Preparing and Processing Primary Data Practice Quiz (24 questions)

All questions and answers

1. What is the primary purpose of preparing raw questionnaire responses before analysis?

To interpret the findings before the responses have been checked for accuracy
To convert them into useful information through a sequence of validation, editing, coding, capture, and cleaning steps
To select a representative sample by removing responses from less accessible locations
To replace questionnaire responses with published statistics before researchers inspect them

To convert them into useful information through a sequence of validation, editing, coding, capture, and cleaning steps

Explanation

Data preparation transforms raw responses into useful information through steps that include validation, editing, coding, capturing, importing, verifying, cleaning, labeling, and storing. Selecting a sample is a different research task, not the purpose of preparing collected responses.

2. In a large research project, when can data preparation and processing begin?

As fieldwork data becomes available, without waiting for all fieldwork to finish
Once the final analysis has identified which responses are most useful
After the research team has published its preliminary conclusions
After every interviewer has completed the entire fieldwork assignment

As fieldwork data becomes available, without waiting for all fieldwork to finish

Explanation

For a large project, processing can proceed as fieldwork data becomes available rather than waiting for fieldwork to end. Waiting until every interviewer finishes may delay the work, but it is not required.

3. What decision is central to validating gathered survey data?

Whether responses should be grouped into categories before their accuracy is checked
Whether the survey findings should be presented in tables or narrative form
Whether interviewers should change the wording of questions for future respondents
Whether each questionnaire is valid and accurate enough to include in analysis or should be discarded

Whether each questionnaire is valid and accurate enough to include in analysis or should be discarded

Explanation

Validation assesses whether gathered data is valid and accurate and determines whether each questionnaire should be analyzed or discarded. Grouping responses into categories is coding, a separate preparation task.

4. An interviewer conducts an interview inconsistently with the required procedure; what does the inclusion rule imply for that questionnaire?

It does not qualify for inclusion in the analysis because the interview was not conducted correctly and consistently
It qualifies for inclusion if the interviewer’s call sheet lists the respondent
It qualifies for inclusion after the answers are recoded to match other respondents
It qualifies for inclusion if the respondent completed most of the questionnaire

It does not qualify for inclusion in the analysis because the interview was not conducted correctly and consistently

Explanation

A questionnaire qualifies for analysis only when its interview was conducted correctly and consistently. A completed form or a call-sheet entry does not by itself establish that the interview met this requirement.

5. Which procedure is part of validating survey data collection?

Telephone every respondent to obtain answers again, then replace the original questionnaires
Compare final survey findings with published statistics, then remove results that differ
Review only completed questionnaires for spelling, then accept the interviewer’s sampling decisions
Telephone a portion of listed respondents to confirm interviews, then check instructions and sampling records

Telephone a portion of listed respondents to confirm interviews, then check instructions and sampling records

Explanation

Validation guidelines include telephoning 10–20% of listed respondents to confirm they were interviewed, checking the questionnaire and instructions against sample requirements, and reviewing interviewers’ call sheets. Recontacting every respondent to repeat the interview is not the stated validation procedure.

6. What does editing a questionnaire or observation form involve?

Critically checking it for correctness and completeness, making needed adjustments, and discarding worthless forms
Summarizing completed forms into findings and resolving differences between groups
Deciding whether the interview followed sampling procedures before checking the form
Assigning numerical codes to answers and storing the coded data for later analysis

Critically checking it for correctness and completeness, making needed adjustments, and discarding worthless forms

Explanation

Editing is the critical review of questionnaires and observation forms for correctness and completeness, with adjustments where needed and worthless forms discarded. Assigning numerical codes is coding rather than editing.

7. How does central office editing differ from field editing?

Field editing is a more thorough review at the head office, while central office editing is a preliminary check in the field
Field editing is preliminary, while central office editing is a more thorough scrutiny and adjustment, usually at the research body’s head office
Field editing checks sampling plans, while central office editing decides whether fieldwork can begin
Field editing focuses on coding responses, while central office editing focuses on selecting respondents

Field editing is preliminary, while central office editing is a more thorough scrutiny and adjustment, usually at the research body’s head office

Explanation

Field editing is preliminary work by a field organiser or supervisor, whereas central office editing involves more thorough scrutiny and adjustment, usually at the research body’s head office. The reverse description incorrectly assigns the more thorough review to field editing.

8. During editing, a reviewer finds unclear handwriting and contradictory answers; which criteria are directly relevant to these problems?

Interview duration and respondent selection, and the choice of coding scheme
Legibility and clarity of handwriting, and consistency of responses
Sample size and respondent availability, and the order of data capture
Questionnaire storage and data labeling, and the timing of fieldwork

Legibility and clarity of handwriting, and consistency of responses

Explanation

Editing criteria include handwriting legibility and clarity as well as inconsistencies in responses. Sample size and respondent availability concern different aspects of survey design and fieldwork.

9. What does coding questionnaire responses accomplish?

It selects a sample of respondents and assigns each person a unique identification number.
It groups responses into classes and assigns numbers so the data can be represented and tabulated by computer.
It checks whether respondents completed every item and removes incomplete questionnaires from analysis.
It converts numerical results into written summaries for interpretation by a research team.

It groups responses into classes and assigns numbers so the data can be represented and tabulated by computer.

Explanation

Coding classifies responses and assigns code numbers so raw data can be represented in computer-readable form and tabulated. Checking questionnaire completeness is a separate data-quality task, not the purpose of coding.

10. A research team decides on response categories while designing its questionnaire, then codes responses after collection is finished. Which description correctly distinguishes these two stages?

Both stages are post-coding because both involve assigning codes to responses.
The categories are post-coded, while coding after collection is pre-coding.
Both stages are pre-coding because the questionnaire was designed before collection.
The categories are pre-coded, while coding after collection is post-coding.

The categories are pre-coded, while coding after collection is post-coding.

Explanation

Pre-coding establishes response categories during questionnaire design, whereas post-coding occurs after data collection. The tempting reversal confuses when the categories are set with when collected responses are coded.

11. After identifying categories for every possible response and assigning each category a code number, what should a researcher compile to document the variables and their coding?

A data-capture file containing the coded responses for analysis.
A questionnaire schedule specifying the order of survey questions.
A codebook describing the variables and their coding.
A sampling frame listing eligible respondents and their contact details.

A codebook describing the variables and their coding.

Explanation

The coding process includes compiling a codebook that describes variables and their coding. A data-capture file holds the entered data, but it does not serve as the documentation of coding specifications.

12. What is data capture in questionnaire-based research?

Selecting an appropriate source from which to obtain survey information.
Classifying questionnaire responses into categories and assigning each category a number.
Entering questionnaire codes into an electronic file that a computer can read for analysis.
Checking electronic records for errors after statistical analysis is complete.

Entering questionnaire codes into an electronic file that a computer can read for analysis.

Explanation

Data capture means entering questionnaire codes into an electronic data file readable by a computer for analysis. Classifying responses and assigning numbers describes coding, a related but distinct activity.

13. Which set consists entirely of recognized sources or methods for obtaining survey data?

Experimental designs, financial reports, audio recordings, field diaries, and interview transcripts.
Web questionnaires, laboratory notes, published articles, expert panels, and postal tracking.
Paper questionnaires, web questionnaires, experimental designs, databases, and optical scanning.
Paper questionnaires, telephone interviews, focus groups, census lists, and manual transcription.

Paper questionnaires, web questionnaires, experimental designs, databases, and optical scanning.

Explanation

Recognized sources include paper and web questionnaires, experimental designs, databases, and optical scanning. The other sets introduce methods or materials that are not identified as data sources in this list.

14. Why can web questionnaires be administered effectively and inexpensively, and how are their responses handled?

They use mailed forms to reach respondents, and completed answers are entered into a database by staff.
They require no intermediary interviewer, and responses populate a database for electronic download after the survey.
They eliminate the need to store responses, which are summarized automatically during questionnaire completion.
They rely on interviewers to enter responses, which are stored in paper files for manual tabulation.

They require no intermediary interviewer, and responses populate a database for electronic download after the survey.

Explanation

Web questionnaires can be efficient and inexpensive because no intermediary interviewer is involved; responses populate a database and can be downloaded electronically after the survey. The interviewer-based approach adds an intermediary rather than explaining the web questionnaire's cost advantage.

15. A researcher is planning a web questionnaire study. Which set of safeguards addresses key concerns with this mode of data collection?

Check that respondents are unique, consider internet-access bias, consider a paper option, and set a submission cut-off date.
Recruit respondents through a database, correct internet-access bias through weighting, require interviewer follow-up, and extend submissions as needed.
Verify that each response is complete, restrict participation to one device, remove paper alternatives, and leave submissions open-ended.
Confirm respondent identities, use paper questionnaires for every participant, omit a submission deadline, and accept duplicate entries.

Check that respondents are unique, consider internet-access bias, consider a paper option, and set a submission cut-off date.

Explanation

Web questionnaire studies should address duplicate respondents and internet-access bias, consider combining web and paper questionnaires, and set a submission cut-off date. The alternative that removes paper options and leaves submissions open-ended omits two of these recommended safeguards.

16. Before examining survey results statistically, what must be done with electronic data collected in a spreadsheet or web questionnaire?

Convert every response into a chart
Remove records that have not been analyzed
Assign readable labels to every coded category
Import it into an analysis package

Import it into an analysis package

Explanation

Electronic data from sources such as spreadsheets and web questionnaires must be imported into an analysis package before analysis. Labeling categories is a preparation task, but it does not bring the data into the package.

17. Which software is one of the statistical analysis packages used for analyzing prepared data?

Minitab
Microsoft Word
Google Slides
Adobe Photoshop

Minitab

Explanation

Minitab is among the statistical analysis packages used for data analysis. The other programs listed are not statistical analysis packages in the provided set.

18. What is the primary purpose of data cleaning?

To detect and correct entry errors by checking internal consistency
To replace unusual responses with average values
To make every variable use the same measurement scale
To convert questionnaire responses into readable labels

To detect and correct entry errors by checking internal consistency

Explanation

Data cleaning checks and corrects data-entry errors by testing internal consistency, including possible and impossible codes. Assigning readable labels is a separate preparation step, not the primary purpose of cleaning.

19. A questionnaire uses a response scale from 1 to 7, but one record contains a value of 9. What type of data-entry error does this illustrate?

An impossible value outside the permitted scale
A duplicate record imported from another dataset
A record entered in the wrong sequence
A transformed value caused by reversing two digits

An impossible value outside the permitted scale

Explanation

A value of 9 is impossible for a scale coded from 1 to 7, so it is an out-of-range entry error. Reversing the digits in a valid value, such as recording 25 instead of 52, is a different kind of error.

20. Which check is appropriate for identifying contradictory responses across variables?

Inspect maximum and minimum values for each record
Create frequency cross-tabulations of the responses
Calculate a mean for each nominal variable
Count the number of labels assigned to each variable

Create frequency cross-tabulations of the responses

Explanation

Frequency cross-tabulations can reveal inconsistent or contradictory responses across variables. Means and ranges are useful checks for continuous variables, not the primary check for contradictions between responses.

21. A numeric field is missing from a record. How should it be treated in statistical calculations?

As an incomplete value that is excluded from calculations
As a zero that contributes to the calculated total
As a category that is grouped with the lowest response
As an invalid code that is automatically replaced by the mean

As an incomplete value that is excluded from calculations

Explanation

A missing value represents an incomplete field, not a zero, and statistical packages exclude missing values from calculations. Zero is a valid data value when it is actually recorded.

22. A dataset contains a variable named Q1, which records respondents’ provinces. What would assigning the variable label “Province” accomplish?

Give the questionnaire variable a readable name
Mark unanswered records as missing values
Describe the meaning of each numeric response code
Replace the stored responses with category names

Give the questionnaire variable a readable name

Explanation

A variable label is a readable name assigned to a questionnaire question represented as a variable, such as labeling Q1 as Province. Describing individual numeric codes is the role of value labels.

23. A province variable uses 1 for Western Province and 2 for Northern Cape. What does a value label do in this case?

Provides readable descriptions for the coded categories
Identifies which records have incomplete responses
Renames the questionnaire variable to identify its subject
Changes the numeric codes stored in the dataset

Provides readable descriptions for the coded categories

Explanation

Value labels provide readable descriptions for a variable’s coded categories, such as linking 1 and 2 to province names. Renaming the variable itself is done with a variable label.

24. A dataset has been entered, verified, cleaned, and labeled. What is the next appropriate step before analysis?

Store the prepared data in the package format in memory or on storage media
Re-enter the data to preserve its original questionnaire format
Begin analysis without retaining a prepared dataset
Remove the labels before saving the dataset

Store the prepared data in the package format in memory or on storage media

Explanation

Once data has been entered, verified, cleaned, and labeled, raw data preparation is complete and the prepared data can be stored until analysis. Removing labels is not required for storing the prepared dataset.

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What does primary data preparation turn raw questionnaire responses into?

Useful information.

Which steps prepare raw questionnaire responses before analysis?

Validation, editing, coding, capturing, importing, verifying and cleaning, labeling, and storing.

What is data validation in survey research?

The process of checking data validity and accuracy and deciding whether questionnaires should be included or discarded.

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