Structural Equation Model to Predict Subjective Quality of Life: A Comparison of Scales with Different Numerical Anchoring

Article excerpt

Objective: The main aim of the current survey was to evaluate a hypothesized model on subjective quality of life (SQOL) ,and to survey the role of scale anchoring on satisfaction and dissatisfaction ratings.

Method: The sample consisted of 456 volunteering students who were randomly assigned in to two different conditions, and rated their current overall life (dis)satisfaction and their (dis)satisfaction on six different domains of life. Each condition used one of the two rating scale formats; the formats differed in anchoring (-5 to +5 and 0 to 10). In order to find how the six different domains of life combine to produce an overall measure of subjective quality of life, a SQOL model was designed; and the strength of this hypothesized model of SQOL was examined using structural equation modeling.

Results: The results of testing for multiple group invariance of the hypothesized model indicated a cross-validity for the studied model for measuring SQOL. Our results also indicated that comparing the two different response formats, only for scores derived from Horizontal (0 to 10) response format, all the paths in the model were found to be significant.

Conclusion: The results of the confirmatory factor analysis (CFA) support the conclusion that the proposed model of SQOL fit the data well, and is able to predict SQOL.

Key words: Psychometrics, Quality of life, Satisfaction, Dissatisfaction

Iran J Psychiatry 2010; 5:134-139

The debate among researchers as to the "ideal" rating format has an extensive history. A desired effect of the rating scale method is to provide subjects with a format that allows them to make equal interval judgments thus meeting statistical assumptions of an interval scale of measurement. However, while the rating scale provides a powerful tool for investigating a wide variety of phenomenon, investigations of rating scale function reveal performance anomalies across scale formats .

Rating scales differ in the number of categories as well as number and placement of labels to aid in selection of a category. "Label" is verbal, descriptive statements placed at various locations along vectors of possible response options. Frequently, these options are numbers of increasing and/or decreasing magnitude. The respondent's task is to select the numerical response option associated with the appropriate label that he/she perceive to be the best representation of his/her attitude or belief on a latent trait. There are several characteristics of response formats that are of relevance to the quality of survey data, ranging from the labeling of response categories and the issue of

administering scales with or without midpoints, to the question of whether response categories are ordered from positive to negative or the other way around.

Rating scales can be presented as a bipolar or unipolar format. There are two ways in which we may signal to respondents whether we wish them to treat a response scale as unipolar or bipolar. The usual way is by using verbal anchors which are either unipolar (eg [no more power, much more power], [not having any success, having great success]) ,or bipolar (eg [much more power, much less power}, [much success, much failure]). The second way, as applied in this research, is to use numeric labels which either imply a unidimensional construct (eg [0 to 10], [0 to 5], [0 to 6], [-5 to 0]) ,or a bipolar construct (e.g. [+5 to -5], [+3 to -3], [+2 to -2]) (1).

While the numeric values are often included only for coding and response convenience, Schwartz and co-workers (2) have demonstrated that they carry more, sometimes unintended, meanings. For a particular question, "How successful have you been in life, so far?", they showed that a scale with numeric values ranging from 0 to 10 was not the same as a scale whose values ranged from -5 to +5. The verbal anchors were "not at all successful" (0 or -5) and "extremely successful" (10 or +5). …