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Data Source Reliability Assessment

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Data Source Reliability Assessment

Data reliability investigation illustration

🔍 Part 1: Data Source Detective

Instructions: Read each data source below and rate its reliability on a scale of 1-5 (1 = Very Unreliable, 5 = Very Reliable). Circle your rating and identify if it's primary or secondary data.

1. Source A: A Statistics New Zealand report on household income published in 2024, based on surveys of 15,000 randomly selected households across New Zealand.

Reliability Rating: 1 - 2 - 3 - 4 - 5

This is _____________ data (primary/secondary)

2. Source B: A Facebook post claiming "90% of teenagers prefer TikTok over Instagram" with no supporting evidence or methodology provided.

Reliability Rating: 1 - 2 - 3 - 4 - 5

This is _____________ data (primary/secondary)

3. Source C: Temperature data you collected yourself using a digital thermometer every hour for one week in Auckland.

Reliability Rating: 1 - 2 - 3 - 4 - 5

This is _____________ data (primary/secondary)

4. Source D: A 2015 newspaper article about smartphone usage among young people, citing a study from 2012.

Reliability Rating: 1 - 2 - 3 - 4 - 5

This is _____________ data (primary/secondary)

5. Source E: Ministry of Education data on NCEA pass rates from 2023, published on the official government website with detailed methodology.

Reliability Rating: 1 - 2 - 3 - 4 - 5

This is _____________ data (primary/secondary)

✅ Part 2: Data Quality Checklist

6. Create a checklist for evaluating data quality. Check the boxes for the most important factors to consider when assessing if a data source is reliable:

Who collected the data and what are their qualifications?

When was the data collected (is it recent and relevant)?

How large was the sample size?

What methods were used to collect the data?

Has the data been peer-reviewed or verified?

Is the source biased or trying to sell something?

Can the results be replicated by others?

Is the data presented with proper context and limitations?

7. Explain why sample size matters when evaluating data reliability. Give an example:
8. You want to investigate "How many hours per week do Year 11 students spend on social media?" Describe one way you could collect PRIMARY data and one way you could find SECONDARY data for this investigation:

Primary data collection method:

Secondary data source:

🎯 Part 3: Extension Challenge

9. Advanced Task: Choose one of the unreliable sources from Part 1. Design an improved method to collect reliable data on the same topic. Include details about sample size, data collection method, and how you would avoid bias:
10. Critical Thinking: Why might two reliable sources give different results about the same topic? Explain with an example:

Success Criteria:

  • I can distinguish between primary and secondary data sources
  • I can evaluate the reliability of different data sources using appropriate criteria
  • I can identify factors that make data sources more or less trustworthy
  • I can design methods for collecting reliable data

ANSWER KEY

Part 1 - Data Source Detective:

1. Source A: Reliability 5, Secondary data (collected by Stats NZ, not by you)

2. Source B: Reliability 1, Secondary data (unreliable - no evidence or methodology)

3. Source C: Reliability 4-5, Primary data (you collected it yourself)

4. Source D: Reliability 2, Secondary data (outdated and second-hand information)

5. Source E: Reliability 5, Secondary data (official government data with methodology)

Part 2 - Data Quality Checklist:

6. All boxes should be checked - these are all important factors

7. Sample answers: Larger samples are more representative of the population and reduce the impact of outliers. Example: Surveying 5 students vs 500 students about study habits.

8. Primary: Survey Year 11 students directly; Secondary: Use existing research studies or government reports on teenage social media use

Part 3 - Extension:

9. Sample answer for Source B: Survey a random sample of at least 200 teenagers using standardised questions, ensure anonymous responses, use multiple schools to avoid bias

10. Sample answer: Different time periods, different populations, different methodologies can all lead to different but valid results

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