The Psychometric Properties of the Addiction to Medical Website Scale (AMWS)

Abstract

Background: Addiction to Medical Website AMW is a kind of behavioral addiction characterized by excessive online health research. This leads to an unpleasant state of mind, health anxiety, and anxiety. Still, the measurement of AMW needs further research efforts. Aims: The present study aimed to develop a scale to assess the Addiction to Medical Website Scale (AMWS) according to DSM-5 and to evaluate its structure, reliability and validity. Method: A survey-based investigation has been carried out among a sample of university undergraduate students (N=220). Two different types of factor analysis are performed, i.e. exploratory factor analysis (EFA) to identify correlation among scale items and domains, and confirmatory factor analysis (CFA) to validate the developed scale. Three-factor structure (i.e. domains) were labelled as Independency, Preoccupation, and Obsession, Negative emotions and social influences, and Excessive use. This study’s findings were validated using CFA conducted in smart PLS using convergent validity and discriminant validity. Results: Reliability of scale was assessed using Cronbach’s alpha, composite true reliability, and composite reliabilities for the overall scale, and the three dimensions are within the acceptable range, i.e., from .866 to .959. Out of 20 Scale Items, 11 items were found highly correlated with factor loading more than 0.708. Scale validity was accessed using Average Variance Extracted (AVE) and via heterotrait-monotrait ratio of correlations (HTMT) and found more than a threshold of 0.90. The highest correlated dimension is found as Negative Emotions and Social Influences (FS: 0.735), followed by Independency, Preoccupation, & Obsession (FS: 0.695), and the last being Excessive Use (FS: 0.681). Conclusions: The AMW Scale demonstrated good psychometric properties, as can be seen from the results. The majority of Scale Items were correlated with high factor loading. The validity of scale further confirms the applicability of scale in large settings. This scale may be helpful for future researchers to validate it again in many other communities.