Data Cleaning and Preparation Procedures for Survey-Based Research: Key Questions and Guidelines

Questions

1. How many people started the survey and how many people completed the survey (2 marks)

1. Are there any missing values? How did you deal with them? (2 marks)

2. Were there any outliers? How did you determine this? How did you deal with them? What are the relevant summary statistics before and after dealing with the outlier(s)? (3 marks)

3. What is reverse scoring and what function does it serve? (1 marks)

4. Provide the descriptive statistics (Mean and Standard deviation) for the variables in Study 1 and the groups in Study 2 (2 marks)

Frequently asked questions about Workbook #2 will be posted here and updated regularly.

1. What are the key steps involved in data cleaning?

Cleaning data involves:

1. Checking your variables
2. Dealing with missing data
3. Checking for invalid data
4. Reverse Scoring
5. Calculating totals or means
6. Checking for outliers

2. Is there any guidance?

The week 5 tutorials cover this content and process in Jamovi to clean data. A recording of this tutorial is available on vUWS.

3. Where is the dataset?

Week 5 tutorial on vUWS. It is the unclean_data.omv Jamovi file that you need to clean. You will then use this dataset moving forward into all other assessments.

4. Which variables should be examined for outliers?

Only variables relevant to your hypotheses.

5. Do the questions count in the word count

Brief of Assessment Requirements 

You are required to produce a short data-cleaning report based on Workbook #2 (unclean_data.omv). The report must answer a set of specific questions (each carries the marks shown) and demonstrate standard data-preparation procedures in Jamovi. Key items to cover:

1. Survey completion counts : number who started vs number who completed (2 marks).

2. Missing data : identify if missing values exist and describe the method used to handle them (2 marks).

3. Outliers : detect, justify method, handle them, and report summary statistics before/after treatment (3 marks).

4. Reverse scoring : define and explain purpose (1 mark).

5. Descriptive statistics : report mean and standard deviation for variables in Study 1 and for the groups in Study 2 (2 marks).

Supporting guidance: show Jamovi procedures/screens used, include brief justification for decisions (e.g., why chosen missing-data method), document any data transformations, and include screenshots or small tables in the appendix if requested. Follow unit rules about format and submission; use the Week 5 Jamovi tutorial recording as process guidance.

How the Academic Mentor Guided the Student 

Step 1 : Understand the questions & locate the dataset

• Mentor confirmed the exact deliverable: a short data-cleaning report answering the five questions with clear justification and reproducible steps.

• Located the dataset (unclean_data.omv) on vUWS and opened it in Jamovi.

Step 2 : Survey start vs completion (How many started, how many completed)

• Method taught: Identify a completion indicator:

  • If the dataset contains a completion or progress column, use that to count completed records.

  • If not, define completion operationally (e.g., respondent provided non-missing responses for all core outcome items or a minimum threshold of answered questions).

• Jamovi steps: Use Data > Filters and Frequencies (or Descriptives) to count all rows (started) and count rows meeting the completion criteria (completed).

• Reporting: Present as and compute completion rate = Y/X ×100%.

Step 3 : Assess missing values and decide how to deal with them

• Detecting missingness: Mentor showed how to run Descriptives > Missing Values in Jamovi or create frequency tables to get counts/percent missing per variable.

• Diagnosis: Assess whether missingness is MCAR, MAR or MNAR (simple checks: compare missing vs non-missing on key demographics; perform Little’s MCAR test if available).

• Decision rules taught:

  • If missingness is minimal (<5>

  • If missingness moderate (5–20%) or systematic → use multiple imputation or model-based handling; report the approach.

  • If key variables have high missingness (>20%) consider excluding variable or collect more data.

• Implementation in Jamovi: Demonstrate simple mean/median imputation (for demonstration) and recommend using mi packages or exporting to R for multiple imputation if required.

• Reporting: State number/percent missing per variable and the chosen method with rationale.

Step 4 Detect and handle outliers

• Detection methods explained:

  IQR method: points beyond Q1 − 1.5×IQR or Q3 + 1.5×IQR (boxplot visual).

  • Z-score method: absolute z > 3 as candidate outlier.

  • Visual checks: boxplots and scatterplots in Jamovi.

• Determination: Mentor taught to check whether extreme values are data entry errors vs true extremes (verify raw case).

• Treatment options & selection rules:

  • Correct obvious data-entry errors (change to correct value or set to missing with note).

  • Winsorize (cap extreme value to nearest non-extreme value) if retaining case needed.

  • Exclude case if outlier unduly influences analysis and is not representative (must justify).

• Jamovi steps: Create boxplots and compute z-scores; use Filters to run analyses before/after handling.

• Reporting requirement: Provide relevant summary statistics (mean ± SD) for the affected variable(s) before and after outlier handling, and briefly justify the chosen approach. (Example format shown below.)

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