Iranian women's social media addiction and sexual function
Description
The present cross-sectional study examined the chain mediating roles of spousal support and dyadic adjustment in the relationship between social media addiction and sexual functioning among 211 married women of reproductive age in Qazvin, Iran.
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Data analysis was conducted using SPSS version 27 and Smart PLS v3 software. Initially, descriptive statistical analysis was conducted including frequencies and percentages for categorical data, and means and standard deviations for ordinal data. The normal distribution of data was assessed using the Shapiro-Wilk test, along with examining central tendency and dispersion indices, skewness, and kurtosis. Pearson correlation coefficients were utilized to determine the relationships between the study variables, including sexual functioning, social media addiction, marital adjustment, and spousal support. To determine the mediating role of social media addiction and spousal support in the relationship between marital adjustment and sexual functioning among women, hypothesis testing was performed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) with the bootstrap method in Smart PLS. PLS-SEM can simultaneously test the direct, indirect, and total effects of each path. Model testing comprised two stages: the measurement model and the structural model. The first stage examined whether the existing data confirmed the proposed model using the relationships between observable and latent variables. The second stage examined measurement error and investigated the relationships between latent variables, assessing model fit. The measurement model was evaluated prior to testing the hypothetical relationships in SEM through confirmatory factor analysis because this step is a crucial prerequisite for SEM to identify valid theoretical and empirical constructs and to establish a model that is reliable and valid for the observations. The measurement model was based on theoretical considerations and focused on validity and reliability (Moksnes et al., 2016). SEM was employed to test the hypothetical relationships between the variables, and bootstrap confidence intervals (5000 samples) were used to determine whether the effects in the model were significant. Total effect, direct effect, indirect effect, and 95% confidence intervals were determined. A 95% confidence interval that does not include 0 indicates a significant effect.