Psychometric Properties of the Persian Language Version of Yang Internet Addiction Questionnaire: An Explanatory Factor Analysis

Reliability and validity are the key concepts in measurement processes. Young internet addiction test (YIAT) is regarded as a valid and reliable questionnaire in English speaking countries for diagnosis of Internet-related behavior disorders.  This study aimed at validating the Persian version of YIAT in the Iranian society.  A pilot and a cross-sectional study were conducted on 28 and 254 students of Qom University of Medical Sciences, respectively, in order to validate the Persian version of YIAT. Forward and backward translations were conducted to develop a Persian version of the scale. Reliability was measured by test-retest, Cronbach’s alpha and interclass correlation coefficient (ICC). Face, content and construct validity were approved by the importance score index, content validity ratio (CVR), content validity index (CVI), correlation matrix and factor analysis. The SPSS software was used for data analysis.  The Cronbach’s alpha was 0.917 (CI 95%; 0.901 - 0.931). The average of scale-level CVI was calculated to be 0.74; the CVI index for each item was higher than 0.83 and the average of CVI index was equal to 0.89. Factor analysis extracted three factors including personal activities disorder (PAD), emotional and mood disorder (EMD) and social activities disorder (SAD), with more than 55.8% of total variances. The ICC for different factors of Persian version of Young Questionnaire including PAD, EMD and for SAD was r = 0.884; CI 95%; 0.861 - 0.904, r = 0.766; CI 95%; 0.718 - 0.808 and r = 0.745; CI 95%; 0.686 - 0.795, respectively.  Our study showed that the Persian version of YIAT is good and usable on Iranian people. The reliability of the instrument was very good. Moreover, the validity of the Persian translated version of the scale was sufficient. In addition, the reliability and validity of the three extracted factors of YIAT were evaluated and were acceptable.

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Reports frequently indicated content validity but sometimes the studies had fewer than five experts for review. Criterion validity was rarely reported and errors in the measurement of the criterion were identified. Construct validity remains underreported. Most reports indicated internal consistency reliability (alpha) but few reports included reliability testing for stability. When retest reliability was asserted, time intervals and correlations were frequently not included.



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abstract: The main goal of the present study is to investigate the psychometric properties of a French version of the Internet Addiction Test (IAT) and to assess its relationship with both time spent on Internet and online gaming. The French version of the Young's Internet Addiction Test (IAT) was administered to a sample of 246 adults. Exploratory and confirmatory analyses were carried out. We discovered that a one-factor model of the IAT has good psychometric properties and fits the data well, which is not the case of a six-factor model as found in previous studies using exploratory methods. Correlation analysis revealed positive significant relationships between IAT scores and both the daily duration of Internet use and the fact of being an online player. In addition, younger people scored higher on the IAT. The one-factor model found in this study has to be replicated in other IAT language versions.

title: Internet addiction: characteristics of a questionnaire and prevalence in Norwegian youth (12-18 years).
abstract: The use of the Internet has increased considerably during the last few years, and there are also some clinical observations that some people 'get hooked', and develop an Internet addiction. An epidemiological study was performed in a representative sample of the Norwegian youth population (N= 3,237; response rate 45.2%). The proportion not using Internet was only 4.9%, while 35.8% were non-frequent users, and 49.6% (1,591) were frequent (weekly) users. They used the Internet on the mean 4.3 hours a week. A mean of 1.98% (boys 2.42%, girls 1.51%) could be described as having an 'Internet addiction' according to the criteria in the Diagnostic Questionnaire of Young (1998), and an additional 8.68% (in sum 10.66) were considered to have an at-risk Internet use (boys 9.21%, girls 8.13%). If these proportions were calculated on individuals who used the Internet frequently, 4.02% fulfilled 5 criteria, and an additional 17.66% 3-4 criteria, giving quite high figures of problematic Internet use (in sum 21.68). The different diagnostic criteria gave a broad range of affirmative answers (from 0.4% to 27.9%). The results have important implications for further studies of prevalence, implementation of preventive measures, and the development of treatment approaches for Internet addiction.

title: Applied Multivariate Statistical Analysis
abstract: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED. 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments. 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A Square-Root Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization.  The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables. 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. Large-Sample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS. 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations.  Comparison of Several Multivariate Population Means (One-Way MANOVA). Simultaneous Confidence Intervals for Treatment Effects. Two-Way Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models. 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors.  The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE. 8. Principal Components.  Summarizing Sample Variation by Principal Components. Graphing the Principal Components. Large-Sample Inferences. Monitoring Quality with Principal Components.  The Orthogonal Factor Model.  Perspectives and a Strategy for Factor Analysis.  The Sample Canonical Variates and Sample Canonical Correlations.   Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments. 12. Clustering, Distance Methods and Ordination.  Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables.  A Method for Comparing  (...a = .10). F-Distribution Percentage Points (...a = .05). F-Distribution Percentage Points (...a = .01). Data Index.