Analysis of Online Questionnaire Data

Volunteering

Overview

Contribute to data analysis for educational research initiatives

You have:

• Proficiency in SPSS and/or SmartPLS according to assigned roles.
• Strong skills in data analysis, visualisation, or report writing depending on the role.
• Ability to work independently while collaborating remotely with the team.
• Familiarity with educational research or online questionnaire analysis is an advantage.

Contract

This is a UNV contract. More about UNV contracts.

This research project directly aligns with the United Nations Sustainable Development Goal 4 (SDG 4): Quality Education. By focusing on the relationship between student attention and academic achievement in community schools, this initiative supports MDF’s mission to enhance educational outcomes and promote inclusive, equitable quality education. Understanding these relationships enables the development of targeted interventions to improve learning environments and outcomes, particularly for underserved communities.

This assignment is meaningful as it provides critical data-driven insights that can shape effective strategies for fostering student success and empowering teachers with evidence-based tools. By contributing to this analysis, volunteers play an essential role in advancing SDG 4 and MDF’s efforts to ensure sustainable education solutions that positively impact the lives of students and teachers in community schools. The findings will also promote long-term educational development, furthering the resilience and capacity of local communities.

To achieve the project’s objectives, MDF requires the support of six motivated online volunteers. Each volunteer will be responsible for a specific aspect of the data analysis process, ensuring an organized and efficient workflow. Below are the assigned responsibilities and workloads for each volunteer:

Volunteer 1: Data Cleaning and Assisting in checking and writing the report - Preprocess the raw questionnaire data, checking for missing values, inconsistencies, and outliers. - Organise the data into a clean format suitable for analysis in SPSS and SmartPLS. - Assist in checking the work results for the other 5 volunteers. - Assist in editing the report for Volunteer 6, if necessary. - Around 16 hours per week, on the estimation.

Volunteer 2: Descriptive Statistics Support - Use SPSS to calculate descriptive statistics such as means, medians, frequencies, and standard deviations to provide an overview of the dataset. - Summarise key patterns related to student attention and achievement. - Around 16 hours for statistical computations and creating summary tables.

Volunteer 3: Correlation and Relationship Analysis - Conduct correlation analysis in SPSS to explore the relationships between variables (e.g., student attention and academic achievement). - Provide detailed interpretations of the results. - Around 16-20 hours per week on the estimation to analyze correlations and create visual representations (e.g., scatter plots).

Volunteer 4: Structural Equation Modeling (SEM) Analysis - Use SmartPLS to build and test structural equation models that evaluate the relationships between student attention, academic achievement, and other related factors. - Around 16-20 hours on the estimation to design, test, and refine the SEM models and provide insights into the structural relationships.

Volunteer 5: Data Visualization Support - Create professional tables, graphs, and figures based on the analysis results to clearly present the findings. - Work collaboratively with other volunteers to ensure consistency in visual outputs. - Around 16-20 hours per week on the estimation to design and finalize the visual representations.

Volunteer 6: Report Writing and Editing - Compile all analysis results into a cohesive report, summarising the findings and highlighting actionable insights. - Ensure the report is well-structured, grammatically correct, and aligned with MDF’s objectives. - Around 16-20 hours on the estimation to draft, edit, and finalize the report for submission to MDF.

1. Proficiency in SPSS and/or SmartPLS (specific to assigned roles).
2. Strong skills in data analysis, visualisation, or report writing (depending on the role).
3. Ability to work independently while collaborating remotely with the team.
4. Familiarity with educational research or online questionnaire analysis is an advantage.

Apply now

Potential interview questions

Added 1 year ago - Updated 1 year ago - Source: unv.org