PRC Data Quality & Validation 3

Question 1

Which approach to data validation focuses on whether different questions measuring the same underlying concept yield consistent results?

Accepted Answer

Internal consistency validation

Answer

External benchmarking

Answer

Fieldwork audit

Answer

Cross-tabulation analysis

Question 2

A quality assurance protocol requires that 10% of completed interviews be re-contacted by a supervisor to verify key responses. This process is known as:

Accepted Answer

Validation callbacks

Answer

Quota verification

Answer

Response weighting

Answer

Data imputation

Question 3

When cleaning secondary data obtained from an external source, researchers should FIRST:

Accepted Answer

Assess the credibility, currency, and methodology of the original data source

Answer

Apply weighting schemes to match their primary research sample

Answer

Recode all variables into the same format used in their primary research

Answer

Delete any variables not directly relevant to their research objectives

Question 4

Which of the following is the MOST appropriate action when an outlier is identified in a quantitative dataset?

Accepted Answer

Investigate whether the outlier represents a data error or a genuine extreme value before deciding how to handle it

Answer

Always delete the outlier to prevent it from distorting results

Answer

Replace the outlier with the variable mean automatically

Answer

Retain the outlier without any review since all data points have equal validity

Question 5

A researcher applies a rule that any respondent who completed a survey in less than one-third of the median completion time is automatically disqualified. This threshold is an example of a:

Accepted Answer

Speeder removal criterion

Answer

Completion rate benchmark

Answer

Non-response adjustment

Answer

Incidence rate filter

Question 6

In a large telephone survey, a quality control supervisor listens to recorded interviews to evaluate interviewer performance. This process is BEST described as:

Accepted Answer

Fieldwork monitoring and interview auditing

Answer

Respondent validation

Answer

Inter-rater reliability testing

Answer

Response bias correction

Question 7

What is 'data documentation' in the context of research data quality, and why is it important?

Accepted Answer

Creating codebooks, variable labels, and metadata records that describe how data were collected, coded, and cleaned

Answer

Archiving raw survey responses in a secure server to comply with GDPR

Answer

Publishing summary statistics alongside the final research report

Answer

Recording interview completion times for speeder detection purposes