# Psychological research: distinguish a design from a complete analysis — Study worksheet

[Study guides](https://unireadinglist.com/guides/psychological-research/)

[Course topic: Research Methods and Statistics](https://unireadinglist.com/subjects/psychology/#course-research-methods-statistics)

Sources checked: 2026-11-04

Study route and exercise: University Reading List editorial project.

For introductory psychological methods with arithmetic, means, proportions and basic graph reading. All units, scores and assignments below are synthetic. No study is conducted and no human data are used. The calculations describe finite assigned-arm means and explicit imputations; they do not by themselves identify a population causal effect.

## Choose your starting point

- [ ] I need a free broad methods spine
  Use Jhangiani and colleagues for measurement, designs and basic statistics. Practise distinguishing manipulation, measurement and sampling before selecting a test.

- [ ] I struggle to evaluate published claims
  Use Morling’s consumer/producer framework to connect claim type with evidence and validity. A well-labeled claim still needs outcome completeness and uncertainty checks.

- [ ] I need help interpreting numerical analysis
  Use Reidy and Dancey as the statistics companion after specifying the design. Software output cannot decide what the missing observations would have been.

## Compare the three books

### Research Methods in Psychology

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; Dana C. Leighton · 4 · 2019
Free design-and-measurement spine

Choose it when: You need an accessible complete methods course with experimental and non-experimental alternatives.
Use another route when: You want free access to mean unrestricted commercial reuse, or need a specialist missing-data monograph.
Selected reading: 2019 fourth edition: Psychological Measurement, Experimental Research, Non-Experimental Research, then Descriptive and Inferential Statistics.
Limits and trade-offs: The author-hosted book is free under noncommercial/share-alike conditions, with third-party exceptions. The selected ISBN identifies the e-book; the print identifier differs.

#### Try before choosing

Write the manipulation, measured outcome, assignment unit and missingness count for the original packet.
Check your work: Random assignment, random sampling and complete observation are three different properties.

Your notes:

________________________________

ISBN: 9781999198107

[Official free reading](https://kpu.pressbooks.pub/psychmethods4e/)

[Book record and access options](https://unireadinglist.com/subjects/psychology/research-methods-in-psychology-jhangiani/)

### Research Methods in Psychology: Evaluating a World of Information

Beth Morling · 5 · 2025
Claims-and-validity spine

Choose it when: You want to interrogate the relation between a reported claim and its design.
Use another route when: You need its framework to substitute for checking the actual sample, measurement or missing-data assumptions.
Selected reading: Fifth edition, 2025: chapter 3 on claims/validities,5 measurement,7 sampling,10–11 experiments; use the limited publisher demo only for its stated sample access.
Limits and trade-offs: The consumer/producer activities contrast with a test-selection manual. Commercial courseware and the complete book are not made free by the existence of a demo.

#### Try before choosing

Write the strongest defensible claim from complete-case means, then a stronger claim that requires an extra assumption.
Check your work: Name the exact extra assumption. An initially randomized allocation does not reveal scores subsequently missing.

Your notes:

________________________________

ISBN: 9781324085744

[Book record and access options](https://unireadinglist.com/subjects/psychology/research-methods-in-psychology/)

### Statistics without Maths for Psychology

John Reidy; Christine P. Dancey · 9 · 2024
Statistics-and-reporting companion

Choose it when: You need verbal and graphical explanations of common analyses and their interpretation.
Use another route when: You expect a software recipe or p-value to settle design validity or selection into observation.
Selected reading: Ninth edition, 2024 paperback ISBN 9781292463445: chapter 1 design,3 descriptives,4 sampling,7 two-condition comparisons,8 issues of significance.
Limits and trade-offs: Its SPSS guidance is useful when a course uses that software, but a book or eText subscription does not itself supply an SPSS licence. The reported results must answer a defined estimand.

#### Try before choosing

Compute observed means and their denominators before considering any significance test.
Check your work: Report assigned n, observed n and missing n separately, with score units. A mean without its population is ambiguous.

Your notes:

________________________________

ISBN: 9781292463445

[Book record and access options](https://unireadinglist.com/subjects/psychology/statistics-without-maths-for-psychology/)

## Check your preparation

### What is the target quantity?

Your notes:

________________________________

### What does random assignment solve?

Your notes:

________________________________

### What is a sensitivity assumption?

Your notes:

________________________________

## A six-step study plan

- [ ] Define the claim and unit

Selected reading: Morling claims discussion or Jhangiani design overview.

What to do: Write the outcome, unit, assignment mechanism and target group.

Check your work: Do not slide from an observed-arm contrast to an unqualified population effect.

Your notes:

________________________________

- [ ] Audit observation

Selected reading: Experimental-validity reading.

What to do: Make assigned/observed/missing tables for both arms.

Check your work: Attrition rates use assigned denominators, not only those who supplied a score.

Your notes:

________________________________

- [ ] Calculate complete cases

Selected reading: Reidy/Dancey descriptives or the free statistics part.

What to do: Compute sums, means and the observed contrast with units.

Check your work: The denominator is visible and the result is labeled complete-case.

Your notes:

________________________________

- [ ] Bound what is unknown

Selected reading: Measurement limits and the original example.

What to do: Use the stipulated 0–10 scale to obtain the lowest and highest assigned-arm contrast.

Check your work: For a lower difference, minimize A’s missing values and maximize B’s; reverse for the upper bound.

Your notes:

________________________________

- [ ] Add one explicit assumption

Selected reading: The changed-assumption follow-up.

What to do: Set a common missing-score mean m and derive the contrast as a function of m.

Check your work: This is not the same as setting each arm’s missing mean equal to its observed mean.

Your notes:

________________________________

- [ ] Write a reproducible conclusion

Selected reading: Reporting discussion in the chosen spine.

What to do: Report observed contrast, unconstrained bound and conditional sensitivity result together.

Check your work: Separate arithmetic uncertainty from sampling uncertainty and from whether the outcome measures the intended construct.

Your notes:

________________________________

## Worked exercise: Original attrition packet: the observed contrast can reverse

Synthetic arms A and B each start with ten assigned units. A has six observed 0–10 scores: 8,8,9,9,10,10. B has eight: 5,5,6,6,7,7,8,8. Other scores are missing but must lie in \[0,10\]. Compute complete-case means and the possible difference in all-assigned means A−B.

Assumptions: Initial allocation is stipulated randomized, but nothing is known about why scores are missing. The score is in points, bounded inclusively by 0 and 10. Every assigned unit has one underlying score. Treat this as a finite arithmetic packet, with no population or causal-effect claim supplied by the numbers.

Your notes:

________________________________

## Change an assumption: Changed assumption: equal missing means across arms

Keep all observed values but assume A’s four missing scores and B’s two missing scores share the same mean m, with 0≤m≤10. Derive the all-assigned contrast, its range and its value at m=6. This is an assumption, not a finding about the missingness mechanism.

Your notes:

________________________________

## Before moving on

- I can define the unit, target mean and observed denominator.
- I can reproduce the missing-score endpoints and common-mean sensitivity formula.
- I can distinguish a finite contrast, a conditional calculation and an inferential causal claim.

## Answer and interpretation

### What is the target quantity?

Here it is the difference between mean scores of all ten assigned units in each arm, not merely completers. A causal population quantity would require additional design and inferential reasoning.

### What does random assignment solve?

It specifies how initial groups were allocated. It does not automatically make later missingness random, measurements valid or the synthetic sample representative.

### What is a sensitivity assumption?

A transparent restriction on unknown values used to see how a conclusion changes. It is not an observed fact or a licence to fill values without reporting the restriction.

### Original attrition packet: the observed contrast can reverse

1. A’s sum is 54 and observed mean 54/6=9; B’s sum 52 and mean 52/8=6.5. Complete-case contrast is 2.5 points. Attrition is 4/10=40% versus 2/10=20%.
2. All-assigned A lies between 54/10=5.4 and (54+40)/10=9.4. B lies between 52/10=5.2 and (52+20)/10=7.2.
3. The smallest A−B is 5.4−7.2=−1.8, attained by A’s missing scores all 0 and B’s all 10. The largest is 9.4−5.2=4.2, with those choices reversed.
4. Both signs are compatible with the supplied observations. Randomization of assignment does not choose between these missing-value completions, and no p-value from the observed cases supplies them.

Observed means 9 versus 6.5, contrast 2.5 points; all-assigned contrast can range from−1.8 to 4.2 points. The observed positive sign is not secured for the stated finite target.

Test the result: Construct both endpoint completions explicitly and keep ten as each target denominator. The bound concerns missing outcomes, not a confidence interval or an estimate of a population causal effect.

### Change an assumption: Changed assumption: equal missing means across arms

1. A’s assigned mean is (54+4 m)/10=5.4+0.4 m; B’s is (52+2 m)/10=5.2+0.2 m. Their difference is 0.2+0.2 m.
2. As m ranges 0…10, the conditional difference ranges 0.2…2.2 points and remains positive. At m=6, A=7.8, B=6.4 and A−B=1.4.
3. The common-mean restriction removes the negative completions from the main packet. It is not implied by random assignment and differs from imputing 9 to A and 6.5 to B. No sampling uncertainty or population effect has been settled.

Under the common missing-mean assumption only, the finite contrast is 0.2+0.2 m, positive over \[0,10\]; at m = 6 it is 1.4 points.

Test the result: Withdraw the common-mean restriction and the original−1.8…4.2 bound returns. Report both results so that a reader can see which conclusion depends on the assumption.

## Common mistakes

### Randomized means complete

Random allocation does not recover an absent outcome or establish representativeness.

### Unknown means zero

Zero is one possible score, not the default meaning of a missing record.

### Sensitivity result presented as observed

An imputation rule changes the admissible values; it must appear beside the resulting claim.

## Sources and edition checks

- [KPU: free fourth edition, design and statistics contents](https://kpu.pressbooks.pub/psychmethods4e/)
  The free fourth edition supports the measurement/design/statistics sequence and distinguishes print and e-book identity.

- [Norton: fifth-edition research consumer/producer framework](https://seagull.wwnorton.com/researchmethodspsych)
  The fifth-edition framework motivates claim-to-evidence checks rather than a test-only workflow.

- [Norton: fifth-edition contents and limited demo](https://nerd.wwnorton.com/nerd/329410/r/goto/cfi/144%21/4?demo=true)
  The publisher’s limited demo verifies chapter titles but is not free complete-book access.

- [Pearson: ninth-edition statistics, formats and contents](https://www.pearson.com/en-gb/subject-catalog/p/statistics-without-maths-for-psychology/P200000011617/9781292474045)
  The selected print product is 2024 despite a©2025 header for the edition. Software exercises do not establish a software entitlement.

These are proposed study activities, not official course assignments or a claim that every book has been read cover to cover.

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