions (Factor 2). The variables loading on Factor 1 were described in the exploratory factor analysis study as “patient obstinacy or passivity” and those on Factor 2 as “nursing intervention”. The variables which loaded on the third factor in the previous study, labelled “indicator of feeding difficulty” have intercorrelated error terms in this model. It is possible, therefore, that this is not a broad latent factor but what is termed a “bloated specific”. In other words the different questions may be probing exactly the same very narrow aspect of feeding difficulty which should, ideally, be captured in a single item. Alternatively, a third broad factor may exist but the questions asked were inadequate for a clear distinction from Factors 1 and 2. The same argument applies to the intercorrelated error terms for variables related to oral aspects of feeding difficulty. It may be the case that these questions are simply probing the same aspect of feeding difficulty or that this part of the EdFED-Q has not been adequately developed as a more general dimension of feeding difficulty. Model 2 (with 3 factors) introduces a third factor which loads highly on variables which may indicate that a patient is having difficulty with feeding rather than describing the actual difficulty. As such this factor is very similar to the third factor from the previous exploratory factor analysis study. One intercorrelation remains between two other such descriptors and the inter-correlation between the oral aspects of feeding difficulty remain. This model offers some improvement over Model 1 because there are a greater number of high loadings of factors on variables and the number of intercorrelated error terms is reduced. Inspection of x2 values and degrees of freedom furthermore suggest a significant incremental fit to the data. The main feature of Model 3 (4 factors) is that the variables with intercorrelated error terms, relating to
the oral aspects of feeding dificulty, have been used to create a fourth factor. This factor loads moderately on all of the variables related to oral difficulty (Q8, Q9 and QlO) and there is, therefore, a case for the existence of a factor specifically related to oral aspects of patient obstinacy or passivity. This model is an improvement on Model 2 because, while there is the same number of high loadings of factors on variables, the number of moderate loadings is increased and the number of intercorrelated error terms is reduced to one. On the basis of the fit indices from the EQS analysis it was difficult to choose between the 3 models as representing the best fit to the data. However, it was important to test these models and specifically to test how a 2 and a 4 factor model compared with the 3 factor model suggested by the original exploratory factor analysis, The 2 factor model is more conservative and is based on the premise that some of the items in the questionnaire are redundant. In contrast, the 4 factor model is based on the premise that the questionnaire requires further development, especially in the area of a factor specific to oral aspects of feeding difficulty. Specifically, the 2 factor model suggests that questions probing different aspects of feeding difficulty should be added the EdFED-Q and the 4 model suggests that questions specific to oral aspects of feeding difficulty should be added. The two suggestions are not mutually exclusive and both suggest a future line of investigation with an improved version of the EDFED-Q applied to a new sample of elderly people with dementia. It should be noted that all of the factors intercorrelate (except the putative Factor 4 in Model 3). This intercorrelation between factors indicates that the various aspects of feeding difficulty are related ; there is, for example, a relationship between the difficulty which a patient has with feeding and the actions which nurses take to alleviate that difficulty. This suggests a line of investigation whereby it may be possible to investigate further any causal relationships among aspects of feeding difficulty.
Summary and conclusions The importance of the present study lies in its improvement upon the previous exploratory factor analysis study. The results of the previous study have largely been confirmed in terms of a 3 factor structure for feeding difficulty in elderly people with dementia. However, the possibility of a fourth factor specifically related to one aspect of patient difficulty has been raised by the present study. The consequences of this are that patient obstinacy and passivity may be more complex than previously suggested (Watson and Deary, 1994). The obstinacy of e.g. turning the head away and refusing to eat may be only one part of the problem of feeding difficulty and another aspect may be related specifically to oral difficulty. There may
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be clinical implications for nursing assessment and intervention aimed at helping elderly people with dementia to feed adequately. Clearly, further development of the EdFED-Q is required if the feeding difficulty of elderly people with dementia is going to be more fully understood. In this light the confirmatory factor analysis, and the stepby-step approach to testing and comparing competing models, has proved very useful. On the one hand, as stated above, the factor structure obtained from exploratory factor analysis has been largely confirmed. On the other hand, the positing of a fourth factor (suggested by intercorrelated error terms in the 2 and 3 factor models) suggests an area of the EdFEDQ which could be developed further. The remaining intercorrelation between two of the variables related to indicating patient difficulty with feeding suggests another area for development. Further investigation should be aimed at increasing the number of questions related to any one of these aspects in order to test, in future, the resulting factor structure. This has implications for future scaling and measurement of single aspects of feeding difficulty in elderly patients with dementia. For future intervention studies, aimed at improving feeding in elderly people with dementia, reliable and validated scales will be required. The present work lays a foundation for the development of such scales and the design of meaningful and effective intervention studies.
Acknowledgements Financial support from the Gardner bequest to the Department of Nursing Studies at the University of Edinburgh is gratefully acknowledged. Nursing staff of the Lothian Health Trust, Scotland UK are gratefully acknowledged for their assistance.
References Akerlund, B.-M. and Norberg, A. (1985) An ethical analysis of double bind conflicts as experienced by care workers feeding severely demented patients. International Journal of Nursing Studies 22,207-16. Anderson, J. G. (1987) Structural equation model in the social and behavioural sciences : model building. Child Development 58, 49-64. Athlin, E. and Norberg, A. (1987) Caregiver’s attitudes to and interpretations of the behaviour of severely demented patients during feeding in a patient assignment care system. International Journal of Nursing Studies 24, 1455153. Athlin, E., Norberg, A., Asplund K. and Jansson L. (1989) Feeding problems in severely demented patients seen from task and relationship aspects, Scandinavian Journal of Caring Sciences 3, 113-l 2 1. Backstrom, A., Norberg, A. and Norberg, B. (1987)
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Feeding difficulties in long stay patients at nursing homes. Caregiver turnover and caregiver’s assessments of duration and difficulty of assisted feeding and amount of food received by the patient. International Journal of Nursing Studies 24,69-76. Bentler, P. M. (1980) EQS structural equations programme manual BMDP. Statistical Software, LA. Biddle, B. J. and Martin, M. M. (1987) Causality, confirmation, credulity, and structural equation modelling. Child Development 58,417. Bryman, A. and Cramer, D. (1990) Quantitative data analysis for social scientists. Routledge, London. Child, D. (1990) The essentials of factor analysis. Cassel, London. Deary, I. J., Hepburn, D. A., MacLeod, K. M. and Frier, B. M. (1991) Partitioning the symptoms of hypoglycaemia using multi-sample confirmatory factor analysis. Diabetologia 36, 771-117. Dillon, W. R. and Goldstein, M. (1984) Principal components analysis and factor analysis. In Multivariate analysis : methods and applications. John Wiley, New York, 23-106. Du, W., DiLuca, C. and Growdon, J. H. (1993) Weight loss in Alzheimer’s disease. Journal of Geriatric Psychiatry and Neurology 6, 3438. Hair, J. F., Anderson, R. E. and Tatham R. L. (1997) Factor analysis. In Multivariate Data Ana1,v.k. Macmillan, New York 2333292. Kline, P. (1994) An easy guide to ,factor analysis. Routledge, London. Michaelsson, E., Norberg, A and Norberg, B. (1987) Feeding methods for demented patients in end stage of life. Geriatric Nursing 7(2), 69973. Norberg, A., Asplund, K. and Waxman, H. (1987) Withdrawing feeding and withholding artificial nutrition from severely demented patients : interviews with caregivers. Western Journal of Nursing Research 9, 3488356. Norberg, A. and Hirschfield, M. (1987) Feeding of severely demented patients in institutions : interviews with caregivers in Israel. Journal ofAdvanced Nursing 12, 551-557. Norberg, A., Norberg, B. and Bexell, G. (1980) Ethical problems in feeding patients with advanced dementia. British Medical Journal 281, 847-849. Osborn, C. L. and Marshall, M. J. (1993) Self-feeding performance in nursing home residents. Journal of Gerontological Nursing 19(3), 7-14. Sandman, P., Adolfsson, R., Nygren, C., Hallmans, G. and Winblad, B. (1987) Nutritional status and dietary intake in institutionalised patients with Alzheimer’s disease and multi-infarct dementia. Journal of the American Geriatrics Society 35, 3138. Siebens, H., Trupe, E., Siebens, A., Cook, F., Anshen, S., Hanauer, R. and Oster, G. (1986) Correlates and consequences of eating dependency in institutionalised elderly. Journal of the American Geriatrics Society 34, 1922198. Volicer, L., Seltzer, B., Rheaume, Y., Fabiszewski, K., Herz, L., Shapiro, R. and Innis, P. (1987) Progression of Alzheimer-type dementia in institutionalised patients: a cross sectional study. The Journal of Applied Gerontology 6, 183394. Volicer, L., Seltzer, B., Rheaume, Y., Karner, J., Glennon. M.. Riley, M. E. and Crino, P. (1989)
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Eating problems in patients with probable dementia of the Alzheimer type. Journal of Geriatric Psychiatry and Neurology 2, 188-195. Walsh, A. (1990) Statistics for the social sciences. Harper and Row, New York. Watson, R. (1993) Measuring feeding difficulty in patients with dementia : perspectivesand problems. Journal of Advanced Nursing 18,25-3 1.
Watson, R. (1994) Measuring feeding difficulty in patients with dementia : developing a scale. Journal of Advanced Nursing 19,257-263.
Watson, R. and Deary, I. J. (1994) Measuring feeding difficulty in patients with dementia : multivariate analysis of feeding problems, nursing interventions and indicators of feeding difficulty. Journal of Advanced Nursing 20,283-287.
Pergamon PII : SOO20-7489(97)00033-3
Feeding difficulty in elderly patients with dementia : Confirmatory factor analysis Roger Watsona* and Ian J. Dearyb dDepartment of Nursing Studies, The University of Edinburgh, Edinburgh EH8 9LL, Scotland, U.K. ‘Department of Psychology, The University of Edinburgh, Edinburgh EH8 9JZ, Scotland, U.K. (Received 12 May 1997;accepted 21 July 1997) Abstract
The latent structure of feeding difficulty in elderly patients with dementia was investigated using multivariate statistical techniques including exploratory and confirmatory factor analysis. A survey design of 345 elderly patients with the diagnosis of dementia using a questionnaire completed by key workers and primary nurses, was used in local psychogeriatric and continuing care of the elderly facilities. Feeding difficulty and nursing intervention were estimated followed by fitting of latent variable models of feeding difficulty to the data using structural equation modelling. Three models of feeding difficulty in elderly patients with dementia, with 2, 3 and 4 factor structures respectively were compared. All three models showed a good fit to the data as assessedby several standard criteria. The 3 and 4 factor models, however, showed significantly better fit than the 2 factor model. The 4 factor model introduced a latent variable of “oral difficulty” with feeding which merits further investigation. This study demonstrates the possibility of developing reliable and validated scales for the assessmentof feeding difficulty in elderly patients with dementia. ‘c 1997Elsevier Science Ltd. All rights reserved. Ker~;ords: Dementia ; feeding ; factor analysis.
Introduction Elderly people with dementia undergo an inevitable decline in their ability to care for themselves and this decline affects all activities of daily living including the ability to feed (Volicer et al., 1987). The rate at which self care abilities decline is individual but, with regard to feeding, the end-point, which is usually indicative of the terminal stages of dementia, is characterized by a complete inability to feed (Norberg et al., 1980).
One of the consequences of reduced ability to feed in elderly patients with dementia is that physical help by others is required in order to ensure that feeding is enabled (Osborn and Marshall, 1993). Even when such help is rendered, nutritional problems may ensue (Sandman et al., 1987). In order to compensate for the reduced ability to feed and in an attempt to obviate nutritional problems a considerable amount of help is required. For example, in nursing care settings it has been estimated that 25% of nurses’ work with elderly
*To whom all correspondence should be addressed: Telephone: 0131 650 3901; Fax: 0131 650 3891; e-mail: R.Watson@ ed.ac.uk 405
dementing patients is spent on helping with feeding (Siebens et al., 1986). The feeding problems of elderly people with dementia have received increasing attention in the past 20 years and a considerable literature has been established and this has been extensively reviewed (Watson, 1993). Much of the research into the feeding problems of elderly people with dementia has been concerned with the interaction between the person feeding the patient (usually a nurse) and the patient. The aspects of this interaction which have been studied include the ethical dimensions (Akerlund and Norberg, 1985), interpretation of feeding difficulty (Athlin and Norberg, 1987) interventions (Michaelsson et al., 1987) withdrawal of feeding (Norberg et al., 1987), description of feeding problems (Volicer et al., 1989) and the influence of feeding difficulty on nutritional status (Du ef al., 1993). A variety of methods has been employed in order to study feeding difficulty in elderly people in the studies referred to above. These methods include interview (Norberg and Hirschfield, 1987), phenomenology (Athlin et ul., 1989) and observation with recording of frequency of events (Backstrom et al., 1987). With very few exceptions, these studies have involved small numbers of subjects and due to this inadequacy rig-
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orous statistical analysis, for example using multivariate techniques, has not been possible. As a result the utility of the results for the detailed description and, indeed, measurement of feeding difficulty in elderly people with dementia has been limited. It has been indicated (Watson, 1993) that there is a need for continuing research which was aimed at assessing more formally, through detailed description and measurement, the feeding difficulty of elderly people with dementia. The justification was that improving the assessment of feeding difficulty would improve the understanding of the problems faced by elderly people with dementia. This, in turn, could lay a foundation for studies which were aimed at alleviating feeding difficulty through nursing intervention or, at least, investigating appropriate nursing interventions which would help to alleviate nutritional problems
Table la
Questionnaire administered to nurses in the present study Does the patient require close supervision while feeding? Does the patient require physical help with feeding? Is there spillage while feeding? Does the patient tend to leave food on the plate at the end of a meal? Does the patient ever refuse to eat? Does the patient turn his head away while being fed? Does the patient refuse to open his mouth? Does the patient spit out his food? Does the patient leave his mouth open allowing food to drop out? Does the patient refuse to swallow? Indicate the appropriate level of care for feeding required by patient : supportive-educative partly compensatory wholly compensatory Possible responseto questions l-10: A = never; B = sometimes ; C = often.
and maximize patient dignity and comfort. Unless the principal domains of feeding difficulty are identified and rendered quantifiable useful research along these lines will be prevented. Previous work carried out by Watson and Deary (1994) using the 11 item Edinburgh Feeding Evaluation in Dementia Questionnaire (EdFED-Q ; Table la) on feeding difficulty in elderly people with dementia has shown, using the technique of exploratory factor analysis, that there is a case for separating out problems related to feeding difficulty into a 3 factor structure. The factors were labelled respectively Factor 1, Patient obstinacy or passivity (e.g. turning the head away and spitting) ; Factor 2, Nursing intervention (e.g. supervision and physical help) ; Factor 3, Indicator of feeding difficulty (e.g. spillage and leaving food on the plate). The possibility also exists that the latent structure revealed by exploratory factor analysis (Watson and Deary, 1994) is deficient and that an alternative underlying structure may exist. This cannot be tested rigorously using exploratory factor analysis ; therefore, using the technique of structural equation modelling, a larger data set was analysed by confirmatory factor analysis. This is a set of statistical techniques which allow a formal, competitive comparison of different models of association between variables in a multivariate data set. In this study these techniques were applied to feeding difficulty data from elderly people with dementia to test models of the latent structure of feeding problems with more rigour than has been possible previously. Therefore, using the EdFED-Q mentioned above the aim of the present study was to test a number of models of feeding difficulty in elderly people with dementia.
Methods Charge nurses in six wards in two psychogeriatric units were asked to identify elderly patients in their care who had the diagnosis of dementia. Once patients
Table lb Correlation matrix of variables from the EdFED-Q showing Pearson’s correlation to two decimal places with P i 0.001 Q1
Q2
Q3
44
QS
Q6
Q7
QS
Q9
QlO
Qll
1.00 0.39 0.20 0.24 0.54 0.51 0.30 0.38 0.34 0.90
1.00 0.31 0.09 0.29 0.28 0.25 0.27 0.18 0.36
1.00 *0.61 0.33 0.41 0.33 0.23 0.30 0.18
1.00 0.51 0.55 0.49 0.32 0.41 0.23
1.00 0.8 1 0.56 0.46 0.48 0.52
1.00 0.66 0.46 0.60 0.50
1.oo 0.53 0.64 0.27
1.oo 0.62 0.35
1.00 0.32
1.00
1.00 :: 43 :: ;:
QS Q9 QlO
Qll *P
0.76 0.41 0.32 0.30 0.46 0.47 0.26 0.31 0.32 0.72
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difficulty in elderly patients with dementia
had been identified copies of the EdFED-Q, the development of which has been described previously (Watson, 1994) were sent to the wards and these were completed by the team leaders of the nurses caring for the patients. Data were collected over 24 months, and 345 subjects were entered into the study. Exploratory factor analysis, using principal components analysis, was carried out on a networked version of SPSS (Statistical Package for the Social Sciences)/PC’ version 4.0.1. for the IBM PC. Confirmatory factor analyses were run on an Apple Macintosh computer using the EQS structural equations package of Bentler (Bentler, 1989).
Exploratory factor analysis Factor analysis is a set of multivariate statistical techniques, the purpose of which is to summarize and reduce data by analysing the interrelationships among a large number of variables (Hair et al., 1993). By this means, a set of dimensions (sometimes called latent traits, factors or components) can be identified which are not otherwise easily observed in a large set of variables. Principal components analysis (PCA) was used here in order to “explain as much of the total variation in the data as possible with as few of these factors as possible” (Dillon and Goldstein, 1984). Strictly speaking PCA does not identify factors, and there are statistical differences between PCA and factor analysis, narrowly defined. Nevertheless, the term factor is commonly used to refer to the components identified by PCA and this convention will be followed here. In order to characterize factors a rotational procedure was used (Child, 1990).
Confirmatory ,fl?ctor analysis : model construction and testing Confirmatory factor analysis allows hypothetical models to be set up before the data are analysed and subsequently tested for their fit, by a number of criteria, to the data. In addition, the testing of alternative, competing models is possible (Deary et al., 1991). The starting points for the construction of models to be tested by the methods outlined in this paper were the exploratory factor analysis previously published (Watson and Deary, 1994) and the exploratory factor analysis published here. In addition, a review of the literature led to the conclusion that there may be different components to the feeding difficulty observed in elderly people with dementia (Watson, 1993). The possibilities from these analyses were either 2 or 3 factor solutions. For instance, the previously published 3 factor solution identified factors related to patient difficulty, indicators of feeding difficulty and nursing intervention. The aim of the present work was to construct models along these lines, looking for features which would enhance the fit of the model to
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the criteria outlined below, and to look for good fitting alternative models. Structural equation modelling (S.E.M.) as used here allows single items concerning feeding difficulty to be investigated in terms of models of their association (covariance). The starting point for the analysis (and for the exploratory PCA) is the fact that most of the items correlate significantly with each other, some more so than others. The most economical hypothesis to explain this promiscuous inter-correlation is that the items are indicators of some powerful latent traits related to feeding difficulty. The aim of the present study is to discover the number and nature of these In latent factors, and their inter-relationships. addition to having loadings on latent factors (it is quite possible that any one item on the questionnaire is an indicator of more than one latent trait) single items will also typically contain a substantial amount of unique/error variance that is not shared with other items. In summary, then, having done much preliminary research on a questionnaire that collects information about most feeding difficulties mentioned in the literature, it should be possible to use S.E.M. to provide an economical model of the principal domains of feeding difficulty and their interactions. The statistical criteria used to decide which model best described the data collected here were : (i) average of the off-diagonal absolute standardized residuals ; (ii) x2 ; (iii) the Bentler-Bonnet fit indices ; (iv) the Lagrange multiplier test. These criteria, respectively, tell the investigator : (i) how much residual correlation a particular model leaves after accounting for the specified pathways (Biddle and Martin ,1987). Lower values, therefore, are sought ; (ii) The x2 test measures the residual correlation and shows, at a predetermined level, whether the observed and predicted values differ from zero (Biddle and Martin, 1987 ; Anderson, 1987). Good models by this criterion have low x’ values and high, non-significant p levels ; (iii) The Bentler-Bonnet fit indices, which should range from r&l, result from a comparison of a “baseline” model, where independence between the variables is assumed, with the observed variances and covariances. A good model has fit indices close to unity and acceptable models have values greater than 0.9 (Anderson. 1987): (iv) The Lagrange multiplier test adds parameters in order to see if these offer any improvement to the specified models (Bentler, 1989). In other words, this test may be used to suggest alternative, better fitting models to the data. Procedure Structural equations, i.e. “equations which stipulate the structure of hypothesized relationships in a model” (Bryman and Cramer, 1990) were constructed for a variety of possible models (13 in total) containing 1, 2, 3, and 4 latent factors. Those models which best fitted the data by the criteria outlined above are discussed in the remainder of this paper.
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Table 2
cipal component suggest, as observed in a previous study, that there is a general factor of feeding difficulty of the data obtained from the EdFED-Q showing loadings underlying the data. The variables with high loadings of variableson factorswhich aregreaterthan 0.400 in bold on the first rotated factor are all related to the feeding difficulty of the patients (e.g. refusing to swallow) First whereas the variables loading on the second and third unrotated Derived solution factors are those related to nursing action (e.g. physiprincipal pattern matrix factors* cal help) and indicators of feeding difficulty (e.g. spill1 Q Communality component 2 3 age), respectively. The values for communality which 10 0.73 1 0.698 0.873 0.038 -0.010 are shown in Table 2 show how much of the variance 8 0.731 0.704 0.825 0.099 0.157 in each variable is accounted for across the factors 9 0.632 0.653 0.801 -0.077 -0.142 which have been retained. 7 0.755 0.848 0.621 -0.240 0.242 Following structural equation modelling three 6 0.664 0.809 0.541 -0.315 0.185 models were shown, by the criteria outlined above, to 2 0.890 0.751 0.117 -0.923 -0.137 fit the data well and the EQS results for these models 0.861 0.722 0.098 -0.919 -0.155 11 are shown in Table 3. The three models (1, 2 and 3) 1 0.774 0.711 -0.020 -0.862 0.081 are, respectively, 2, 3 and 4 factor models and are 3 0.360 0.475 -0.085 -0.555 0.187 represented diagrammatically in Figs 1, 2 and 3. A 3 4 0.833 0.525 -0.059 -0.076 0.909 5 0.751 0.619 0.296 0.061 0.740 factor model has already been proposed (Watson and Deary, 1994)and is supported by the exploratory fac* The solution was obtained using the oblimin procedure on tor analysis presented here. However, as indicated SPSS PC’. above, the casefor a 2 factor model could be made on the basis that much of the variance in the data is explained by the first 2 factors. A 2 factor model could also be supported by examining the relative sizes of Results the Eigenvalues. If thesehad been compared using the The correlation matrix for the present data is shown scree slope method (Child, 1990) instead of Eigin Table lb. Apart from the low correlation between envalues greater than 1, given that the Eigenvalue for one pair of variables (Q3 and Q5) all the remaining Factor 3 is very close to unity, then a 2 factor model correlations are highly significant. The majority of the could have emerged.The previously published 3 factor correlations are modest in size but there are some model (Watson and Deary, 1994) included a factor large correlations between variables 47, Q8 and QIO ; labelled “indicators of feeding difficulty” (e.g. spillage likewise there is a high correlation between variables and leaving food on the plate) as thesewere viewed as Q4 and Ql. In addition, there are very high cordifferent from behavioural problems such as spitting relations between variables Ql, Q2 and Ql 1. Thereand turning the head away. However, this model may fore, while there is much shared variance among all be too liberal in its interpretation of the data on feedof the variables, someof theserelationships appear to ing difficulty and nurses, indeed, may not differentiate be stronger than others. Greater clarity, in terms of between indicators of feeding difficulty and actual any underlying dimensions in the correlation matrix, difficulties which elderly patients with dementia could be obtained by means of factor analysis. display. The inclusion of the 4 factor model will be The results of the exploratory factor analyses are shown in Table 2. Principal components analysis, using the criterion of Eigenvalues greater than 1, reduced the 11 variables to 3 factors (an Eigenvalue Table 3 “corresponds to the percentageof variance explained EQS output for structural equation modelling of the best fitting 2, 3 and 4 factor models by the equivalent number of variables” (Walsh, 1990)). However, the Eigenvalues were 5.26, 1.67 and No. of latent Model 3 Model 1 Model 2 1.06, respectively, for the three factors. As the first variables 4 3 2 two factors explain 47.8% and 15.1% (total 62.9%) 0.019 0.019 0.024 of the variance in the data, respectively, it could be AODASD* 60 78 60 argued that there are only two substantial factors. x2 Principalcomponentsanalysisfollowed by oblique rotation
This conservatism is supported size which is well beyond the
by the large sample
minimum 1 : 5 ratio of variables to subjects suggested by Child (1990) and the 1 : 10 suggestedby Kline (Kline, 1994).With such large sample sizes the possibility of finding variables with Eigenvalues greater than 1 is increased. For a fuller account of the interpretation of the factor matrix in relation to EdFED-Q seeWatson and Deary (1994). The loadings
of variables
on the first unrotated
prin-
Significance (W** BBNFIt BBNNFIf
0.001 (33)
0.005 (31)
0.005 (31)
0.969 0.969
0.976 0.979
0.979 0.988
* AODASD = Average off-diagonal absolute standardized residuals. p BBNFII = Ben&-Bonnet normed fit index. $ BBNNFI = Ben&-Bonnet non-normed fit index. ** df = degrees of freedom.
R. Watson and I. J. Deary/Feeding
difficulty in elderly patients with dementia
409
,240 c
s .272
,812 C ,769
,925
-
,621
).467
/
Fig. 1. Diagrammatic representation of the structural equations specifying the 2 factor model of feeding difficulty in elderly people with dementia. Rectangular boxes represent measuredvariables, circles represent latent variables and arrows without origins represent residual variance specific to the respective variables. Straight lines represent causal pathways, with arrows indicating the direction of causation and curved lines represent variables permitted to intercorrelate. Numbers adjacent to arrows represent parameter estimates for optimal model fitting from the EQS programme. Ql to Qll are explained in Table la.
explained below. The three models can be considered to be competing models for the representation of the data from the study. All three models are presented because of their superior fit, according to the criteria described above, compared with other possible models and because of their similarity according to these same criteria. From Table 3 it can be seen that the average offdiagonal absolute standardized residuals are small and very similar indicating that most of the covariance among the items has been accounted for by all of the models. The x2 values are significant but this is a function of the large sample size used in this study. Even very small differences between the predicted and observed residual correlations will be significant. Bentler-Bonnet fit indices of greater than 0.9 are taken to indicate an adequate fit of the model to the data and it can be seen from Table 3 that the fit indices for all three models are more than adequate. The
Lagrange multiplier test did not indicate any parameters with fixed values which might have been freed in order to improve the fit of the model to the data. Because all fixed parameters were those fixed at zero, the Lagrange multiplier test indicates that no additional pathways would improve the fit of the models. Goodness of fit between the models and the data has been achieved, generally speaking, by allowing items to load on more than one latent variable, and/or correlation between error terms. With large subject samples it is rarely the case that behavioural items will be so pure as to load on only one latent variable and have error variance entirely uncorrelated with that of any other item. Cross loading by variables on factors simply means that the variance of each item is explained by a combination of the variance from more than one factor. In cases where error terms have been allowed to correlate this recognises that variance
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R. Watson and I. J. Deary/Feeding difficulty in elderly patients with dementia
,324
.384
Fig. 2. Diagrammaticrepresentationof the structuralequationsspecifyingthe 3 factor modelof feeding difficulty in elderlypeoplewith dementia.For conventionsseelegendto Fig. 1.
assumedto be specific to one item may be similar to that of another item. This expedient measure combines variables with large amounts of unexplained covariance. Correlation between latent variables indicatesthat they are considered to be oblique, i.e. intercorrelated, for the purposes of the model. Model 1 (Eg. 1)
This is a 2 factor model where the 2 factors are allowed to correlate with one another. Variables Q4, QS, Q6, Q7, Q8 and Q9 load highly on Factor 1 and variables Ql and Q3 at very low levels. The variables loading on this factor describe aspects of patient behaviour (e.g. spitting) and incidents which indicate that a patient is having difficulty (e.g. spillage). Ql, Q2 and Ql 1 and to a lesser extent Q3 load highly on Factor 2. These variables describe nursing interventions (e.g. physical help). The intercorrelations between error terms in this model are between 43, Q4 and QS two of which (43 and 44) were previously described as indicators of
feeding difficulty and one of which (QS) is a general descriptor of patient refusal to eat. Variables Q8, Q9 and QlO which have universally intercorrelated error terms describe aspectsof patient difficulty which are oral in nature (e.g. spitting and refusing to swallow). Therefore, there is good reason to suppose that they might share some specific variance. It is a moot point whether the shared variance among these 3 items should be considered as correlated error variance or an identifiable additional latent variable. Model 2 (Fig. 2)
This is a 3 factor model in which all of the factors are correlated. Variables Q6, 47, Q8 and QlO and to a lesser extent Q9 load highly on Factor 1 and these are variables (e.g. spitting) which describe aspects of patient behaviour in feeding difficulty. Ql, 42 and Qll load highly on Factor 2, and these variables describe nursing intervention (e.g. physical help), and 4 other variables load moderately on this factor. Variables Q4 and Q5 load highly on Factor 3 and these
R. Watson and I. J. Deary/Feeding
difficulty in elderly patients with dementia
(c
411
1
,324
,269
1 ,527
Fig. 3. Diagrammatic representation of the structural equations specifying the 4 factor model of feeding difficulty in elderly people with dementia. For conventions see legend to Fig. 1.
variables are indicators of feeding difficulty and a general descriptor of feeding difficulty respectively. Intercorrelations between error terms in this model are between 43 and Q4 both of which are indicators of feeding difficulty and between Q8, Q9 and QlO all of which describe oral aspects of feeding difficulty (e.g. spitting and refusing to swallow).
ways of construing the same model, but the significant improvement of Models 2 and 3 over Model 1 can be gauged by comparing the difference in the value of x2 (reduced by 18) and the degrees of freedom (reduced by 2). Otherwise, for all 3 models the average of the off-diagonal absolute standardized residuals are low and, likewise, the fit indices are all acceptable.
Model 3 (Fig. 3)
Discussion
This is a 4 factor model in which the first three factors are as described above in Model 2. The assumptions underlying this model, however, are that the variance shared by QS, Q9 and QlO represent an additional latent variable or factor and that this factor is unrelated to the other 3 factors. QS, Q9 and QlO load moderately on Factor 4 and these variables all describe oral aspects of feeding difficulty (e.g. spitting and refusing to swallow). Inter-correlations between error terms in this model are between 43 and 44 both of which are indicators of feeding difficulty. Models 2 and 3 are really just theoretically different
As described in the introduction, previous analysis of data from elderly people with dementia, using exploratory factor analysis, has shown that a 3 factor structure may account for the variance in the data from the present study (Watson and Deary, 1994). A second exploratory factor analysis, reported here, pointed to the possibility that a 2 factor solution may also be possible. This conclusion is based on the Eigenvalues obtained in the present study and on the distribution of variables across the factors. The loading of variables on Factors 2 and 3 in the present exploratory factor analysis was different from the pre-
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vious study. However, the case for a 3 factor model is very convincing from the loadings of variables on putative factors following oblique rotation. The present study was carried out in order rigorously to test competing models for the data and the technique which was applied was confirmatory factor analysis. The present study also had the advantage, while including data from a previous analysis, of a much enhanced data set. The models tested all fitted the data well and a model with one factor less (i.e. 2 factors) than the results of the previously reported exploratory factor analysis was tested in addition to a 3 factor and a 4 factor model. The relative merits of these models will be discussed. While cross loading between factors was evident in all of the models, the factors were discriminated on the basis of high loadings on individual variables. In Model 1 (with 2 factors) the variables on which factors load highly are clearly divided between those which are related to the feeding difficulty of the patient (Factor 1) and those which are related to nursing actions (Factor 2). The variables loading on Factor 1 were described in the exploratory factor analysis study as “patient obstinacy or passivity” and those on Factor 2 as “nursing intervention”. The variables which loaded on the third factor in the previous study, labelled “indicator of feeding difficulty” have intercorrelated error terms in this model. It is possible, therefore, that this is not a broad latent factor but what is termed a “bloated specific”. In other words the different questions may be probing exactly the same very narrow aspect of feeding difficulty which should, ideally, be captured in a single item. Alternatively, a third broad factor may exist but the questions asked were inadequate for a clear distinction from Factors 1 and 2. The same argument applies to the intercorrelated error terms for variables related to oral aspects of feeding difficulty. It may be the case that these questions are simply probing the same aspect of feeding difficulty or that this part of the EdFED-Q has not been adequately developed as a more general dimension of feeding difficulty. Model 2 (with 3 factors) introduces a third factor which loads highly on variables which may indicate that a patient is having difficulty with feeding rather than describing the actual difficulty. As such this factor is very similar to the third factor from the previous exploratory factor analysis study. One intercorrelation remains between two other such descriptors and the inter-correlation between the oral aspects of feeding difficulty remain. This model offers some improvement over Model 1 because there are a greater number of high loadings of factors on variables and the number of intercorrelated error terms is reduced. Inspection of x2 values and degrees of freedom furthermore suggest a significant incremental fit to the data. The main feature of Model 3 (4 factors) is that the variables with intercorrelated error terms, relating to
the oral aspects of feeding dificulty, have been used to create a fourth factor. This factor loads moderately on all of the variables related to oral difficulty (Q8, Q9 and QlO) and there is, therefore, a case for the existence of a factor specifically related to oral aspects of patient obstinacy or passivity. This model is an improvement on Model 2 because, while there is the same number of high loadings of factors on variables, the number of moderate loadings is increased and the number of intercorrelated error terms is reduced to one. On the basis of the fit indices from the EQS analysis it was difficult to choose between the 3 models as representing the best fit to the data. However, it was important to test these models and specifically to test how a 2 and a 4 factor model compared with the 3 factor model suggested by the original exploratory factor analysis, The 2 factor model is more conservative and is based on the premise that some of the items in the questionnaire are redundant. In contrast, the 4 factor model is based on the premise that the questionnaire requires further development, especially in the area of a factor specific to oral aspects of feeding difficulty. Specifically, the 2 factor model suggests that questions probing different aspects of feeding difficulty should be added the EdFED-Q and the 4 model suggests that questions specific to oral aspects of feeding difficulty should be added. The two suggestions are not mutually exclusive and both suggest a future line of investigation with an improved version of the EDFED-Q applied to a new sample of elderly people with dementia. It should be noted that all of the factors intercorrelate (except the putative Factor 4 in Model 3). This intercorrelation between factors indicates that the various aspects of feeding difficulty are related ; there is, for example, a relationship between the difficulty which a patient has with feeding and the actions which nurses take to alleviate that difficulty. This suggests a line of investigation whereby it may be possible to investigate further any causal relationships among aspects of feeding difficulty.
Summary and conclusions The importance of the present study lies in its improvement upon the previous exploratory factor analysis study. The results of the previous study have largely been confirmed in terms of a 3 factor structure for feeding difficulty in elderly people with dementia. However, the possibility of a fourth factor specifically related to one aspect of patient difficulty has been raised by the present study. The consequences of this are that patient obstinacy and passivity may be more complex than previously suggested (Watson and Deary, 1994). The obstinacy of e.g. turning the head away and refusing to eat may be only one part of the problem of feeding difficulty and another aspect may be related specifically to oral difficulty. There may
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difficulty in elderly patients with dementia
be clinical implications for nursing assessment and intervention aimed at helping elderly people with dementia to feed adequately. Clearly, further development of the EdFED-Q is required if the feeding difficulty of elderly people with dementia is going to be more fully understood. In this light the confirmatory factor analysis, and the stepby-step approach to testing and comparing competing models, has proved very useful. On the one hand, as stated above, the factor structure obtained from exploratory factor analysis has been largely confirmed. On the other hand, the positing of a fourth factor (suggested by intercorrelated error terms in the 2 and 3 factor models) suggests an area of the EdFEDQ which could be developed further. The remaining intercorrelation between two of the variables related to indicating patient difficulty with feeding suggests another area for development. Further investigation should be aimed at increasing the number of questions related to any one of these aspects in order to test, in future, the resulting factor structure. This has implications for future scaling and measurement of single aspects of feeding difficulty in elderly patients with dementia. For future intervention studies, aimed at improving feeding in elderly people with dementia, reliable and validated scales will be required. The present work lays a foundation for the development of such scales and the design of meaningful and effective intervention studies.
Acknowledgements Financial support from the Gardner bequest to the Department of Nursing Studies at the University of Edinburgh is gratefully acknowledged. Nursing staff of the Lothian Health Trust, Scotland UK are gratefully acknowledged for their assistance.
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