Métodos empleados para obtener nuevas ideas

The reliability o f a scale is a fundamental way to reflect the amount o f error, random and systematic, inherent in any measurements (Streiner and Norman, 1995). Two frequently used indicators o f a scale’s reliability are test-retest reliability and internal consistency. The test-retest reliability o f a scale is assessed by administering it to the same people, on the same difference occasions, and calculating the correlation between two scores obtained. It refers to the reproducibility or consistency o f the instruments.

Intraclass Correlation Coefficient (ICC), unweighted kappa and weighted kappa can be used to test test-retest correlations. High test-retest correlations indicate a more reliable measure (Pallant, 2003).

The second aspect o f reliability that can be assessed is internal consistency. This is the degree to which the items that make up the scale are all measuring the same underlying attribute. Internal consistency can be measured a number o f ways. The commonly used statistics are the inter-item correlation, the corrected item-total correlation and Cronbach’s coefficient alpha (Pallant, 2003).

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The validity o f a scale refers to the degree to which it measures what it is supposed to measure. Unfortunately there is no one clear-cut indicator o f scales validity (Pallant, 2003). However, many different approaches can be used to assessing validity in different situation. Usually, face and content validity, criterion validity and construct validity are used to test the validity o f a scale. The terms face validity and content validity are technical descriptions o f the judgem ent that a scale looks reasonable. Both face and content validity refers to a subjective judgement by experts and some empirical persons whether the scale appears appropriate for the intended purpose (Streiner and Norman, 1995). Criterion validity refers to the correlation o f a scale with some other measure that is accepted as the “gold standard”, while construct validity involves testing a scale score, not against a single criterion, but in terms o f theoretically derived hypotheses concerning the nature o f underlying variable and construct and is a way o f assessing validity by investigating its relationship with other constructs. If other scales o f the same or similar attributes are available, criterion validity should be tested. If no such other measure exists, construct validity testing becomes even more important. In the absence o f criterion, some evidence o f construct validity should be available (Streiner and Norman, 1995).

Testing the Index of Eating Difficulty (IED)

There are three kinds o f scaling responses, namely nominal variable, ordinal variable and interval variable (Streiner and Norman, 1995). Generally speaking, rating scales, where the response is on a five-point or seven-point scale, are not considered interval level measurements since we can never be sure that the distance between “strongly disagree” and “disagree” is the same as between “agree” and ” strongly agree” (Streiner and Norman, 1995). In psychological and attitudinal research, some techniques, such as

Guttman scaling, have been developed to combine individual items into scales and indices (Mclver and Carmines, 1981). The Guttman scaling is a means o f analysing the underlying characteristics o f several items in order to examine how closely a set o f items corresponds with the idea o f cumulativeness (Petersen, 1989). Guttman scales are unlikely to have interval scale properties but ordinal scale (Streiner and Norman, 1995).

Guttman scaling has been widely used in the area o f attitude measurement in survey research (Leake, 1990; Petersen, 1989).

The IED is a new index, which has not been used before. It is necessary to establish its reliability and validity. The purpose o f the present study was to develop and evaluate the Guttman Scaling-Index o f Eating Difficulty (IED). The IED is based on certain Chinese foods, whether people could ordinarily eat them. The IED were computed by summing the responses to every item they were able to eat. If people could eat, the answer was

“yes” which is coded as 1. Conversely, if people could not eat food, the answer was

“no” which is coded as 0. Then the order o f frequency o f “yes” responses was arranged and the patterns o f responses were examined.

There are two important properties, namely reproducibility and scalability to test the reliability o f the index. The Coefficient o f Reproducibility (CR) is a measure o f the extent to which a responses scale is a predictor o f the respondent’s pattern. The formula for this coefficient is CR = 1.0 - (no. errors) / [(no. items) x (no. respondents)] (Streiner and Norman, 1995). CR can vary between 0 and 1, and should be higher than 0.9 for the scale to be reliable. The Coefficient o f Scalability (CS) is truly unidimensional and cumulative. The formula for this coefficient is CS = 1.0 — (no. errors) / Maximum errors.

CS also vary 0 to 1, and should be at least 0.6 (Streiner and Norman, 1995).

The validity o f IED was tested as well. The face and content validity was tested by

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comments from two experts o f dentistry, one expert on nutrition and one expert on statistics. Then administration o f questionnaire was test on 205 Chinese elderly in the second pilot study. Informal discussions took place, aiming to improve the understanding o f the content o f the questionnaire. Since no other “gold standard” index existed, three other measures - General Eating Difficulty (GED), Dissatisfaction with Chewing ability (DCA) and OIDP eating impact were used to test construct validity.

The IED is a dichotomous variable. Therefore, the Chi-Squared test was used.

106 subjects were involved for test-retest reliability. Weighted kappa was calculated on Index o f Eating Difficulty because IED was an ordinal variable.

Testing the OIDP index

The Oral Impacts on Daily Performances (OIDP) index (Adulyanon and Sheiham, 1997) has been widely used. However, every time a scale is used in a new context or with a different group o f people, it is necessary to re-establish its psychometric properties (Streiner and Norman, 1995). In this study, the OIDP was applied in Mainland China for the first time. Therefore, it was necessary to retest its psychometric properties, which refer to reliability and validity. Reliability, in terms o f rest-retest reliability and internal consistency were tested, while validity, in terms o f face, content, construct validity was also assessed in the present study.

Related to internal consistency, three measures, the inter-item correlation, item-total correlations and Cronbach’s alpha (a) Coefficient (Cronbach, 1951), which is based on the average correlation among the items and the number o f items in the scale (Streiner and Norman, 1995), were used in this study.

There were two stages related to the assessment o f the face and content validity of the

OIDP index. In the first stage, a panel o f Chinese experts in dentistry, two epidemiologists and a medical statistician gave the comments on the face and content validity before the second pilot study. Second stage, OIDP was tested on subjects in the second pilot study. Informal detailed discussion, some wording modifications and the comprehensiveness o f the OIDP index implied in order to explore the relevance and understand o f the content o f the questionnaire. All changes were done before the main study. Construct validity was tested by investigation o f relationship o f OIDP scores and self-perceived dental treatment, self-perceived oral health and self-perceived general health using Kruskal-Wallis tests.

106 subjects were re-interviewed for test -retest reliability throughout the whole process o f study. Weighted kappa was calculated on the OIDP categories and ICC for OIDP scores.

The assessment o f the reliability and the validity of the OIDP index refer to the total sample including edentate people and dentate people.

4.4.5.2. Descriptive analysis

Descriptive results were conducted in order to identify the main patterns o f data. This included information about sample distribution, demographic background, dental status, eating difficulty, oral impact, self-perceived dry mouth, and self-perceived general health. The number o f teeth, number o f occluding pairs and number o f unfilled spaces which were continued data but not normally distributed while sociodemographic data were categorical variables. Therefore, the non-parametric statistical test (Mann-Whitney for binary variables, Kruskal-Wallis for variables with more than two categories) was used for the relationship between clinical dental status and sociodemographic variables.

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4.4.5.3. Univariate analysis

Before performing a logistic regression, it is necessary to carry out univariate analysis to explore the patterns o f relationships between the variables. The results o f univariate analysis guided the decision for which variables to include and adjust for in the multiple logistic analysis

The Chi-Squared test was used to determine if two categorical variables are related.

Index o f Eating Difficulty (IED), General Eating Difficulty (GED) and Dissatisfaction with Chewing Ability (DCA), OIDP eating impact and self-perceived dry mouth are dichotomous variables (yes/no). Therefore, the Chi-Squared test could be used to explore the relationships between IED, GED, DCA, OIDP eating impact, and self-perceived dry mouth and sociodemographic factors, and dental status. For dichotomous explanatory variables (such as sex, occupation, self-assessed social class), the Chi-Squared test was used. For those explanatory variables that had more than two categories (like age group, number o f teeth, number o f occluding pairs and number of unfilled spaces), the Chi-Squared test for trend was used.

Mann-Whitney and Kruskal-Wallis tests are appropriate to test the significance of the association between a categorical variable and a continuous variable. The OIDP scores were continuous variables and not normally distributed. Therefore, the same non-parametric statistical tests (Mann-Whitney for binary variables, Kruskal-Wallis for variables with more than two categories) were used to compare the scores on different groups o f explanatory variables.

Simple logistic regression analyses were also used as part o f the univariate analysis to test the unadjusted relationships between potential explanatory variables and the

outcome o f interest.

A p value equating to 0.05 is considered statistically significant. When comparing each o f the three groupings o f the numbers o f teeth, the numbers o f occluding pairs and the numbers o f unfilled spaces with each other, modified Bonferroni correction was used to adjust the alpha value (Keppel, 1991). Here, alpha was set at less than 0.05.

4.4.5.4. Multiple logistic regression

This section investigated the relationships between clinical dental status and Index of Eating Difficulty (IED), General Eating Difficulty (GED), Dissatisfaction with Chewing Ability (DCA), binary OIDP and OIDP eating impact. It also explored the relationship between eating difficulty and oral health-related quality o f life (OIDP). Due to GED, DCA and OIDP eating impact were a dichotomous variable, IED was an ordinal variable and OIDP scores were not normally distributed. The investigation of the way that these outcome variables were associated with explanatory variables should be done using the logistic and not the linear regression. When multiple logistic regression analyses were run, a set o f adjustment variables were entered into the model along with one clinical variable. Subsequently, different regression analyses were carried out for each clinical variable.

For the regression models, p-values were obtained from the Wald test, and estimated odds ratios and their 95% confidence limits were determined. Here alpha was set at 0.05.

The outcome measures (IED, GED, DCA, OIDP eating impact, Binary OIDP) could be influenced by non-clinical and clinical variables simultaneously. In order to trace the true relationships between the eating difficulty, oral health-related quality o f life and the

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clinical variables, the initial results would have to be adjusted. As the different clinical measures were used alternately, the adjustment process would help to avoid confounding by non-clinical variables.

The rate o f tooth loss increases with age (Marcus et al., 1996). In addition, age was associated with oral health-related quality o f life (Steele et al., 2004). The results from the second national survey o f oral health status in China showed that men had more remaining natural teeth than women (Wang et al., 2002). The results from the initial analysis in this study showed that occupation and self-assessed social class were associated with eating difficulty and oral impacts. Therefore, sociodemographic factors (including age, sex, occupation and self-assessed social class) were adjusted in the first adjusted model. Furthermore, studies showed that self-perceived general health was related to chewing ability (Miura et al., 2005) and well-being (Murata et al., 2006). The second adjusted model was adjusted by age, sex, occupation and self-assessed social class plus self-perceived general health. For exploring the relationships between eating difficulty and OIDP scores, and OIDP eating impact, one more adjusted model was needed in order to include clinical variables. This model, apart from the effects of age, sex, occupation and self-assessed social class, self-perceived general health, it also adjusted for the numbers o f OPs, OPRs and self-perceived dry mouth, since the latter aforementioned variables were also significantly related to eating difficulty and oral health-related quality o f life.

Chapter 5

5. Results

5.1. Introduction

This chapter presents the findings o f this study. Firstly, general results including response rate and the results from reliability and validity tests are presented. Secondly, descriptive results are displayed in section 5.3. This includes details about the sample and frequency distribution o f the variables in the study. Then the results from univariable and logistic regression analyses looking at the relationships o f clinical dental status and eating difficulty, and oral health-related quality o f life are presented on section 5.4 and 5.5. Next, the results regarding the relationships between eating difficulty and OIDP scores, and OIDP eating impact are displayed in section 5.6. Finally, a summary o f the main findings is presented in Section 5.7.

5.2. General Results