Yue Yan1, Tao Xin1, Dahua Wang1, Dan Tang2. 1. School of Psychology of Beijing Normal University, Beijing, China. 2. Center for Population and Development Studies, Renmin University of China, Beijing, China.
Abstract
BACKGROUND: The Geriatric Anxiety Inventory (GAI) was developed to assess anxiety in older adults. The objectives of this work were as follows: (a) to analyze the psychometric properties of the Chinese version of the GAI (GAI-CV), and (b) to explore the extent of anxiety and related factors in the elderly Chinese residents of Beijing. METHODS: Participants in this study included 1,047 people (59.4% female) more than 60 years old who were living in the community. They were randomly selected from 15 communities in Beijing. Basic information was collected. Anxiety was measured using the GAI-CV, the Self-Rating Anxiety Scale (SAS), and the Beck Anxiety Inventory (BAI). RESULTS: The GAI-CV exhibited good internal consistency (Cronbach's α = 0.94) and demonstrated good concurrent validity against the SAS (r = 0.52, p = 0.018) and the BAI (r = 0.560, p = 0.000). Item response theory (IRT) analyses showed that the items of the GAI-CV exhibited high difficulty (0.97-2) and discrimination parameters (1.91-5.33). The items exhibited information parameters greater than 1.25 with the exceptions of items 2, 12, and 18. The GAI-CV scores were significantly associated with gender, age, and chronic disease. However, no significant differences due to marriage or education were found. CONCLUSIONS: The GAI is a new scale that was specifically designed to measure anxiety in older people. The results of this study suggest that the GAI-CV had good psychometric properties, but some items need to be modified. IRT analyses indicated that the GAI-CV provided good measures of anxiety across the moderately high to very high levels. The GAI-CV may be a useful instrument for further research studies aimed at analyzing high-level anxiety among older adults in China.
BACKGROUND: The Geriatric Anxiety Inventory (GAI) was developed to assess anxiety in older adults. The objectives of this work were as follows: (a) to analyze the psychometric properties of the Chinese version of the GAI (GAI-CV), and (b) to explore the extent of anxiety and related factors in the elderly Chinese residents of Beijing. METHODS:Participants in this study included 1,047 people (59.4% female) more than 60 years old who were living in the community. They were randomly selected from 15 communities in Beijing. Basic information was collected. Anxiety was measured using the GAI-CV, the Self-Rating Anxiety Scale (SAS), and the Beck Anxiety Inventory (BAI). RESULTS: The GAI-CV exhibited good internal consistency (Cronbach's α = 0.94) and demonstrated good concurrent validity against the SAS (r = 0.52, p = 0.018) and the BAI (r = 0.560, p = 0.000). Item response theory (IRT) analyses showed that the items of the GAI-CV exhibited high difficulty (0.97-2) and discrimination parameters (1.91-5.33). The items exhibited information parameters greater than 1.25 with the exceptions of items 2, 12, and 18. The GAI-CV scores were significantly associated with gender, age, and chronic disease. However, no significant differences due to marriage or education were found. CONCLUSIONS: The GAI is a new scale that was specifically designed to measure anxiety in older people. The results of this study suggest that the GAI-CV had good psychometric properties, but some items need to be modified. IRT analyses indicated that the GAI-CV provided good measures of anxiety across the moderately high to very high levels. The GAI-CV may be a useful instrument for further research studies aimed at analyzing high-level anxiety among older adults in China.
Authors: Helge Molde; Inger Hilde Nordhus; Torbjørn Torsheim; Knut Engedal; Anette Bakkane Bendixen; Gerard J Byrne; María Márquez-González; Andres Losada; Lei Feng; Elisabeth Kuan Tai Ow; Kullaya Pisitsungkagarn; Nattasuda Taephant; Somboon Jarukasemthawee; Alexandra Champagne; Philippe Landreville; Patrick Gosselin; Oscar Ribeiro; Gretchen J Diefenbach; Karen Blank; Sherry A Beaudreau; Jerson Laks; Narahyana Bom de Araújo; Rochele Paz Fonseca; Renata Kochhann; Analuiza Camozzato; Rob H S van den Brink; Mario Fluiter; Paul Naarding; Loeki P R M Pelzers; Astrid Lugtenburg; Richard C Oude Voshaar; Nancy A Pachana Journal: J Gerontol B Psychol Sci Soc Sci Date: 2020-08-13 Impact factor: 4.077