Parent-School Communication via Primary School Class WeChat Groups: Patterns and Responsibility Allocation Attitudes

Description

This data warehouse contains the datasets, scripts, and results related to the study on parent-school communication behaviors and responsibility allocation in class-based WeChat groups. The repository includes raw data from 1,286 parents, processed analysis outputs (Latent Class Analysis and Correspondence Analysis), and the corresponding scripts used for data processing and analysis. For detailed descriptions of the folder structure, data files, and scripts, please refer to the ReadMe.txt file located in the root directory of the warehouse.

Steps to reproduce

Step 1: Data Preparation 1.1 Download and extract all files from the data warehouse. 1.2 Ensure the following tools are available: SPSS: Used for performing Correspondence Analysis (CA); Mplus: Used for performing Latent Class Analysis (LCA); Stata: Used for performing Bootstrap analysis and calculating Cramér's V. Step 2: Latent Class Analysis (LCA) The scripts include functionality for data import, saving, and result storage. 2.1 Calculate Cramér's V: Run the script\cramersv_lca_script\cramersv_lca.do script in Stata to calculate the Cramér's V coefficient between the communication perception variables, assessing the correlations among these variables. 2.2 Latent Class Analysis (LCA): Use Mplus to run the following input files for latent class analysis: lca_2_profiles.inp: Generates a 2-class latent profile model; lca_3_profiles.inp: Generates a 3-class latent profile model; lca_4_profiles.inp: Generates a 4-class latent profile model; lca_5_profiles.inp: Generates a 5-class latent profile model. 2.3 Model Selection: Choose the optimal model based on indicators such as AIC, BIC, aBIC, and Entropy. Step 3: Correspondence Analysis (CA) The scripts include functionality for data import, saving, and result storage. 3.1 Use Stata to run the script\bootstrap_script\bootstrap.do script to perform a chi-square analysis, evaluating the relationship between parental responsibility allocation attitudes and latent classes of communication perception. 3.2 Use Stata to run the script\cramersv_lca.do script to perform a bootstrap analysis of the chi-square statistics, assessing the robustness of the statistical results. 3.3 Use SPSS to run the script\ca_script\ca.sps script to perform Correspondence Analysis (CA). This step analyzes the relationship between parent-school responsibility allocation and latent communication perception classes.

Institutions

• Yunnan University

Categories

Licence