PRC Data Quality & Validation 2

Question 1

Which of the following best describes 'data cleaning' in the research process?

Accepted Answer

Identifying and correcting errors, inconsistencies, and outliers in a dataset prior to analysis

Answer

Data cleaning encompasses the systematic process of detecting and correcting inaccurate, incomplete, or inconsistent records to improve data quality before analysis.

Question 2

A researcher discovers that 5% of survey records in a dataset are exact duplicates. The most appropriate corrective action is to:

Accepted Answer

Remove duplicate records, keeping only one instance per unique respondent

Answer

Exact duplicate records represent the same respondent counted multiple times, which inflates sample size and biases results; removing all but one instance corrects this.

Question 3

Which of the following is a common method for handling missing data that involves predicting missing values based on other variables in the dataset?

Accepted Answer

Multiple imputation

Answer

Multiple imputation creates several plausible replacements for missing values based on observed data patterns, preserving statistical power and reducing bias better than deletion methods.

Question 4

In data validation, a 'range check' ensures that:

Accepted Answer

Numeric values fall within predefined acceptable boundaries

Answer

A range check verifies that all numeric entries fall within logically acceptable minimum and maximum boundaries (e.g., a 5-point scale should only contain values 1–5).

Question 5

The practice of verifying that a percentage distribution of responses closely mirrors known population benchmarks is called:

Accepted Answer

Benchmarking or external validation

Answer

Benchmarking or external validation compares survey marginals against known population statistics (e.g., census data) to assess whether the sample reflects the target population.

(PRC) Professional Researcher Certification Practice Test

(PRC) Professional Researcher Certification Practice Test

PRC Data Quality & Validation 2