MKTG2002: Quantitative Research Design Data Collection

Assessment 

Task Description

The purpose of this assessment is for you to gain expertise in quantitative data collection, analysing the data to identify patterns and relationships that will enable you to make managerial recommendations. You will conduct a survey and use industry-standard software to gain insights from the data which will form the basis for a professional report.

Skills

• Analytical skills & Digital skills: Quantitative research skills, in form of data collection using Qualtrics (online survey),
• Analytical skills & Digital skills: Quantitative research/data analysis skills (SPSS), as well as data interpretation skills,
• Communication skills: Report writing, and presentation skills.

Assessment Summary

The assessment required students to demonstrate expertise in quantitative research through a structured process of data collection, analysis, and interpretation, culminating in managerial recommendations. The key components included:

1. Survey Design and Data Collection Creating an effective survey using Qualtrics to gather relevant quantitative data.
2. Data Analysis Using SPSS to process survey responses, identify patterns, relationships, and statistical insights.
3. Data Interpretation Translating numerical findings into meaningful conclusions for business or managerial decision-making.
4. Professional Reporting Presenting findings in a formal report with clear explanations, visualizations, and actionable recommendations.

Learning objectives covered:

• Develop proficiency in quantitative research tools and software.
• Apply statistical and analytical techniques to real-world data.
• Communicate data-driven insights effectively to a professional audience.
• Make informed managerial or strategic recommendations based on evidence.

Step-by-Step Approach Guided by the Academic Mentor

1. Understanding the Assessment Requirements:

   • The mentor began by helping the student clearly understand the scope: focus on quantitative research skills, data analysis, and professional reporting.
   • Key learning outcomes (analytical, digital, and communication skills) were highlighted.

2. Survey Design Using Qualtrics:

   • The mentor guided the student in defining clear objectives for the survey.
   • Assisted in crafting precise, unbiased questions to collect reliable quantitative data.
   • Reviewed survey logic and flow to ensure clarity and respondent engagement.

3. Data Collection Process:

   • The student was trained to distribute the survey to the relevant audience while maintaining ethical research standards.
   • The mentor emphasized response tracking, quality control, and initial data cleaning to prepare for analysis.

4. Data Analysis in SPSS:

   • Step-by-step tutorials on importing survey data into SPSS.
   • Guidance on running descriptive statistics, correlation tests, and relevant inferential analyses.
   • Mentor helped the student interpret statistical outputs in line with the research objectives.

5. Data Interpretation and Report Writing:

   • The mentor instructed the student to translate numerical results into actionable insights.
   • Provided templates and feedback for presenting data visually (charts, tables) and explaining patterns.
   • Reviewed draft recommendations for clarity, relevance, and managerial applicability.

6. Final Outcome:

   • The student produced a comprehensive professional report detailing survey methodology, analysis, findings, and recommendations.
   • The report demonstrated proficiency in quantitative research methods and effective communication of insights to a managerial audience.

Conclusion

Through mentor-guided support, the student successfully completed the assessment by:

• Collecting robust data using Qualtrics.
• Analysing and interpreting data using SPSS.
• Presenting findings in a professional report with actionable recommendations.

Learning outcomes achieved:

• Mastery of quantitative data collection and analysis tools.
• Enhanced analytical and critical thinking skills.
• Ability to communicate complex data insights to business stakeholders.
• Application of evidence-based decision-making for managerial recommendations.

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