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VCE Applied Computing Data Analytics study materials
VCE Applied Computing Data Analytics study resources

VCE Applied Computing Data Analytics practice questions

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Question 1Data analytics and data management concepts, including data integrity, validation and the data life cycle

Which statement best describes the purpose of a data validation rule when entering records into a database?

  1. It guarantees that every stored value is factually accurate about the real world.
  2. It encrypts sensitive fields so unauthorised users cannot read them.
  3. It confirms that entered values meet predefined criteria before the record is accepted. Correct answer.
  4. It permanently archives old records so storage space is released.

Explanation

Validation checks that data falls within acceptable rules such as a range, a format or a list of permitted values at the point of entry. It improves the reliability of stored data by rejecting entries that break those rules, but it cannot confirm that a value truly reflects reality.

Question 2Data analytics and data management concepts, including data integrity, validation and the data life cycle

A school collects students' test scores. Which of the following is an example of a data integrity problem rather than a validation problem?

  1. A score field is left blank when the form is submitted.
  2. A score of 105 is entered for a test marked out of 100.
  3. A letter is typed into a field that only accepts digits.
  4. The same student's name is recorded as 'J. Nguyen' in one table and 'Jane Nguyen' in another. Correct answer.

Explanation

Data integrity concerns consistency and correctness of data across the whole system, including matching records between tables. Inconsistent name formats for the same student create conflicting records that validation rules at entry would not detect.

Question 3Data analytics and data management concepts, including data integrity, validation and the data life cycle

Which validation technique would best ensure that a date of birth entered in a form is a real calendar date rather than 31 February?

  1. A presence check
  2. A consistency check
  3. A range check
  4. A format check Correct answer.

Explanation

A format check confirms the entry matches an expected pattern, and a well-designed date format check rejects impossible day and month combinations. Presence, range and consistency checks do not test whether the day exists in the month.

Question 4Data analytics and data management concepts, including data integrity, validation and the data life cycle

A retail database stores customer postcodes in a field set to exactly four characters. Which data type issue is most likely to arise?

  1. Postcodes will automatically be validated against a national address list.
  2. The field will encrypt postcodes so they cannot be sorted.
  3. Postcodes with leading zeros may be stored incorrectly if a numeric type is chosen. Correct answer.
  4. The field will reject all alphabetic characters regardless of type.

Explanation

Choosing a numeric type strips leading zeros, so a postcode such as 0800 becomes 800 and no longer matches the real code. Storing postcodes as text preserves the exact characters and avoids this loss of information.

Question 5Data analytics and data management concepts, including data integrity, validation and the data life cycle

Which stage of the data life cycle involves removing obsolete records and deciding whether they should be archived or destroyed?

  1. Analysis
  2. Collection
  3. Disposal Correct answer.
  4. Storage

Explanation

The disposal stage covers the end of a record's useful life, where an organisation decides to archive for legal reasons or securely destroy data. Collection, storage and analysis all occur earlier in the cycle.

Question 6Data analytics and data management concepts, including data integrity, validation and the data life cycle

An organisation keeps backup copies of its database on a separate server in another building. Which aspect of data management does this primarily support?

  1. Data security through encryption
  2. Data validation through range checks
  3. Data redundancy for faster querying
  4. Data availability through disaster recovery Correct answer.

Explanation

Geographically separate backups allow an organisation to restore data and continue operating if the primary site fails. This supports availability and disaster recovery rather than validation or query speed.

Question 7Data analytics and data management concepts, including data integrity, validation and the data life cycle

A form asks users to select a payment method from a drop-down list showing only 'Card', 'Bank transfer' and 'Cash'. Which validation method is being applied?

  1. A check digit
  2. A range check
  3. A list or lookup check Correct answer.
  4. A length check

Explanation

A list check restricts entry to a predefined set of permitted values, which is exactly what a drop-down menu enforces. Range checks test numeric limits, length checks count characters, and check digits verify codes.

Question 8Data analytics and data management concepts, including data integrity, validation and the data life cycle

Why is a check digit useful when entering a long membership number by hand?

  1. It confirms the member's identity against a photo.
  2. It detects common typing errors such as a single wrong or swapped digit. Correct answer.
  3. It encrypts the membership number so it cannot be read.
  4. It stores the number in a shorter format to save space.

Explanation

A check digit is calculated from the other digits and appended to the code, so a mistyped or transposed digit usually causes the recalculation to fail. This makes it effective at catching human transcription errors.

Question 9Data analytics and data management concepts, including data integrity, validation and the data life cycle

A dataset of survey responses contains a field where 40 percent of entries are blank. Which data quality dimension is most directly affected?

  1. Timeliness
  2. Uniqueness
  3. Completeness Correct answer.
  4. Accuracy

Explanation

Completeness measures how much of the required data is present, so a large proportion of blanks reduces completeness. Accuracy concerns whether values are correct, timeliness concerns currency, and uniqueness concerns duplication.

Question 10Data analytics and data management concepts, including data integrity, validation and the data life cycle

A hospital must keep patient records for seven years after the last visit. Which data management consideration does this requirement illustrate?

  1. The need to normalise the database to third normal form
  2. A legal retention obligation affecting the disposal stage Correct answer.
  3. The benefit of using a check digit on patient identifiers
  4. The requirement for real-time data visualisation

Explanation

Retention rules set by law or policy determine how long records must be kept before disposal, which directly shapes the disposal stage of the life cycle. Normalisation, check digits and visualisation address different concerns.

Question 11Data analytics and data management concepts, including data integrity, validation and the data life cycle

Two tables in a relational database are linked so that every order record must refer to an existing customer. Which concept ensures this?

  1. A primary key
  2. An index
  3. A foreign key Correct answer.
  4. A view

Explanation

A foreign key in the order table stores the customer's primary key and enforces referential integrity, so an order cannot point to a customer that does not exist. A primary key uniquely identifies rows in its own table only.

Question 12Data analytics and data management concepts, including data integrity, validation and the data life cycle

Which situation best demonstrates a breach of referential integrity?

  1. A product price is stored with two decimal places.
  2. An order record refers to a customer ID that has been deleted from the customer table. Correct answer.
  3. A student's name is stored in uppercase letters.
  4. A customer record has a blank phone number.

Explanation

Referential integrity requires that a foreign key value matches an existing primary key in the related table. Once the referenced customer is removed, the order's foreign key points to nothing, which is an orphan record.

Question 13Data analytics and data management concepts, including data integrity, validation and the data life cycle

A company merges two customer databases and finds the same person listed twice with different addresses. What is this problem called?

  1. Data encryption
  2. Data duplication Correct answer.
  3. Data validation
  4. Data redundancy

Explanation

Data duplication occurs when the same real-world entity appears more than once, often after merging sources. Redundancy is the unnecessary storage of the same data in multiple places within a well-defined design, which is related but distinct.

Question 14Data analytics and data management concepts, including data integrity, validation and the data life cycle

Which practice most directly reduces the risk of data loss from accidental deletion by a staff member?

  1. Setting a validation rule on the deletion form
  2. Restricting the database to a single user
  3. Applying regular automated backups with version history Correct answer.
  4. Increasing the size of the hard drive

Explanation

Regular automated backups with version history allow a deleted record to be restored from an earlier copy. Larger storage, validation rules and single-user access do not provide a way to recover deleted data.

Question 15Data analytics and data management concepts, including data integrity, validation and the data life cycle

A weather station records temperature every minute, producing millions of rows. Which data management strategy best keeps the database efficient while preserving long-term trends?

  1. Storing every reading in a spreadsheet on a staff laptop
  2. Aggregating older readings into hourly averages and archiving the raw data Correct answer.
  3. Deleting all readings older than one day
  4. Removing the timestamp field to reduce row size

Explanation

Aggregating older readings into hourly averages reduces the volume of actively queried data while preserving trends, and archiving raw data retains detail for later needs. Deleting data or removing the timestamp destroys analytical value.

Question 16Data analytics and data management concepts, including data integrity, validation and the data life cycle

Which of the following best explains why a validation rule cannot guarantee data accuracy?

  1. Validation rules require a check digit in every field.
  2. Validation rules are only run after data has been archived.
  3. Validation rules test whether data meets a rule, not whether the content is true. Correct answer.
  4. Validation rules are only applied to numeric fields.

Explanation

Validation confirms that an entry satisfies a specified rule, such as being within a range or matching a format. A value can satisfy every rule and still be factually wrong, so accuracy depends on the source and the person entering data.

Question 17Data analytics and data management concepts, including data integrity, validation and the data life cycle

A data analyst needs to explain why a report showing average salaries is misleading. Which data quality issue is most likely responsible?

  1. A small number of extremely high salaries strongly skew the mean. Correct answer.
  2. The salary field uses an incorrect data type for currency.
  3. The salary records were entered in alphabetical order.
  4. The report was generated using a validated form.

Explanation

A mean is sensitive to outliers, so a few very large values can pull the average well above what most employees earn. The median is more robust in this situation, and this is a distribution issue rather than a data type or validation issue.

Question 18Data analytics and data management concepts, including data integrity, validation and the data life cycle

Which stage of the data life cycle immediately follows data collection and involves checking, correcting and standardising the data?

  1. Disposal
  2. Visualisation
  3. Analysis
  4. Cleaning Correct answer.

Explanation

After collection, data is cleaned to remove errors, fill gaps and standardise formats before it is stored or analysed. Analysis and visualisation come later, and disposal comes at the end of the cycle.

Question 19Data analytics and data management concepts, including data integrity, validation and the data life cycle

A field stores a person's age. A validation rule limits entries to whole numbers between 0 and 120. Which two validation techniques are being combined?

  1. A presence check and a length check
  2. A list check and a consistency check
  3. A type check and a range check Correct answer.
  4. A format check and a check digit

Explanation

Requiring whole numbers is a type check, and limiting values between 0 and 120 is a range check. Together they reject both non-numeric entries and implausible ages.

Question 20Data analytics and data management concepts, including data integrity, validation and the data life cycle

An online store requires that each product record have a unique product code. Which database feature enforces this requirement?

  1. A default value
  2. A primary key Correct answer.
  3. A memo field
  4. A foreign key

Explanation

A primary key is a field or combination of fields whose value uniquely identifies each row, so duplicates are rejected. Foreign keys link to other tables, defaults fill empty values, and memo fields store long text.

Question 21Data analytics and data management concepts, including data integrity, validation and the data life cycle

A researcher collects survey data and later discovers that several respondents misunderstood a question. Which data quality dimension is affected and what is the best response?

  1. Timeliness, so the report should be published immediately.
  2. Accuracy, so affected responses should be excluded or the question reworded and the survey rerun. Correct answer.
  3. Uniqueness, so duplicate respondents should be merged.
  4. Completeness, so blank fields should be filled with averages.

Explanation

Misunderstood questions produce answers that do not reflect what the respondents actually think, which is an accuracy problem. The sound response is to exclude affected responses or to reword the question and collect data again.

Question 22Data analytics and data management concepts, including data integrity, validation and the data life cycle

Which statement about the difference between data and information is correct?

  1. Data is processed output, while information is raw facts.
  2. Information is always numeric, while data is always text.
  3. Data and information are identical terms used interchangeably.
  4. Data consists of raw facts, while information is data processed into a meaningful context. Correct answer.

Explanation

Data is a collection of raw facts and figures that on its own lacks meaning. When data is organised and interpreted in context, it becomes information that supports decisions.

Question 23Data analytics and data management concepts, including data integrity, validation and the data life cycle

A company stores customer data in a cloud service hosted overseas. Which data management issue is most directly raised?

  1. Whether storage of personal data complies with privacy and data protection laws Correct answer.
  2. Whether the database uses a check digit on invoices
  3. Whether the data type of the customer ID is correct
  4. Whether the customer names are stored in uppercase

Explanation

Moving personal data across national borders can create obligations under privacy and data protection legislation about where and how data may be held. Data types, letter case and check digits are technical details unrelated to this legal concern.

Question 24Data analytics and data management concepts, including data integrity, validation and the data life cycle

A spreadsheet contains a column of phone numbers, and some entries begin with a zero that disappears. What is the most appropriate fix?

  1. Apply a range check to the column.
  2. Sort the column in descending order.
  3. Set the column data type to text so leading zeros are preserved. Correct answer.
  4. Add a check digit to each number.

Explanation

Phone numbers are identifiers rather than quantities, so storing them as text preserves leading zeros and other characters. Changing the type addresses the cause, while range checks, check digits and sorting do not restore lost characters.

Question 25Data analytics and data management concepts, including data integrity, validation and the data life cycle

Which sequence correctly orders the stages of the data life cycle from beginning to end?

  1. Collection, storage, analysis, disposal Correct answer.
  2. Analysis, collection, disposal, storage
  3. Collection, analysis, storage, disposal
  4. Storage, collection, analysis, disposal

Explanation

Data is first collected, then stored so it can be accessed, analysed to produce information, and finally disposed of or archived when no longer needed. Storage must precede analysis because analysis operates on stored data.

Question 26Data analytics and data management concepts, including data integrity, validation and the data life cycle

A school stores each student's date of birth in a field formatted as DD/MM/YYYY. Which data type and format decision best supports data integrity for this field?

  1. Store the value as text so that slashes and leading zeros are preserved exactly as typed
  2. Store the value in whichever format each staff member finds easiest to read
  3. Store the value as a date type with a single agreed format across all records Correct answer.
  4. Store the value as a number by removing the slashes between day, month and year

Explanation

A dedicated date type lets the database reject impossible values and compare or sort dates correctly, while one agreed format keeps every record consistent. Free text or stripped numbers lose meaning and allow inconsistent ordering, which undermines integrity.

Question 27Data analytics and data management concepts, including data integrity, validation and the data life cycle

A supermarket loyalty system records every item scanned by every customer. Which characteristic of big data does the sheer number of individual transaction records most directly describe?

  1. Volume Correct answer.
  2. Veracity
  3. Variety
  4. Velocity

Explanation

Volume refers to the scale or quantity of data being generated and stored, which is what a huge count of individual transaction records measures. Variety concerns different data types, velocity concerns speed of arrival, and veracity concerns reliability.

Question 28Data analytics and data management concepts, including data integrity, validation and the data life cycle

A council wants to publish open data about parking fines but must remove names, addresses and licence plates first. Which data management practice is being applied?

  1. Data aggregation to combine many records into totals
  2. Data archiving to reduce storage costs over time
  3. Data validation to check entries against a set of rules
  4. Data de-identification to protect personal privacy before release Correct answer.

Explanation

De-identification removes or masks personally identifying fields so individuals cannot be recognised in a released dataset. Validation, archiving and aggregation serve different purposes and do not by themselves remove identifying details.

Question 29Data analytics and data management concepts, including data integrity, validation and the data life cycle

A data entry clerk types a postcode into a field that accepts exactly four digits. The clerk enters 3000, which is a valid format but belongs to a customer in a different state. What does this example demonstrate?

  1. Referential integrity has been breached by the postcode entry
  2. Validation prevents errors, so the postcode must be accurate
  3. The field should store postcodes as numbers to improve accuracy
  4. A validation rule can confirm format while accuracy still depends on the person entering data Correct answer.

Explanation

Validation only tests whether data meets specified rules such as length and character type. A value can pass every rule yet still be wrong, because accuracy depends on the source and the person entering it.

Question 30Data analytics and data management concepts, including data integrity, validation and the data life cycle

A retail company records the time each online order is placed and later uses these timestamps to identify the busiest hour of the day. Which statement best describes what the timestamps become at this stage?

  1. They become validation rules because they check order entries
  2. They become information because they have been processed into a meaningful pattern Correct answer.
  3. They remain raw data because no new values have been created
  4. They become metadata because they describe other records

Explanation

Data becomes information once it is processed and interpreted so it has meaning for a decision, such as identifying peak trading hours. The timestamps are unchanged, but their organisation produces insight.

Question 31Data analytics and data management concepts, including data integrity, validation and the data life cycle

A hospital's patient system must ensure that every appointment record links to a patient who actually exists in the patient table. Which database mechanism enforces this requirement?

  1. A check constraint limiting appointment times to business hours
  2. An index built on the patient surname column
  3. A foreign key in the appointment table referencing the patient table Correct answer.
  4. A primary key on the appointment table

Explanation

A foreign key in the child table must match an existing primary key value in the parent table, which enforces referential integrity. Primary keys, check constraints and indexes address uniqueness, value ranges and search speed rather than cross-table links.

Question 32Data analytics and data management concepts, including data integrity, validation and the data life cycle

A dataset lists a customer's state as 'VIC' in one record and 'Victoria' in another, even though both refer to the same customer. Which action during the data life cycle stage of data cleaning best resolves this?

  1. Standardise the values to a single agreed code for the state field Correct answer.
  2. Add a validation rule that accepts any text in the state field
  3. Merge the two values into a new field containing both spellings
  4. Delete both records so the inconsistency is removed

Explanation

Data cleaning includes standardising inconsistent representations so that identical real-world values are stored the same way. Deleting records loses valid data, and loosening validation or combining spellings preserves the inconsistency.

Question 33Data analytics and data management concepts, including data integrity, validation and the data life cycle

A payroll spreadsheet stores hourly rates in a column formatted as currency. A new staff member pastes rates copied from a web page and some cells show text instead of numbers. Which consequence is most likely when totals are calculated?

  1. The spreadsheet will automatically convert all text entries to numbers before totalling
  2. The totals will be correct because the displayed values look like rates
  3. Affected rows may be excluded from totals, producing understated pay figures Correct answer.
  4. The currency format will be removed from every cell in the column

Explanation

When numeric-looking values are stored as text, spreadsheet functions often ignore them, so totals cover fewer rows than intended. The displayed appearance does not guarantee that a value is stored as a number.

Question 34Data analytics and data management concepts, including data integrity, validation and the data life cycle

An analyst must decide whether to keep a field recording the date each customer record was last updated. Which reason best justifies retaining this metadata?

  1. It increases the file size, which discourages unauthorised copying
  2. It automatically corrects outdated values in other fields
  3. It allows users to judge how current each record is and to audit changes Correct answer.
  4. It replaces the need for backups of the customer database

Explanation

Metadata such as a last-updated timestamp helps users assess currency and supports auditing of changes over time. It does not correct values, replace backups or provide security through file size.

Question 35Data analytics and data management concepts, including data integrity, validation and the data life cycle

A fitness app collects step counts from users' phones and sends the data to a server every few seconds. Which data management approach best handles this continuous high-speed stream?

  1. Discard all readings and rely on users' memory of their activity
  2. Ask users to upload their step counts manually once each month
  3. Store every raw reading indefinitely in a single spreadsheet for later review
  4. Process the stream in real time and store only summarised or required values Correct answer.

Explanation

Streaming data arrives too quickly and in too much detail for manual handling, so real-time processing with selective storage keeps the system manageable. Manual uploads and discarding data defeat the purpose of automatic collection.

Question 36Data analytics and data management concepts, including data integrity, validation and the data life cycle

A researcher records participants' answers to a question about weekly exercise. Many participants select '0 hours' because the option list did not include part-hours. Which data quality dimension is most affected?

  1. Uniqueness
  2. Consistency
  3. Accuracy Correct answer.
  4. Timeliness

Explanation

Accuracy concerns whether values correctly represent reality. When the collection instrument forces respondents into a wrong answer, the recorded values do not reflect actual behaviour even though they are complete and consistently formatted.

Question 37Data analytics and data management concepts, including data integrity, validation and the data life cycle

A national retailer must retain sales records for tax purposes for five years and then dispose of them securely. Which element of a data management policy does this requirement most directly address?

  1. A backup rotation plan for the sales database
  2. A normalisation process for the sales tables
  3. A validation rule applied to sales totals
  4. A data retention schedule with defined disposal procedures Correct answer.

Explanation

A retention schedule states how long each category of data must be kept and how it should be destroyed once that period ends. Validation, backup rotation and normalisation address data correctness, recoverability and structure rather than retention and disposal.

Question 38Data analytics and data management concepts, including data integrity, validation and the data life cycle

Two analysts query the same sales database. One reports total revenue of 1.2 million dollars and the other reports 1.5 million dollars for the same period. Investigation shows one query included cancelled orders. Which explanation best fits?

  1. The records were captured with an invalid date format
  2. The data has a volume problem that prevents accurate aggregation
  3. The analysts applied different definitions or filters, so their results are not comparable Correct answer.
  4. The database has a referential integrity failure between orders and customers

Explanation

Different results from the same source usually arise from differing definitions, filters or inclusion rules rather than from storage faults. Agreeing on what counts as revenue, such as excluding cancelled orders, is needed before figures can be compared.

Question 39Data analytics and data management concepts, including data integrity, validation and the data life cycle

A company plans to combine customer records from three acquired businesses into one system. Each source uses a different customer identifier, and some customers appear in more than one source. Which approach best protects data integrity during this merge?

  1. Define a master key and matching rules, then de-duplicate and reconcile records before loading Correct answer.
  2. Convert all identifiers to text so that differences between sources no longer matter
  3. Keep the three systems separate indefinitely so that no records need to be reconciled
  4. Load all records directly into the target table and remove duplicates only if complaints arise

Explanation

Merging sources requires agreed matching rules and a single master identifier so duplicates can be identified and conflicting values reconciled before loading. Converting identifiers to text or delaying de-duplication preserves conflicts and creates unreliable records.

Question 40Data analytics and data management concepts, including data integrity, validation and the data life cycle

An analyst notices that a sensor occasionally reports a temperature of minus 500 degrees Celsius, far outside the sensor's physical range. Which combination of actions best protects the dataset?

  1. Change the sensor's recorded units so that the extreme values become plausible
  2. Delete the entire day's readings whenever an extreme value appears
  3. Accept all readings and average them so that extreme values have little influence
  4. Apply a range check at capture and flag or quarantine readings outside plausible limits for review Correct answer.

Explanation

A range check at the point of capture stops implausible values entering the dataset, and flagging or quarantining them allows investigation without discarding valid data. Averaging hides errors, deleting whole days loses good readings, and altering units misrepresents the measurements.

Question 41Data collection, sources and acquisition methods, including primary and secondary data

A student measures the temperature of a school courtyard every hour using a digital thermometer and records the readings. What type of data is the student collecting?

  1. Primary data, because a thermometer is an instrument
  2. Secondary data, because temperatures are widely published
  3. Primary data, because the student collected it directly for this purpose Correct answer.
  4. Secondary data, because the readings are numbers

Explanation

Primary data is gathered firsthand by the person conducting the investigation, specifically for the current purpose. The student personally operated the thermometer and logged each reading, so the measurements originate with them. Data is not classified as secondary merely because an instrument was used or because the values are numeric.

Question 42Data collection, sources and acquisition methods, including primary and secondary data

A netball coach wants feedback on a new training drill. Which method would most reliably produce primary data about players' opinions of the drill?

  1. Watching a documentary about elite netball
  2. Reviewing last season's match scores
  3. Surveying the players after they complete the drill Correct answer.
  4. Reading a sports science textbook chapter on drills

Explanation

Primary data must be collected firsthand from the relevant source for the specific purpose. Surveying the players who actually performed the drill captures their direct opinions. Textbooks, past scores and documentaries are pre-existing secondary sources that do not record these players' responses to the new drill.

Question 43Data collection, sources and acquisition methods, including primary and secondary data

Which of the following is an example of secondary data?

  1. Responses a researcher records during a face-to-face interview
  2. Measurements a technician takes with a calibrated scale
  3. Census population figures downloaded from a government statistics website Correct answer.
  4. Sensor readings logged automatically by a newly installed weather station

Explanation

Secondary data is data that was collected by someone else for another purpose and is then reused. Census figures published by a government agency were gathered for official purposes and are now being reused. Interviews, new sensor logs and fresh scale measurements are all collected firsthand by the researcher.

Question 44Data collection, sources and acquisition methods, including primary and secondary data

A school canteen wants to know which lunch items students prefer. Which data source would best answer this question?

  1. A survey of students at the canteen during lunchtime Correct answer.
  2. The canteen's electricity usage records
  3. The manufacturer's packaging of each food item
  4. A national report on dietary guidelines

Explanation

The research question concerns the preferences of this school's students, so data must come from those students. A survey conducted at the canteen directly captures their stated preferences. Packaging, national guidelines and electricity records do not record what these students like to eat.

Question 45Data collection, sources and acquisition methods, including primary and secondary data

A researcher needs information about historical rainfall patterns in a region over the past 50 years. Which source is most appropriate?

  1. A long-term archive of weather bureau records Correct answer.
  2. A single rain gauge reading taken today
  3. A social media poll about favourite seasons
  4. An interview with one local farmer

Explanation

The question requires long-term historical measurements across many years. A maintained archive of official weather bureau records provides consistent, recorded rainfall data spanning decades. One current reading, a poll about preferences, or a single interview cannot supply systematic 50-year measurements.

Question 46Data collection, sources and acquisition methods, including primary and secondary data

Which of the following best describes quantitative data?

  1. Data expressed as numbers that can be measured or counted Correct answer.
  2. Data collected only through interviews
  3. Data recorded as descriptive labels or categories
  4. Data that has been stored in a spreadsheet

Explanation

Quantitative data is numerical and represents quantities that can be counted or measured, such as heights or test scores. Qualitative data, by contrast, consists of descriptive categories or labels. The collection method or storage format does not determine whether data is quantitative.

Question 47Data collection, sources and acquisition methods, including primary and secondary data

A gym records each member's age, membership type and number of visits per month. Which of these fields holds categorical (nominal) data?

  1. Total hours exercised
  2. Number of visits per month
  3. Age in years
  4. Membership type Correct answer.

Explanation

Categorical data places items into named groups without a meaningful numerical order. Membership type (such as standard or premium) is a label describing a category. Age, visit counts and hours exercised are all measured numerically, making them quantitative.

Question 48Data collection, sources and acquisition methods, including primary and secondary data

What is the main advantage of automated data capture, such as sensors logging readings continuously, compared with manual entry?

  1. It reduces transcription mistakes and can record data continuously Correct answer.
  2. It removes the need to clean or validate the data
  3. It always produces data that is easier to interpret
  4. It guarantees the data will be free of all errors

Explanation

Automated capture records readings directly without a person retyping values, which lowers transcription error and allows continuous logging. It does not eliminate every error, since faulty sensors or poor calibration can still produce bad values. Automated data still requires cleaning, validation and interpretation.

Question 49Data collection, sources and acquisition methods, including primary and secondary data

A market research team wants detailed reasons behind customers' choices. Which acquisition method is best suited to gathering this kind of rich qualitative data?

  1. A structured online survey with rating scales
  2. Automated sales transaction logs
  3. Semi-structured interviews with a sample of customers Correct answer.
  4. A spreadsheet of website click counts

Explanation

Semi-structured interviews allow open-ended responses and follow-up questions, which reveal the reasoning behind choices. Rating scales, transaction logs and click counts capture structured or numeric data but cannot explain motivation. Rich qualitative insight therefore requires methods that let participants explain their views.

Question 50Data collection, sources and acquisition methods, including primary and secondary data

A researcher plans to survey students about part-time work. Which sampling approach gives every student in the school an equal chance of being selected?

  1. Surveying only students in the researcher's own class
  2. Asking the first 30 students who arrive at school
  3. Selecting 30 students using a random number generator from the full enrolment list Correct answer.
  4. Inviting students to volunteer through an announcement

Explanation

A simple random sample gives every member of the population an equal probability of selection. Drawing names from the complete enrolment list with a random number generator achieves this. Convenience, class-based and volunteer samples all introduce selection bias because not everyone has an equal chance.