Year 11 – Health and Movement Science

Phase 4: Analysis and Conclusion

The purpose of Phase 4

Phase 4 is where your group carries out the investigation. In this phase, you collect the data using the method designed in Phase 3, then process, present and analyse that data to work out what it shows.

In the official NESA process, Phase 4 focuses on data collection, data presentation, and data analysis. This includes quantitative and/or qualitative data, presentation in textual, tabular, or diagrammatic form, and analysis using methods such as mean, median, correlation, and alignment to normative values.

Step 1: Carry out the data collection

Start by following the exact method your group planned in Phase 3. The more consistently you follow the method, the stronger your data will be. If your group changes the process during collection, the quality of the data can be affected.

What to do

  • review the method before starting
  • make sure each group member knows their role
  • prepare all equipment, forms, spaces, and materials
  • follow the procedure as planned
  • record results straight away
  • use the same approach each time.

What to watch for

  • missing steps
  • inconsistent instructions
  • rushed recording
  • different group members doing the same step in different ways

Example: If your group is running a short exercise test, all participants should complete the same activity, for the same time, under the same conditions as much as possible.

Step 2: Keep the process ethical and safe

Ethical responsibilities do not stop once data collection begins. Throughout Phase 4, your group still needs to protect consent, privacy, confidentiality, and fairness. Participants should be treated respectfully, reminded that participation is voluntary, and allowed to withdraw if they want to.

What to do

  • remind participants what they are agreeing to
  • make sure participation is voluntary
  • allow participants to skip questions or stop if needed
  • keep completed surveys, notes, and files secure
  • monitor any physical or emotional risks during the process.

Why this matters

Good data is not enough on its own. The investigation also needs to be conducted responsibly.

Example: If your group is collecting data about stress, do not leave completed surveys where other students can read them.

Step 3: Gather enough useful data

Try to collect the amount of data your group originally planned. This might mean reaching a target number of participants, responses, observations, or trials. Quality matters as much as quantity, so your group should also check that the data is clear and usable.

What to do

  • aim to reach your planned sample size or number of trials
  • check that responses are complete and readable
  • check that measurements make sense
  • note any missing or unusable data.

If something goes wrong

If your group collects less data than planned, or if some data cannot be used, do not ignore it. Either collect more data if that is realistic, or note it as a limitation later.

Example: If your group hoped for 30 survey responses but only received 18, that does not ruin the investigation, but it should be acknowledged later.

Step 4: Record raw data clearly and consistently

As data is collected, it needs to be recorded in a way that is clear, consistent, and easy to follow. Poor recording creates problems later, even if the method itself was strong. NESA’s supporting material emphasises using the same recording system throughout, such as a spreadsheet, table, results sheet, or logbook.

What to do

  • use the recording format planned in Phase 3
  • label each result properly
  • use the same units each time
  • write legibly if recording by hand
  • check digital entries carefully
  • have one group member check the data after collection if possible.

What labels may include

  • participant number
  • date
  • time
  • trial number
  • category
  • measurement unit.

Example: Instead of writing Score = 8, write Participant 04, pre-test concentration score = 8/10. This is important because it makes the result traceable (who and what was measured), so your group can compare like-for-like data and check reliability and validity later.

Step 5: Document any incidents or changes

If anything unexpected happens during collection, write it down. This could include equipment problems, participant withdrawals, mistakes in the process, or small changes your group had to make. These details may become important later when evaluating the investigation.

What to record

  • what happened
  • when it happened
  • whether it affected the data
  • what your group did in response.

Example: If one trial had to be repeated because the stopwatch was started late, that should be noted.

Step 6: Organise and clean the raw data

Once collection is complete, your group should organise the raw data so it is easier to work with. This may include entering it into a spreadsheet or table, checking for repeated entries, incomplete answers, or obviously incorrect responses, and deciding how those issues will be handled.

What to do

  • put the raw data into one organised place
  • check for missing responses
  • check for repeated entries
  • check for impossible or clearly incorrect values
  • decide how to handle unusable entries
  • keep a record of those decisions.

Example: If a participant writes that they do 110 hours of exercise per week, your group would need to decide whether to exclude that response and note why.

Step 7: Present the data clearly

NESA’s Phase 4 requires data presentation in textual, tabular, or diagrammatic form. This means your group needs to present the results in a way that makes them easier to understand.

Options for presenting data

  • textual presentation for short written summaries or qualitative findings
  • tabular presentation for exact figures
  • diagrammatic presentation such as graphs or charts for patterns and comparisons.

Choosing the right format

  • use a bar graph to compare groups
  • use a line graph to show change over time
  • use a pie chart to show proportions
  • use a scatter plot to show a relationship between two variables.

What every table or graph needs

  • a clear title
  • labelled axes or headings
  • units of measurement
  • enough detail for the reader to understand what is being shown.

Example: A graph labelled only “Results” is weak. A graph labelled “Average reaction time before and after caffeine consumption (milliseconds)” is much stronger.

Step 8: Analyse the data

This is the part where your group works out what the results actually show. NESA identifies data analysis in Phase 4 and includes methods such as mean, median, correlation, and alignment to normative values.

For quantitative data

Your group may:

  • calculate mean
  • calculate median
  • work out percentages
  • compare groups
  • identify trends
  • look for correlations
  • compare results to normative values or benchmarks.

Example: If your group surveyed students about screen time, you might compare your class average to an Australian benchmark or guideline.

For qualitative data

Your group may:

  • identify repeated ideas
  • group responses into themes
  • use coding
  • choose short representative examples if needed.

Example: If several participants mention feeling calmer, more focused, or less stressed, those responses could be grouped under a theme such as improved concentration and mood.

Important!

Analyse the data honestly. Do not try to force the findings to match the hypothesis. Unexpected findings are still valid findings. It will probably give you more to talk about in your presentation!

Step 9: Start interpreting what the findings mean

By the end of Phase 4, your group should begin working out what the findings suggest. This is not the full Phase 5 conclusion yet, but it is the point where your group starts linking the results back to the research question, the hypothesis if relevant, and the background research.

What to do

  • ask whether the findings support the research question or hypothesis
  • compare the findings with earlier research
  • note any patterns, trends, surprises, or mixed results
  • avoid making claims that go beyond the data.

Be careful with interpretation

A correlation may show a relationship, but it does not automatically prove cause and effect.

Example: If students who sleep more also report lower stress, that shows a relationship. It does not automatically prove that sleep alone caused the lower stress.
↳ It could be that a third factor, such as better time management, lower overall workload, stronger social support, or fewer financial pressures, leads to both more sleep and lower stress.
↳ It could also be that students who feel less stressed find it easier to fall asleep and stay asleep (so lower stress leads to more sleep), or that the link is partly explained by another health behaviour, such as regular physical activity, better nutrition, or less caffeine use.

So how do you report on this?
The safe way to state it is: “There is a relationship between sleep and stress in our results.” i.e that’s a correlation: when one goes up (sleep), the other tends to go down (stress).
Do not say: “Sleeping more causes lower stress” because your data didn’t prove cause-and-effect. It only showed they happen together.

Later in the presentation you could say something like: “Our results suggest sleep and stress are linked, but we can’t say sleep causes lower stress. There may be other factors (like time management, support, exercise, diet, caffeine) affecting both. To test cause, we’d need a different type of study (e.g. changing sleep patterns on purpose and measuring stress).”

Step 10: Check reliability and validity

Before moving on, your group should think about whether the data is trustworthy.

Questions to ask

  • Was the method carried out consistently?
  • Do the results reflect what you were actually trying to measure?
  • Were there any strange or unreliable responses?
  • Were there any outliers or errors that affected the results?

Why this matters

This helps your group prepare for the evaluation in Phase 5.

Example: If some participants clearly joked in open-ended survey answers, your group may decide those responses should not be used in the final analysis.

Step 11: Save, back up, and review as a group

Before ending Phase 4, make sure all data and analysis work is saved properly. Your group should also discuss the findings together so that everyone understands what the data shows. This is especially important in a Collaborative Investigation, where shared understanding matters.

What to do

  • save digital files in more than one place
  • keep paper records secure
  • make sure all members understand the main findings
  • discuss any unusual results together
  • keep a record of any group decisions made during analysis.

What you should have by the end of Phase 4

By the end of Phase 4, your group should have:

  • completed data collection
  • recorded raw data clearly
  • organised and cleaned the data
  • presented the data in textual, tabular, and/or diagrammatic form
  • analysed the data using methods that suit the investigation
  • identified the main findings
  • started interpreting what those findings mean
  • saved and backed up all data and analysis work.
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