Lecture 1: Core Concepts in Inferential Statistics
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1. Key Definitions & Explanations
Inferential Statistics are a branch of statistics used to draw conclusions about a population based on a sample.
Why it matters: We usually can’t measure an entire population, so we use samples and probability to estimate population characteristics.
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Sampling Distribution
Definition: The distribution of a statistic (e.g. sample mean, X̄) across all possible samples of the same size from a population.
Why it matters: It shows what kinds of values the statistic (like a mean) might take just by chance when sampling repeatedly from the same population.
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Central Limit Theorem (CLT)
Definition: If you take many samples of size n, the distribution of the sample means will be approximately normal, even if the original population isn’t — as long as n is large enough.
* Mean of sampling distribution = μ
* Standard deviation = σ / √n → called the Standard Error (SE)
Why it matters: CLT lets us apply normal distribution rules and z-scores to sample means.
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Standard Error (SE)
Definition: The standard deviation of the sampling distribution.
* If population σ is unknown: estimate using s / √n
* A smaller SE means your sample mean is more precise
Why it matters: SE tells us how much we expect sample means to vary. It’s crucial for calculating z-scores and testing hypotheses.
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Hypothesis Testing – The 3 Core Steps
1. Form a Hypothesis
* Null hypothesis (H₀): Population mean = some value (e.g. μ = 125)
* Alternative hypothesis (H₁): Population mean is different (e.g. μ > 125)
2. Collect a Sample
* Take a sample from the population
3. Compare
* Use probability theory (e.g. z-scores) to assess how likely the sample result is, assuming H₀ is true
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Z-Score for Sample Means
Formula:
z=Xˉ−μσ/nz = \frac{X̄ - μ}{σ / \sqrt{n}}
What it means: Tells you how many SEs your sample mean is from the hypothesised population mean.
Why it matters: Lets you calculate the probability of getting a result as extreme as your sample, assuming H₀ is true.
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2. Hypothesis Testing Concepts
Term
Definition
Example
H₀ (Null Hypothesis)
No effect/difference; status quo
μ = 125
H₁ (Alternative Hypothesis)
There is an effect or difference
μ > 125
Significance Level (α)
Cut-off for rejecting H₀
α = .05 or .01
Region of Rejection
Tail ends of the sampling distribution where results are too unlikely under H₀
If p < α, reject H₀
Critical Value
The z-score boundary of the region of rejection
±1.96 (for α = .05 two-tailed)
Observed z (zobs)
The calculated z-score from your sample
Compare with critical z
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3. One-Tailed vs Two-Tailed Tests
Test Type
Use When...
Critical z (α = .05)
Notes
One-Tailed
You predict a specific direction (e.g. increase)
±1.64
More powerful, but riskier
Two-Tailed
You don’t predict the direction
±1.96
More conservative
* One-tailed example: H₁: μ > 125 → You expect memory to increase
* Two-tailed example: H₁: μ ≠ 125 → You expect a change, not sure which way
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4. Worked Example: Ginkgo Biloba & Memory
* Population mean: μ = 125
* Population SD: σ = 10.5
* Sample size: n = 20
* Sample mean: X̄ = 130
Step 1: Hypotheses
* H₀: μ = 125
* H₁: μ > 125 (one-tailed)
Step 2: Calculate z
z=130−12510.5/20≈2.13z = \frac{130 - 125}{10.5 / \sqrt{20}} ≈ 2.13
Step 3: Find p
* Area below z = 2.13 → 0.9834
* p = 1 − 0.9834 = 0.0166
Conclusion:
* p < .05 → Reject H₀
* Interpretation: Ginkgo Biloba likely improves memory
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5. Why Use Normal Distributions?
* Many psychological traits (IQ, memory) are normally distributed
* Lets us apply probability to real-world scores
* Z-tables convert z-scores to percentiles
Examples:
* z = 1 → 84.13% of values fall below
* z = -1 → 15.87% below
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6. Summary Cheat Sheet
Concept
What It Tells You
Inferential Stats
Are your sample results meaningful?
CLT
Why we can use normal distributions for X̄
Standard Error
How much the sample mean is expected to vary
Z-Score
How extreme your sample result is
Significance Level
Where to draw the line to reject H₀
Region of Rejection
Where sample results are too unlikely under H₀
One-Tailed Test
Tests a predicted direction
Two-Tailed Test
Tests any difference, no predicted direction
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Lecture 2: Decision Errors in Hypothesis Testing
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1. What Are Decision Errors?
In hypothesis testing, you're making an educated guess about a population based on your sample data.
Sometimes, that guess is wrong — this is where decision errors come in.
There are two types:
* Type I Error (False Positive)
* Type II Error (False Negative)
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2. Type I Error (False Positive)
Definition: Rejecting the null hypothesis (H₀) when it is actually true.
You conclude there is an effect, when in reality, there isn't.
* Probability of making this error = α (alpha)
→ Common values: 0.05 or 0.01
* Example: Saying a drug works when it actually doesn’t
* On a graph:
* The black shaded area (right tail) represents this error
* It lies beyond the critical value where you reject H₀
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3. Type II Error (False Negative)
Definition: Failing to reject the null hypothesis (H₀) when it is actually false.
You miss a real effect that exists.
* Probability of this error = β (beta)
* Power of a test = 1 − β
→ Power is the chance of correctly rejecting H₀ when it’s false
* Example: Saying a drug doesn’t work when it actually does
* On a graph:
* The yellow shaded area (left tail of H₁ distribution) shows this error
* Occurs when true values of H₁ fall close to the critical value, making them look like H₀
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4. Summary Table: Outcomes in Hypothesis Testing
Decision
Truth
Probability Symbol
Meaning
Reject H₀
H₀ is true
α
Type I Error
Retain H₀
H₀ is true
1 − α
Correct Decision
Reject H₀
H₀ is false
1 − β
Correct Decision (Power)
Retain H₀
H₀ is false
β
Type II Error
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5. Relationship Between Type I and Type II Errors
* You can only make one type of error per test (not both)
α and β are linked:
* Increasing α (e.g. from 0.01 to 0.05) reduces β
→ Increases power (chance of detecting a real effect)
* But it also increases the risk of a Type I Error
* Choosing α = 0.05 vs 0.01 depends on how serious each error would be in your context
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6. How to Minimise Type II Errors (Increase Power, Reduce β)
1. Increase α (significance level)
* Use α = 0.05 when Type I errors are less serious
* Use α = 0.01 when Type I errors have serious consequences
2. Increase sample size (n)
* Larger samples = less variability
* This makes it easier to detect real effects
* Reduces β without increasing α
3. Use more powerful statistical tests
* Parametric tests (e.g. t-tests, ANOVA) have more power if assumptions are met
4. Improve experimental design
* Control confounding variables
* Use reliable measures
* Reduce random noise
* Plan carefully to increase signal clarity
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7. Visual Summary of Errors and Decisions
Graph setup:
* Two bell curves:
* One for H₀ (e.g. mean = 125)
* One for H₁ (e.g. mean > 125)
* A critical value marks the boundary between rejecting or retaining H₀
Shaded areas:
* Black (right) = Type I Error → Rejecting H₀ when it's actually true
* Yellow (left) = Type II Error → Failing to reject H₀ when it’s false
Clear areas:
* Right side of H₁ curve = Power (correct rejection of H₀)
* Left side of H₀ curve = Correct retention when H₀ is true
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Lecture 3: t-Tests and Unknown Population SD
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1. From Known to Unknown Population SD (σ)
Previously:
* You knew both population mean (μ) and standard deviation (σ).
* You could use the normal distribution to run your test.
But in real life:
* You usually don’t know σ, so you estimate it using the sample standard deviation (s).
⚠️ Estimating from a sample adds uncertainty → this changes the distribution we use.
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2. Why Not Use the Normal Distribution for Small Samples?
When n < 120 and σ is unknown:
* The normal distribution underestimates uncertainty.
* Instead, use the t-distribution, which:
* Is wider (fatter tails)
* Changes shape depending on sample size
* Reflects added uncertainty from estimating σ
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3. Degrees of Freedom (df)
The t-distribution depends on degrees of freedom (df).
* df = n − 1, where n = sample size
* Think of df as: the number of values that are "free to vary" once the mean is known.
🔑 Why this matters:
* Smaller df = wider t-distribution (more uncertainty)
* Affects the critical t-value used for rejecting H₀
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4. Single-Sample t-Test
Used when:
* You have one sample
You want to test whether its mean differs from a hypothesised μ
* You don’t know σ, only have sample data
Assumptions:
* Data are interval/ratio scale
* Sample scores are normally distributed
* You estimate standard error using s
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Steps to Perform a Single-Sample t-Test
1. Calculate observed t-value
t=Xˉ−μs/nt = \frac{X̄ - μ}{s / \sqrt{n}}
2. Degrees of freedom
df=n−1df = n - 1
3. Find the critical t-value
* Use a t-table with your df and chosen α (e.g. 0.05)
4. Compare
* If ∣tobs∣≥tcritical|t_{obs}| ≥ t_{critical}, reject H₀
* If not, retain H₀
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Worked Example
* Hypothesised mean: μ = 125
* Sample mean: X̄ = 130
* Sample size: n = 20
* Sample SD: s = 11.5
Step 1 – Standard Error:
sXˉ=11.520=2.57s_{X̄} = \frac{11.5}{\sqrt{20}} = 2.57
Step 2 – t-value:
t=130−1252.57=1.94t = \frac{130 - 125}{2.57} = 1.94
Step 3 – Degrees of Freedom:
df=20−1=19df = 20 - 1 = 19
Step 4 – Compare to critical t:
* For a two-tailed test, α = 0.05, critical t = 2.093
Conclusion:
1.94 < 2.093 → retain H₀ (no significant difference)
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5. Two-Sample t-Test
Used when comparing two groups (e.g., treatment vs control).
* Tests if the difference between sample means is statistically significant
tobs=Xˉ1−Xˉ2sXˉ1−Xˉ2t_{obs} = \frac{X̄_1 - X̄_2}{s_{X̄_1 - X̄_2}}
Where:
* Xˉ1X̄_1 and Xˉ2X̄_2 are sample means
* sXˉ1−Xˉ2s_{X̄_1 - X̄_2} is the standard error of the difference
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Two Designs:
1. Independent Samples
* Different participants in each group
* e.g., Group A vs Group B
2. Related Samples (Paired/Repeated Measures)
* Same participants tested twice, or
* Matched pairs (e.g., twins, before/after design)
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Key Logic Stays the Same:
* Estimate standard error based on group data
* Calculate t
* Compare to critical t using df and α
* Make decision: reject or retain H₀
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Lecture 4: Basic Anatomy and Structural MRI
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Neurons: Key Building Blocks
* Neurons receive signals from the external world and body, transmit signals between each other, and send signals back to the body.
* Components:
* Cell body (soma): Contains nucleus (DNA), mitochondria (energy).
* Dendrites: Short, branch-like structures that receive messages from other neurons and transmit them to the cell body; can also transmit messages in specialized neurons.
* Axon: Long tube-like structure carrying electrical signals from cell body to axon terminals.
* Axon terminals: Pass information to dendrites of other neurons.
* Myelin: Fatty substance that speeds up signal transmission along axons.
* Neurons have dense dendritic connections, which differ from other mammals.
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Brain Tissue Types
* Brain is organized into:
* Grey matter: Contains neuronal cell bodies, dendrites, and support cells (e.g., astrocytes).
* White matter: Mainly composed of axons.
* Cerebrospinal fluid (CSF): Surrounds and cushions the brain.
* Cell body and dendrites = Grey matter; axons = White matter.
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Grey Matter Functions
* Houses neuronal cell bodies and support cells.
* Functions include:
* Processing and interpreting information for sensation and perception.
* Sending signals for coordinated movement and automatic processes.
* Mental functions: decision making, memory, cognition.
* Importance: Damage or degradation leads to major changes in behavior and body function.
* Damage causes:
* Normal aging leads to grey matter degradation.
* Risk factors accelerating damage: high blood pressure, diabetes, smoking.
* Diseases affecting grey matter: Alzheimer’s, Parkinson’s, stroke, trauma, anxiety, depression, autism, diabetes, and others.
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Introduction to MRI
* MRI images are composed of 3D pixels called voxels (0.5–3 mm).
* Each voxel contains thousands of neurons; MRI measures average signals.
* Images can be blurry due to patient movement or “noise” (indirect measurement via magnetic properties).
* Scanner settings can be adjusted for different image types depending on research or clinical questions.
* MRI analysis is heavily based on statistics.
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MRI Basics
* Uses a strong magnetic field (baseline always on).
* Safety checks required (pacemakers, metal clips, pins, etc.).
* Based on the magnetic moment of hydrogen nuclei (protons) in water and fat.
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Structural MRI
* Visualizes gross brain anatomy (<1 mm resolution).
* Scan resolution affects detail and scan time.
* Different scan types highlight various tissues, iron content, or blood vessels.
* Some use contrast agents to improve visualization.
* Standard structural MRI scan takes about 5 minutes to acquire one high-resolution 3D image.
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Clinical Use of Structural MRI
* Used by medical doctors to visually identify pathologies without formal analysis:
* Tumors
* Traumatic brain injury
* Stroke damage
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Research Use of Structural MRI
* Workflow: MRI scanning → Experiment analysis → Results → Interpretation.
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Example Research Question
* How does the volume of grey matter in the gustatory cortex differ between people who prefer ice cream versus fish and chips?
Hypotheses
* H0 (Null): No difference in grey matter volume between groups.
* H1 (Alternative): There is a difference (one- or two-tailed depending on research design).
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MRI Image Processing Steps
1. Acquire images.
2. Brain extraction (remove non-brain tissue).
3. Segmentation (separate grey matter, white matter, CSF).
4. Isolate gustatory cortex.
5. Quantify grey matter volume.
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Interpreting Grey Matter Volume Graphs (Hypothetical)
* Graph 1: Moderate mean difference, high overlap → high variation → difficult to conclude real difference (likely non-significant).
* Graph 2: Large mean difference, low overlap → strong evidence for real difference → likely significant result.
* Graph 3: Small mean difference, low variation → difference small but consistent → can still be statistically significant.
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The t-Test
* t-statistic formula:
t=Difference between group meansStandard error (variation within groups)t = \frac{\text{Difference between group means}}{\text{Standard error (variation within groups)}}
* Even a small mean difference can be significant if within-group variation is low (Graph 3).
* The t-test evaluates how much group means differ relative to data variability.
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Statistical Inference
* The t-distribution is used when population standard deviation is unknown and sample size is small.
* A p-value < 0.025 (two-tailed) indicates statistical significance, supporting rejection of the null hypothesis.
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Limitations of Structural MRI
* Does not directly measure tissue types (GM/WM/CSF); uses relative differences.
* Resolution limited to millimeters; each voxel contains thousands of cells.
* Poor distinction between bone and air.
* Variable contrast in deep brain regions; some structures hard to visualize.
* Single scans don’t capture all pathologies.
* Presence of noise and artifacts due to indirect measurement.
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Complementary Techniques
Computed Tomography (CT):
* Uses X-rays, better bone imaging.
* Shows membranes, vessels, tumors.
* Safer for metal implants.
* Spatial resolution ~0.5–2 mm.
Histology:
* Microscopic anatomy (micrometer resolution).
* Post-mortem or biopsy samples.
* Uses staining to highlight cells.
* Useful for detecting tissue damage, tumors, inflammation, pathogens.
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Lecture 5 Notes
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White Matter: Function and Importance
* White matter connects different brain regions, enabling communication and information exchange.
* Axons in white matter are often covered with myelin, which speeds up electrical signal transmission. This myelin makes the white matter appear white.
* Damage or disruption in white matter can cause behavioral and bodily function changes.
* Normal aging degrades white matter.
* Risk factors accelerating white matter damage include:
* High blood pressure
* Diabetes
* Smoking
* Diseases affecting white matter:
* Multiple Sclerosis (MS)
* Stroke
* Also linked to Parkinson’s, Autism, and others.
Diffusion MRI (dMRI) Principles
* Based on water molecule movement (diffusion) in brain tissue.
* Water diffusion is directionally restricted in white matter due to axon structures, providing info on axon direction and integrity.
* Measures average diffusion in a 3D pixel (voxel), not single axons.
* Diffusion MRI takes many fast images (~100-300) over ~5 minutes; each image measures diffusion along one gradient direction.
* Has lower spatial resolution (1-3 mm) compared to structural MRI (<1 mm), making images blurrier.
* Sensitive to distortions from air pockets (e.g., sinuses).
* Gradient coils control the magnetic field direction to measure diffusion along different axes.
* Difficulties include crossing fibers and interpreting directions in complex regions.
Clinical Use of Diffusion MRI
* Clinicians use built-in analysis tools on diffusion scans to detect:
* Tumors
* Demyelination (e.g., MS)
* Stroke damage
* Neurodegenerative diseases
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Example Research Question
* Q: How does grey matter volume in the gustatory (taste) cortex differ between people who prefer ice cream vs fish and chips?
* Q: How does white matter integrity between the thalamus and gustatory cortex relate to ice cream enjoyment?
Hypotheses
* H0 (Null Hypothesis): No difference or no relationship (exact phrasing depends on test)
* H1 (Alternative Hypothesis): There is a difference or relationship (one- or two-tailed depending on prediction)
Analysis Steps
1. Obtain axonal integrity measures.
2. Isolate the white matter tract between thalamus and gustatory cortex.
3. Quantify average axonal integrity along the tract.
4. Correlate axonal integrity with ice cream enjoyment scores.
5. Interpret results:
* Positive relationship means higher integrity corresponds to higher enjoyment.
* Correlation strength given by r-value.
* Cannot infer causation or rule out confounding variables.
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Diffusion MRI Limitations
* Does not measure axon size or density directly.
* Measures averages of fiber groups, not individual fibers.
* Crossing/kissing fibers complicate interpretation.
* Difficult in pulsatile areas (e.g., brainstem).
* Limited by scanner hardware.
* Susceptible to fast imaging artifacts.
Complementary Techniques
* Tracer Studies: Track individual fibers with high spatial resolution, used post-mortem.
* Histology: Measures axonal and myelin dimensions directly at microscopic scale, but requires tissue samples.
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Functional MRI (fMRI)
* Measures changes in MRI signal over time during tasks or rest.
Captures Blood Oxygen Level Dependent (BOLD) signals reflecting changes in blood oxygenation.
* BOLD signal reflects the haemodynamic response to neuronal activity:
* Initial dip (brief oxygen consumption spike)
* Peak (increased blood flow oversupply)
* Post-stimulus undershoot (readjustment)
* Structural MRI: 1 image in ~5 minutes, high spatial resolution (<1 mm).
* Diffusion and fMRI: many images in ~5 minutes, lower resolution (1-3 mm), sensitive to artifacts.
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Clinical and Research Use of fMRI
* Rarely used clinically except in pre-surgical mapping (e.g., tumor resection).
* Widely used in psychological research to study brain-behavior links.
* Experimental workflow: MRI scanning > physiology > experiment > analysis > interpretation.
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Neuronal Energy Demand and Hemodynamics
* Neurons need oxygen and glucose delivered by blood.
* Increased neuronal activity → increased blood flow and oxygen delivery (haemodynamic response).
* fMRI detects these changes via BOLD signal.
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Example fMRI Food Preference Experiment
* Participants receive:
1. Milkshake (sweet stimulus)
2. Tasteless drink (control)
3. Rest periods (baseline)
* Single-subject regression models analyze brain activation.
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Functional MRI Limitations
* Does not measure electrical or metabolic activity directly.
* Measures blood oxygenation changes secondary to neural activity.
* Sensitive to artifacts.
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Other Imaging Techniques
Technique
Measures
Temporal Resolution
Spatial Resolution
Notes
PET
Brain metabolism (radioactive tracers)
Slow (5-10 seconds/image)
Lower (4-5 mm)
Can be combined with CT or MRI
EEG
Brain electrical activity
Very fast (milliseconds)
Poor (cm scale)
Measures surface activity, used for epilepsy etc.
Combined EEG/fMRI
Electrical + Blood oxygenation
Combined advantages
Combined spatial info
Provides richer data, but complex
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Lecture 6: Psychology and Qualitative Research
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What is Psychology?
* According to the American Psychological Association (APA):
“Psychology is the study of the mind and behaviour and that the discipline embraces all aspects of the human experience.”
* Key question: How do we best investigate all aspects of human experience?
* Qualitative research argues that sometimes words are more important than numbers.
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What is Qualitative Research?
* A research method producing descriptive (non-numerical) data, like:
* Observations of behaviour
* Personal accounts of experiences
* Goal: To understand how individuals perceive the world from their unique viewpoints.
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Core Aspects of Qualitative Research:
* Focus on the human experience
* Use quotes or images as data
* Capture the original qualities of the data
* Explore the unknown
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Qualitative Research vs Quantitative Research:
* Qualitative:
* Usually no specific hypothesis beforehand (Denzin & Lincoln, 2005)
* Guided by a research question
* Offers richer, deeper, broader understanding than quantitative methods
* Can generate scientific theories from the data itself (Harré, 2004)
* Quantitative:
* Uses numbers and statistical analysis
* Seeks to infer meaning from numbers
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Modes of Representation:
* Quantitative: Meaning is inferred from numbers and properties of interest.
* Qualitative: Meaning is represented through language patterns (discourse), preserving the original quality of the data using quotes and images.
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When to Use Qualitative Research?
* When the research aim requires understanding of meaning and experiences, rather than just measuring differences or frequencies.
* Example:
* Q: Do most PSYC210 students want to be clinical psychologists?
→ Quantitative approach (numbers, percentages) is ideal
* Q: What careers do PSYC210 students want to have?
→ Qualitative approach (descriptions, quotes) is ideal
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What Drives Qualitative Research?
* Can numbers alone explain everything about human experience?
* Philosophy of Psychological Research (Harré, 2004):
* Material Properties: Quantifiable traits of phenomena (e.g., height, weight)
* Intentional Properties: The meaning people assign to those traits (e.g., what “airwaves” mean to different people)
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Intentional Properties & Measuring Meaning:
* When people are involved, meaning matters.
* Numbers can tell us what happened (e.g., Group A performed better), but can they capture why or how people make sense of things?
* Understanding intentional properties (meaning making) is the primary focus of Qualitative Psychology.
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Lecture 7: Ontology, Epistemology, Reflexivity & Qualitative Research
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Ontology: The Question of Existence
* Concerns what exists, how reality operates, and what it means to ‘be.’
* Two main views:
* Naïve Realism: Reality exists objectively and can be directly known; our perceptions are straightforward reflections of the real world.
* Strong Relativism: Reality is socially constructed; perceptions vary by person and context, so there is no single objective reality.
* Ontology exists on a spectrum:
Naïve Realism > Critical Realism > Strong Relativism
* In research:
* Quantitative research aligns with Naïve Realism (one true reality).
* Qualitative research aligns with Strong Relativism (multiple constructed realities).
* Critical Realism sits in the middle: acknowledges subjectivity and context but holds that more accurate knowledge is possible through research.
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Epistemology: The Nature of Knowledge
* Concerns what we know and how we know it.
* Two main positions:
* Positivism: Knowledge is ‘out there’ to be discovered objectively; truth is provisional but can be approached via objective methods.
* Assumes researcher can avoid bias with proper tools.
* Experiments and objective measurements are ideal.
* Example: Measuring the width of a room objectively.
* Social Constructivism: Knowledge is created through social processes and interpretations.
* Truth is not discovered but constructed and situated.
* Language shapes reality; knowledge emerges through interaction.
* Research focuses on meaning and interpretation, often via qualitative methods.
* Epistemology also exists on a spectrum:
Positivism > Social Constructivism
* In research:
* Quantitative research mostly reflects Positivism.
* Qualitative research mostly reflects Social Constructivism.
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Reflexivity: Awareness of Researcher Influence
* Research doesn’t happen in isolation — researchers bring their values, experiences, and biases.
* Reflexivity means being self-aware and critically examining how the researcher’s perspective influences:
* Research design
* Data collection
* Analysis
* Reporting
* Key questions in reflexivity:
* Who benefits from this research?
* Who might be excluded?
* How do my beliefs shape my interpretation?
* Reflexivity is embedded throughout the research process.
* Qualitative researchers are “human instruments” actively shaping and interpreting data.
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Double Hermeneutic in Qualitative Research
* Two layers of interpretation in interviews/focus groups:
1. Participant tries to make sense of their own experience and explain it.
2. Researcher tries to make sense of the participant’s meaning-making.
* This process involves ontology, epistemology, and reflexivity.
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Image Analysis: Communicating Subjective Experience
* Ontology: Reality of experience is private; what we experience is larger than what we can describe, and what others interpret is filtered further.
* Epistemology: Knowledge is constructed; understanding another’s experience is always an interpretation, not direct access.
* Reflexivity: Encourages reflection on communication limits and how both speaker and listener shape meaning.
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Practical Drivers of Qualitative Research: The Role of Questions
* Quality qualitative research depends on the questions asked and how they’re asked.
* Preparation and practice are essential.
* Types of questions:
* Closed questions: Fixed choices, often used in quantitative research (e.g., “Do you want to be a psychologist? Yes/No/Maybe”)
* Open-ended questions: Invite elaboration and rich description, key for qualitative research (e.g., “What does your ideal career look like?”)
* How questions are phrased shapes the kind of data gathered.
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Lecture 8: Qualitative Research Questions, Sampling, Question Design & Ethics
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Qualitative Research Questions
* The research question guides the purpose, design, methods, analysis, and reporting of the study.
* Quantitative research questions usually involve hypotheses or predictions expressed numerically (e.g., "Do university students watch more movies than others?").
* Qualitative research questions are broader and exploratory, focusing on understanding experiences or perceptions (e.g., "Why did Dr. Arahanga-Doyle find the character Korg funny?").
* Good qualitative research questions:
1. State the goal clearly (what you want to understand).
2. Define the population sample (who you study).
3. Define the setting (where the study takes place).
4. Identify the primary topic (the main focus or phenomenon).
5. Be precise and feasible to realistically answer within constraints.
* Example:
“The research question is to understand why Hitaua found Korg funny in Thor: Ragnarok.”
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Study Design
* General types: Cross-sectional (one point in time) or longitudinal (over time).
* Key steps:
1. Sampling participants
2. Planning interview questions
3. Addressing ethical considerations
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Sampling
* Quantitative sampling:
* Focused on statistical power to detect effects.
* Requires random sampling from the population.
* Qualitative sampling:
* Driven by the research question.
* Focused on depth and richness of data.
* Answers “Who do we need to talk to for meaningful insights?”
* Types of qualitative sampling (Sullivan & Forrester, 2019):
* Sample of convenience: easiest available participants.
* Purposeful sampling: selecting participants with specific characteristics.
* Homogeneous sample: participants with similar experiences.
* Comparative sample: varied experiences around the same topic.
* Diverse sample: maximal variation to gain multiple perspectives.
* Snowball sampling: participants recruit others.
* Theoretical sampling: ongoing, flexible sampling guided by emerging findings.
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Interview Question Design
* Fundamental to qualitative research success.
* Two main types:
1. Closed questions: fixed choices (e.g., “Yes,” “No,” “Maybe”).
2. Open-ended questions: invite detailed, expanded answers.
* Key tips for good questions:
1. Use clear, answerable wording.
2. Start easy, then move to deeper questions.
3. Use open, non-leading questions.
4. Silence is okay – gives space to think.
5. Know your questions well – practice is essential.
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Ethical Principles in Psychological Research
Core principles include:
1. Cultural sensitivity: respect participants’ cultural backgrounds.
2. Informed consent: participants must understand and agree freely.
3. Protection from harm: minimize risks and distress.
4. Confidentiality: protect participants’ privacy and data.
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Stanford Prison Experiment (Video Warning)
* The video contains strong language and depicts cruelty.
* Questions to consider while watching.
* How does this differ from real prison?
* Differences between the two ‘prison’ studies?
* Why were the studies terminated early?
* Who appeared most distressed?
* Would you participate in this kind of study?
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Lecture 9: Ethical Principles in Psychological Research
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Origins of Ethical Principles
* Developed in response to unethical studies that caused:
* Harm (e.g., Stanford Prison Experiment)
* Deception (e.g., Milgram Electric Shock Studies)
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Variations on Milgram’s Study Conditions
* Touch: Teacher holds learner’s hand on shock plate (baseline = no touch).
* Group pressure to disobey: Two other “teachers” refuse to continue shocks, encouraging participant to do the same.
* Conflicting experimenters: One experimenter tells to stop, the other to continue.
* Intimate relationships: Learner is a friend or relative, not a stranger.
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Ethical Issues & Racial Prejudice
* Research historically privileged Western, white perspectives (ethnocentrism).
* Example: Apartheid-era research reinforced racial hierarchies (1948–1994).
* Contemporary ethical research aims to challenge this bias (Linda Tuhiwai Smith, 2012).
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Ethics and Participant Involvement
* Quantitative approach: Participants as “subjects” who provide data.
* Qualitative approach: Participants as “co-researchers” engaged with researchers, not studied on.
* Both approaches require participant protections and sometimes research training.
* Ethics committees review and approve research plans as a safeguard.
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Core Ethical Principles
1. Cultural Sensitivity
2. Informed Consent
3. Protection from Harm
4. Confidentiality
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1. Cultural Sensitivity
* Challenges ethnocentrism by valuing diverse ways of knowing.
* Encourages researchers to reflect on their own ethnicity (reflexivity).
* Involves liaison with community leaders/members (e.g., Ngāi Tahu iwi consultation at Otago).
* Interviewers must build rapport and be prepared to respond sensitively to emotional or surprising disclosures.
Example: Interview with ‘Grace’ about alcohol use—interviewer responds empathetically to distress rather than reflecting back neutrally.
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2. Informed Consent
* Two parts:
* Informing: Explain exactly what participation involves.
* Consent:
* Signed consent forms.
* Proxy consent for those unable to consent.
* Passive consent (e.g., non-refusal for children).
* In qualitative research:
* May need to ask permission to record.
* Explain how data (including quotes) will be used.
* Sometimes share interview questions in advance.
Voluntary Consent Challenge:
* Example scenario where participants may feel pressured to hand over phones despite consent being “voluntary.”
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3. Deception and Protection from Harm
* Deception is:
* Allowed only when necessary (e.g., when awareness would invalidate results).
* Never used with children or in qualitative research.
* Debriefing is required to:
* Reveal any deception used.
* Provide sources of help if distress occurred.
* Risks in qualitative research are mostly psychological (distress, embarrassment, reputational damage), not physical.
* Researchers must assess and minimize these risks during design.
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4. Confidentiality
* Protect participant data by:
* Restricting access to researchers and participants.
* Removing identifiers (names, places) from transcripts.
* Common dilemmas:
* Are focus groups confidential internally?
* How long should data be stored?
* Can data be reused?
* What if participants want data deleted or want to be identified?
* Managing quotes that may reveal identities.
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Lecture 10: Qualitative Data Analyses
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Overview
Common qualitative data analysis methods:
* Reflexive Thematic Analysis
* Narrative Analysis
* Grounded Theory
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1. [Reflexive] Thematic Analysis (TA)
What is it?
* A method to analyze qualitative data by organizing it into key features called themes.
What is a theme?
* “A patterned response or meaning within the data set.” (Braun & Clarke, 2006, p. 82)
* Also called categories or dominant discourses in other methods.
Main features of themes:
* Typically recurring across data.
* Themes may overlap.
* Themes are not “neutral” but actively interpreted by the researcher, influenced by their social, cultural, historical, and ideological positions.
Researcher’s role:
* Interpret data, evoke participant voices, tell a story about the data.
Key analytical tasks:
* Compare accounts.
* Understand meanings, definitions, and representations.
* Recognize semantic (explicit) vs. latent (underlying) meanings.
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Braun and Clarke’s 6-step process for Reflexive TA:
1. Familiarise yourself with the data:
* Transcribe, read, re-read, note initial ideas.
2. Generate initial codes:
* Code relevant data extracts; extracts can have multiple codes.
* Move beyond the data’s natural structure to a code collection.
3. Generate initial themes:
* Group codes into themes; not predetermined, use visual tools.
4. Review themes:
* Level 1: Do themes fit coded extracts?
* Level 2: Do themes fit the entire data set?
5. Define and name themes:
* Refine theme specifics and overall narrative.
* Theme names should be concise and descriptive (Braun & Clarke, 2006).
6. Produce the report:
* Use vivid quotes to illustrate themes.
* Include thematic map.
* Connect themes to research questions and literature.
Note:
* Reflexive TA is foundational and widely applicable across qualitative research.
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2. Narrative Analysis
* Focuses on the stories people tell about their lives.
* Research questions focus on sense of self and understanding experiences/events.
* Participants encouraged to elaborate freely.
* Participant accounts are analyzed as whole narratives, not broken into themes.
* Epistemology: Social constructivist (knowledge is constructed through social processes).
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3. Grounded Theory
* Aims to generate theory from the ground up using bottom-up knowledge processes.
* Research starts broadly without exhaustive literature review.
* Data (interviews, focus groups) is collected, coded, and analyzed using memo-writing to build theory.
* Epistemology: Shares features with positivist approaches.
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Lecture 11: One-on-One Interviews in Qualitative Research
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Why Interviews?
* Most common form of systematic social inquiry across social sciences.
* The self interacts with the social world primarily through talking.
* Qualitative research assumes words carry meaningful insights to be taken seriously.
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One-on-One Interviews
* The most common source of qualitative data.
* Smaller sample sizes work well in qualitative research.
* The smallest sample is a case study (n = 1), allowing deep exploration of a single case.
* Most studies have more than one participant to gather varied perspectives.
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Example Interview (Shazia)
* Pay attention to:
* How the interview starts.
* Types of questions asked:
* Closed questions: Suggest limited answers.
* Open-ended questions: Invite detailed, expanded responses.
* How participant uses terms like "friend" and "friendship."
* The time needed to transcribe a full interview.
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Question Types
Open-ended Questions:
* Encourage detailed and expanded answers.
* Example: "Can you tell me how you became friends?"
Closed Questions:
* Suggest limited, often yes/no or brief answers.
* Example: "So you’ve been friends since you were six?"
* Can sometimes disrupt interview flow but useful for checking understanding.
Building on Closed Questions:
* Closed questions can lead into open-ended follow-ups to expand the conversation.
* Example: "Is that more with schoolwork or personal things?"
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How Much Structure?
Research interviews vary in structure:
1. Unstructured Interviews:
* Research topic in mind but very open-ended.
* Ideal for exploratory research and pilot studies.
2. Structured Interviews:
* Mostly closed questions with fixed order.
* Resembles a questionnaire; limited expansion allowed only if pre-planned.
* Useful in quantitative research and diagnostic screenings.
3. Semi-structured Interviews:
* Mainly open-ended questions, with flexibility in question order.
* Interviewer and participant share almost equal roles.
* Expansion and probing encouraged, but focused on the research topic.
* Ideal for focused qualitative research.
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Summary Table: Interview Types
Interview Type
Question Type
Role of Interviewer
Expansion Allowed?
Best For
Unstructured
Open-ended
Researcher-guided
Very open
Exploratory/pilot research
Structured
Closed (fixed order)
Interviewer-controlled
Limited (pre-defined)
Quantitative, screening
Semi-structured
Mainly open-ended
Collaborative
Encouraged
Focused qualitative studies
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Final Notes
* Interviews generate rich verbal data for qualitative analysis.
* Transcription of interviews is time-consuming but essential for detailed analysis.
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Lecture 12: Focus Groups in Qualitative Research
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What is a Focus Group?
* An informal group discussion involving 2 or more selected individuals on specific topics.
* Applies interview techniques to a group setting.
* Definition (Beck, Trombetta & Share, 1986): “A focus group is an informal discussion among selected individuals about specific topics.”
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Why Use Focus Groups?
1. Interaction Benefits:
* Collective sense-making through group discussion.
* Co-creation of ideas emerges in interaction.
* Communication itself can be a key research focus.
2. Working with Unfamiliar Groups:
* Helps explore power dynamics within groups.
3. Stimulating New Topics:
* Participant interactions can generate new discussion points not anticipated by researchers.
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Focus Group vs. One-on-One Interviews
* One-on-One.
* More private, individual perspectives.
* Participants may share more personal or detailed answers.
* Focus Group:
* Group dynamics influence responses.
* Participants may build on or challenge each other’s views.
* Can reflect social interactions and collective meaning-making.
* Responses may be influenced by dominant participants or peer pressure.
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Considerations as a Participant
* In a focus group with friends, responses might be influenced by social bonds, with some speaking first and others following.
* In groups with strangers, dynamics may be more cautious or formal.
* Dominant voices may steer discussion; shy participants may hold back.
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Challenges of Focus Groups
* Participants may challenge or dominate conversations.
* Researcher influence on discussion can be diluted.
* Too many new discussion topics may arise, making focus difficult.
* Some participants may be shy or withdrawn.
* Conversations can feel both natural and unnatural simultaneously.
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Dos and Don’ts of Focus Groups (Sullivan & Forrester, 2019)
Do:
* Prepare clear questions and structure.
* Use everyday language to explain issues to participants.
* Use and test audio recording equipment beforehand.
* Encourage discussion flexibly using different techniques.
* Pay attention to group dynamics.
* Stay adaptable during the session.
Don’t:
* Use psychological jargon participants won’t understand.
* Recruit uninterested participants.
* Stick too rigidly to a schedule if it hampers discussion.
* Skip piloting your questions.
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Methodological Reflection: Researching Student Drinking
* Different methods can be used: diary studies, experiments, brain imaging, one-on-one interviews, or focus groups.
* Each has strengths and weaknesses depending on ontology, epistemology, ethics, design, and expected outcomes.
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Pros and Cons of Focus Groups
Pros:
* Existing relationships encourage sharing of shared experiences.
* Groups of strangers can provide fresh explanations.
* Skilled interviewer can balance participation.
* Reflects natural group interactions.
Cons:
* Pre-existing friendships may lead to assumptions or tensions.
* Strangers may feel uncomfortable.
* Risk of dominant participants overshadowing others.
* Large groups can be hard to manage.
* Environment may not always feel natural; interruptions and talking over each other can occur.
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Lecture 13: Photo-elicitation in Qualitative Research
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What is Photo-elicitation?
* A research method using photographs during interviews or focus groups as prompts.
* Coined by Harper (2002): photos evoke deeper elements of human consciousness than words alone.
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Strengths of Photo-elicitation
* Acts as a memory aid for participants.
* Can elicit more detailed information than verbal questions alone.
* Evokes different kinds of information (emotional, sensory, symbolic).
* Helps build rapport between researcher and participant.
* Can be empowering for participants to share their perspectives visually.
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Key Points in Photo-elicitation Interviews
* Photos serve as interview prompts guiding discussion.
* Involves self-presentation — taking, presenting, and discussing photos shapes the interview focus.
* Enables questions and insights not accessible with words alone.
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Types of Photo-elicitation (Levels of Participant Involvement)
1. Researcher-led:
* Researcher selects and provides photos related to research questions.
* Photos mainly aid memory.
* Analysis usually limited.
2. Participant-led:
* Participants take or choose their own photos.
* Photos are integrated into interviews and data analysis.
* Broader scope for meaning and interpretation of images.
* Participants decide which photos to include and discuss.
3. Participatory Photo-elicitation (Photovoice):
* Participants actively lead the whole research process: question formulation, data gathering, analysis, and write-up.
* Term coined by Wang and Burris (1997).
* High participant empowerment and engagement.
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Where Photos Are Used in Research
1. Interviews only
* Photos used only during interviews.
* Researcher- or participant-led.
* Ethical considerations limit wider use.
2. Interviews + Academic Publications
* Photos included in publications.
* Ethical concerns may require anonymity: no faces, blurred images, blocked eyes.
3. Interviews + Publications + Public Exhibitions
* Photos displayed publicly (exhibitions, websites).
* Typical for participatory photovoice projects.
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Ethical Considerations
* How intrusive can participant-taken photos become?
* Risks of inappropriate behaviour during photo-taking.
* Consent from non-participants accidentally photographed.
* How would participants feel about being asked to appear in study photos?
* What information should be provided to participants about photo use and consent?
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Lecture 14: Media and Media-Elicitation in Social Research
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What is Media?
* Media = indirect information sharing (vs. direct face-to-face talking).
* Mass media uses technologies like printing press, photography, radio, TV, computers, smartphones.
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Media Circulate Patterns of Meaning (Hodgetts & Chamberlain, 2006)
* Media is central to modern life.
* It plays a big role in constructing shared understandings of the world.
* Fictional media can reveal dominant and subversive ideas in society.
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Media Circulate Patterns of Language
* Media discourses are patterns of language that represent and circulate versions of reality.
* They show shared ideas about ‘how things are’ socially and culturally (the “here and now”).
* Useful for studying how people make sense of their world, including psychological concepts.
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Is Media-elicitation like Photo-elicitation?
* Yes! Both use media as prompts in qualitative research.
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Three Types of Media-Elicitation Studies
1. Researcher-led Media-elicitation
* Researchers pre-select media stimuli (real or mock media).
* Used as interview or focus group prompts.
2. Participant-led Media-elicitation
* Participants gather or spontaneously mention media relevant to the topic.
* Can be done in real-time, including social media use.
3. Primary Media Analysis
* No participants; only media texts are analyzed.
* Media is studied as cultural artifacts that shape shared understandings.
* Fictional media especially reveals dominant/subversive social ideas.
* Highly contextualized research method.
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Example: Using Inside Out in Media-Elicitation
* Researcher-led: Show Inside Out to participants, then conduct interviews/focus groups based on it.
* Participant-led: Ask participants to bring/share media on topics like gender and emotions.
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Discussion Questions to Consider
* Which media sources did you interact with recently? (e.g., newspaper, online news, Facebook, Instagram, TikTok, Netflix, Disney movies, broadcast TV)
* Do you trust all media sources equally? Why or why not?
* Which media source do you engage with the most?
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Lecture 15: Research Design & Mixed Methods Research
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What is Research Design?
A strategic plan of procedures in a study to reach valid conclusions, focusing on:
* Participant selection & assignment
* Data collection
* Data analysis
Research designs include: experiments, quasi-experiments, observational, longitudinal, surveys, focus groups, and other non-experimental methods.
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Types of Methods
* Qualitative methods: interviews, themes & discourses, photo-elicitation, relativism, non-numeric data, media analysis, focus groups
* Quantitative methods: structured, surveys, repeated measures, realism, numeric data, A/B testing, central limit theorey
* Mixed Methods: integration of qualitative and quantitative approaches in a single study (middle of the Venn diagram)
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Mixed Methods Definition
Research combining collection, analysis, and integration of both qualitative and quantitative data to draw inferences in one study.
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Purpose of Research
* To generate valuable knowledge with practical impact
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Strengths of Mixed Methods
* Broader, deeper understanding
* Complete picture of research problem
* Allows qualitative insights to inform quantitative study (and vice versa)
* No hierarchy assuming one method is better
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Limitations of Mixed Methods
* Requires training in both qualitative and quantitative approaches
* More costly in time and money
* Some audiences may be resistant to mixed methods studies
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Ontology & Epistemology in Mixed Methods
* Potential clash between different worldviews (ontologies) and knowledge theories (epistemologies)
* Mixed methods typically grounded in Pragmatism — practical approach valuing what works best to answer the research question
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What Drives Mixed Methods Research?
* Influenced by:
* Positivism (quantitative)
* Social Constructivism (qualitative)
* Pragmatism (practical combination)
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Mixed Methods Example: Motivations & Self-Esteem in University Students
Research Question:
* How do different motivations to study impact students’ self-esteem?
* Does a sense of purpose affect self-esteem?
Quantitative measures:
* General Self-Efficacy Scale (Schwarzer & Jerusalem, 1995)
* Number of motivations to study (Stephens et al., 2012)
Qualitative questions:
* Describe why two chosen motivations are important to you
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Mixed Methods Research Design (Creswell et al., 2003) — Four Key Factors
1. Implementation
* Order of data collection:
* Qualitative before quantitative (sequential)
* Quantitative before qualitative (sequential)
* Both at the same time (concurrent)
2. Priority
* Which data type is prioritized: qualitative, quantitative, or equal?
* This affects analysis and interpretation
3. Integration
* When qualitative and quantitative data are integrated:
* Research question formulation
* Data collection
* Data analysis
* Interpretation (most common)
4. Theoretical Perspective
* Research’s ultimate goal (explicit or implicit) to advocate for change or transformation
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Mixed Methods Typologies (Creswell et al., 2003)
1. Sequential Explanatory
* Quantitative first, then qualitative
* Equal priority
* Quant informs Qual
2. Sequential Exploratory
* Qualitative first, then quantitative
* Equal priority
* Qual informs Quant
3. Concurrent Triangulation
* Both qualitative and quantitative collected simultaneously
* Equal priority
4. Concurrent Nested
* Both collected simultaneously
* One method dominant, other nested as smaller component
Lecture 16 Summary
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Mixed Methods Research: “The Third Methodological Movement”
Strengths:
* Broader, deeper understanding & corroboration
* Provides a fuller picture of research problems
* Allows qualitative data to enrich quantitative studies and vice versa
* No assumption that one method is superior
Limitations:
* Requires training in both methods
* More costly in time and money
* Some audiences resist mixed methods
Ontology & Epistemology:
* Different assumptions about reality can clash
* Mixed methods adopt Pragmatism — focusing on practical outcomes
Creswell et al. (2003) Four Key Factors:
1. Implementation (order of data collection)
2. Priority (which method is emphasized)
3. Integration (when methods are combined)
4. Theoretical Perspective (ultimate research goal)
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Basic vs. Applied Research
* Basic Research: seeks fundamental understanding/theory advancement without direct application (“pure” or “blue skies” research)
* Applied Research: targets solving real-world problems with utility focus
* Not a strict dichotomy — they complement each other
Research Quadrants:
* Use-inspired basic research (Pasteur’s quadrant): fundamental knowledge + practical use
* Pure basic research (Bohr’s quadrant): fundamental knowledge without immediate application
* Pure applied research (Edison’s quadrant): practical use without searching for fundamental knowledge
* Tinkering: no fundamental search or immediate application
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Cross-Sectional Research
* Measures data at one point in time — a “snapshot”
Strengths: quick, inexpensive, hypothesis generating, simple
Weaknesses: cannot establish causality, no temporal data, hard to study rare phenomena
Causality Requires:
1. Cause and effect related
2. Cause precedes effect in time
3. Alternatives ruled out
4. Articulated mechanism explained
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Longitudinal Research
* Studies same variables/participants over time (sometimes years)
Strengths: allows causal inference, clear temporal relationships
Weaknesses: participant drop-out, contamination by other variables, “temporal erosion”
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Dunedin Multidisciplinary Health and Development Study (“The Dunedin Study”)
* World-leading longitudinal research from New Zealand
* Video prompt: Observe representation of Māori and analyze language used
* Media elicitation methods used in analysis (interviews, focus groups)
* Example reference: Yan et al. (2021) study on media racial stereotypes
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Psychological Interventions: The Three T’s (Cohen et al., 2017)
* Targeted: given to those who need it
* Tailored: fits motivation & situation
* Timely: delivered at the right time
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Social Psychology Overview
* Scientific study of how people are influenced by others (actual, imagined, or symbolic presence)
* Focus: Individuals and groups (ingroup/outgroup)
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Early Social Psychology
* Focused on interpersonal behaviour and stimulus-response models
* Studied discrimination, prejudice, and bias
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Kurt Lewin’s Holistic Approach
* Behaviour equation: B = f(P, E) (Behaviour = function of Person × Environment)
* Emphasizes interaction between personal history/personality and social/physical environment
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Minimal Group Paradigm (MGP) (Tajfel & Turner, 1979)
* Experimental setup creating meaningless groups to study discrimination
* Participants allocated into groups with no real context and asked to rate abstract paintings
* Findings:
* Ingroup favouritism common
* Participants often maximize differences between groups, even at own cost
* Demonstrates intergroup bias can arise without meaningful group conflict
* Shows social categorization strongly influences behaviour
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Critiques & Limitations of MGP
* Laboratory setting lacks real-world ecological validity
* Groups in real life have meaning, unlike arbitrary MGP groups
* Allocation tasks don’t commonly happen in daily life
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Social Norms
* Unwritten ‘rules’ governing social behaviour
* Developed through regular communication within groups
* Provide shared reality and reduce uncertainty
* Recognized even if not agreed with
* Exist independently of social isolation
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Lecture 17 Summary
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Social Norms
* Definition: Unwritten rules guiding social behaviour, often shaped within groups who communicate regularly.
* No set number needed to form a norm; social isolation doesn’t prevent awareness of norms.
* Norms help reduce uncertainty by providing a shared social reality.
* People recognize norms even if they don’t agree with them.
* Not all social behaviours are explained solely by social norms.
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Two Types of Social Norms
1. Descriptive Norms: What people actually do; typical behaviours in a context.
2. Injunctive Norms (Prescriptive Norms): What people should do; socially approved behaviours regardless of actual behaviour.
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Dynamic Norms
* Awareness that social norms can change over time.
* Highlighting change can make behaviour change seem more possible and motivate action.
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Data Collection in Social Psychology
Three main methods:
1. Self-Report (surveys, questionnaires)
2. Observation (direct behaviour monitoring)
3. Archival Data (existing records like newspapers, medical files, diaries, sports data)
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Self-Report & Likert Scales
* Likert scale: Measures attitudes on a range, e.g., Strongly Disagree → Strongly Agree (usually 5 points).
* Assumes equal psychological distance between options.
* Advantages: sensitive, flexible, easy to analyze compared to simple agree/disagree or 1–100 scales
* Most studies use an odd number of options to allow a neutral midpoint.
Challenges with Likert scales:
1. Difficulty understanding/responding due to language or education
2. Out-of-range responses (e.g., writing “yes” when options are numeric)
3. Varied response patterns due to cultural differences and self-enhancement bias (tendency to emphasize positive self-views)
4. Scale reliability (do all items measure the same construct?) — tested by Cronbach’s Alpha
5. Construct validity (are we measuring what we intend to measure?)
* Children under ~6 struggle with Likert formats; words describing frequency work better than visuals.
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Self-Report Weaknesses
* Poor question design
* Relies on memory
* Social desirability bias (respondents answer what they think is acceptable)
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Observational Research
* Researchers watch and record behaviour directly.
* Advantages: no memory reliance, allows for interrater reliability checks.
* Participants might behave differently if aware they’re observed.
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Archival Data
* Uses existing records (media, medical, diaries, sports, prior research data).
* Useful for large-scale trend analysis.
* Context of data can shift over time, influencing interpretation.
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Natural Experiments
* Occur when real-world events create conditions similar to experiments without deliberate manipulation.
* Quasi-experimental design with non-random groups.
* Strengths: real-world validity, varied data types, large-scale comparison.
* Weaknesses: confounding variables, lack of proper control groups.
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Lecture 18 Summary
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What is Culture?
* No single definition, but generally:
Culture = knowledge and behaviours characterizing a group (Heyes, 2020).
* Expanded to include shared ideas, symbols, meanings, language, norms, values, and information.
* Culture is a system; psychology is deeply organized by culture.
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1. Culture as a Social System
* Culture is fluid, with no fixed rulebook.
* It’s an advanced form of social interaction.
* Culture exists both inside people’s minds and outside, in patterns of ideas, practices, institutions, and artifacts (Markus & Kitayama, 2010).
* Culture and psyche shape each other in a mutual constitution (Heine, 2020).
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Layers of Culture
* Psychological structures/processes: Self, emotions, cognition.
* Daily experiences: Home, school, workplace interactions.
* Practices and institutions: Language, education, media.
* Cultural ideas: Ecological, religious, economic concepts.
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2. Psychology Organized by Culture
* Repeated cultural exposure shapes cognition, motivations, and behaviours.
* People actively coordinate their behaviours with cultural norms and public meanings.
* Culture doesn’t rigidly determine behaviour but makes some behaviours and ways of thinking more likely.
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Studying Culture
* Culture is a process of knowing and interpreting the world.
* Culture and mind are inseparable.
* Multiple interdependent factors influence psychological processes.
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Cross-Cultural Psychology
* Dominant cultural practices produce measurable psychological differences (Greenfield, 2000).
* Provides comparative methods to study different cultures.
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Etic vs. Emic Research
* Etic: External perspective; imposes classification on what’s studied; useful for cross-cultural comparison.
* Emic: Insider perspective; discovers classification from within culture; focuses on indigenous constructs.
* Phonetic vs. Phonemic analogy:
* Phonetic = universal sounds (etic)
* Phonemic = language-specific sounds (emic)
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Research From Within (Emic Research)
* No comparison group, research done by someone inside the culture.
* Involves positionality and reflexivity (researcher awareness of their own influence).
* Challenges positivist ideas of bias—recognizes value in insider perspectives.
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Insider Researcher Strengths (Ross, 2017)
* Easier access and rapport with participants.
* More nuanced data and deeper understanding of norms, values, and socio-political context.
* Avoids cultural misunderstandings common in outsider research.
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Insider Researcher Challenges (Ross, 2017)
* Assumptions about shared understanding can limit explorations
* Pre-existing relationships might affect openness in conversation.
* Navigating power dynamics is complex.
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Key Takeaway
* The mind and culture are intertwined:
“One cannot be a self by one’s self.”
Cultural meanings shape mental processes and vice versa (Heine, 2020).
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Lecture 19 Summary: Kava and Its Cultural, Social, Economic, and Health Aspects
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The Legend and Cultural Significance
* Ancient Tongan tragedy highlighting the importance of social relations between the king and other social classes.
* The couple Fevanaga and Fefafa sacrifice their daughter Kava to fulfill reciprocal social obligations to the King of Tonga.
* Bitterness of kava and sweetness of sugarcane symbolize their great sacrifice.
* Their sacrifice led to the creation of the kava ceremony, a key social institution in Tonga (Mahina et al., 2009).
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General Information About Kava
* Botanical name: Piper methysticum.
* Indigenous only to the Pacific region.
* Used as root, powdered form, and beverage.
* Central cultural element across Pacific societies.
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Social Factors of Kava Use
* Promotes sense of belonging and socialization.
* Helps establish and nurture relationships.
* Supports intergenerational harmony through Talanoa (open dialogue).
* Provides financial support for individuals, families, and villages.
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Demand and Supply Issues (Outside Pacific)
* Rising demand outside Pacific nations combined with limited supply causes price increases.
* Kava pills and products are emerging.
* Controversies such as the Australian Government saga regarding regulation and safety.
* Issues integrating other drugs into kava products.
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Economic & Environmental Impacts
* Natural disasters (cyclones, floods, droughts) threaten kava crops, leading to supply scarcity and price hikes.
* Investment in climate-resilient farming and disaster preparedness is critical for stability.
* Financial aid, subsidies, and improved infrastructure help farmers adopt better practices and increase quality.
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Cultural Factors Related to Kava Use
* Different experiences “on the ground” (traditional settings) versus bars or casual settings.
* Strong connection to Pacific homelands and heritage.
* Use of language (native tongues) encouraged during kava ceremonies.
* Reinforces status and rank within social hierarchy.
* Celebrates arts like oratory, storytelling (witting), and music.
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Health Benefits of Kava
* Reduces anxiety and depression.
* Protects neurons from damage.
* Relieves pain and relaxes muscles.
* Alleviates insomnia, migraines, and menstrual problems.
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Kava Use in Digital Spaces
* Enables intergenerational connection with family and friends.
* Helps maintain cultural safety and identity remotely.
* Encourages creative and innovative ways to keep the kava tradition alive online.
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Lecture 20 Summary
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Heine’s Categories of Psychological Universals
Category
Description
Example
Non-Universal
Psychological process does not exist in all cultures; cultural invention.
Abacus reasoning
Existential Universal
Process exists in all cultures but differs in how or frequency of use; qualitatively distinct.
Success/failure as motivation (White & Lehman, 2005)
Functional Universal
Process present and used similarly across cultures but accessibility varies among cultures.
Social belonging’s impact on resilience
Accessibility Universal
Process exists everywhere, solves the same problem, accessible in the same way and frequency.
Mere exposure effect (positive affect to familiar objects)
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Decision Flow for Categorizing Psychological Processes
1. Is the process cognitively available in all cultures?
* No → Non-universal
* Yes → Go to next step
2. Is the use of the process the same?
* No → Existential universal
* Yes → Go to next step
3. Is the process accessible in the same way and frequency?
* No → Functional universal
* Yes → Accessibility universal
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Example Study: Abacus Training on Chinese Children (Stigler, 1984)
* Chinese children showed specific error patterns ("confusions of five") in mental calculations after abacus training, indicating culture-specific cognitive strategies (non-universal process).
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How Are Cultures Sustained?
* Cumulative Cultural Evolution: Beneficial cultural modifications build up over time.
* Imitation vs. Emulation: Two key mechanisms in cultural learning.
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Mechanisms for Cultural Evolution
Type
Description
Imitative Learning
Learner precisely copies the model’s behavior, intentions, and goals.
Emulative Learning
Learner focuses on environmental results, tries to figure out “how it works” without copying intentions.
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Nagell et al. (1993) Study
* Human children and chimps observed two methods to use a rake-like tool for food:
* Teeth up (more effective)
* Teeth down (less effective)
* Results:
* Chimps emulated — tended to choose the effective method.
* Children imitated — even those shown the less effective method continued to copy it exactly.
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Herrmann et al. (2007) Study
* Compared 2.5-year-old children, chimpanzees, and orangutans on:
1. Physical problem-solving (finding reward, reward after rotation)
2. Social problem-solving (understanding pointing, imitation)
* Children showed stronger social problem-solving and imitation skills compared to apes.
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Cumulative Cultural Evolution in Action
* Example: The hammer’s evolution from early tools (archaeological records) to modern forms shows how cultural knowledge accumulates and improves over time.
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Lecture 21 Summary
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Context & Indigenous Psychology
* Key idea: Context shapes how knowledge and skills work—e.g., a city dweller depends on a desert nomad’s expertise in the desert (Ibn Khaldun).
* Indigenous psychology: The scientific study of human behavior/mind native to a particular culture, designed for that group’s context and needs (Kim & Berry, Yang, Ho).
* Five core elements:
1. Knowledge systems native to the cultural group
2. Knowledge systems as research basis
3. Bottom-up, insider-led research
4. Designed specifically for its people
5. Methodologically diverse
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Emergence of Indigenous Psychologies
* Reaction against Western (especially American) psychology’s unjustified universal claims
* American psychology itself is indigenous to its culture.
* Goal: awaken identity, national consciousness, and culturally relevant research (Kim, Park & Park, 2006).
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Talanoa Research Method (Tualaulelei & McFall-McCaffery, 2019)
* A Pacific qualitative tool based on open, informal conversation (talanoa).
* Emphasizes sharing stories, thoughts, feelings.
* Widely used in Pacific research, culturally appropriate qualitative method.
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Psychological Universals (Norenzayan & Heine, 2005)
* Non-Universal: Process doesn’t exist in all cultures (cultural invention).
* Existential Universal: Exists in all cultures but used differently or with different frequency.
* Functional Universal: Same across cultures but less accessible in some.
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Māori Interconnected Identity
* “We exist only because we’re connected” – Judge Joe Williams.
* Emphasis on relational, collective selfhood.
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Kaupapa Māori Research (Walker, Eketone & Gibbs, 2006)
* Philosophy guiding Māori research ensuring Māori protocols, values, and control are respected.
* Emic (insider) approach that challenges dominant (Western) research models.
* Avoids comparing Māori to others with generalized measures.
* Core principles:
1. Tino rangatiratanga (self-determination)
2. Social justice
3. Māori worldview
4. Te reo Māori (language)
5. Whānau (family)
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Rua, Hodgetts & Stolte (2017) – Māori Men’s Indigenous Psychological Perspective
* Explored Māori men’s selfhood shaped by cultural relationships, traditions, places.
* Method: in-depth interviews + ethnographic observation emphasizing joint interpretation.
* Four Primary Themes:
1. Tūrangawaewae – traditional place to stand (marae as cultural repository)
2. Whānau – family support system, interconnected self
3. Kaumātua – elders as wisdom holders and cultural authority
4. Whakapapa and Whanaungatanga – relational connectedness experienced materially
* Portrayed Māori men as caring, nurturing, and culturally defined in their interconnected self.
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Evolving Kaupapa Māori Research (Mahuika, 2008)
* Recognizes Māori have been influenced by Western thought through colonization.
* Kaupapa Māori does not reject Pākehā knowledge but empowers Māori to define their own identities and futures.
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He Awa Whiria – Braided Rivers Model (Angus & Sonja Macfarlane, 2019)
* Metaphor for blending Indigenous and Western knowledge streams to create stronger, more powerful knowledge than either alone.
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Today’s Outcomes
* Understanding of Indigenous Psychology
* Kaupapa Māori Psychology principles and research examples
* Introduction to the He Awa Whiria model
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Practice Exam Questions
When conducting a focus group, it us important that a researcher:
1. Has a list of prepared discussion topics and questions, but is aware that they may change based on the conversations that take place
2. Focuses most of the questions towards participants who are talking the most
3. Begins every discussion topic, but takes no further action in directing where the discussions travel
4. Uses a stopwatch to time how much every participant is talking to ensure parity across the group.
Which of the following statements about mixed methods research is FALSE?
1. Concurrent and sequential designs are two important factors to consider when conducting mixed methods research
2. The purpose of the research should be considered before implementing mixed methods research
3. Mixed methods research can mitigate limitations of quantitative and qualitative research
4. A priority must be given to either quantitative or qualitative research
An important feature of indigenous psychology is that it is:
1. Driven primarily by top-down theories of psychology
2. Methodologicaly diverse
3. Designed for the benefit of researchers
4. Limited in its ability to draw meaningful conclusions
Label the different parts of a neuron?
What are the two major types if tissues in the brain?
What are each of them predominantly made up of?
What do each of them do?
What can damage to each of them cause?
What is MRI signal based on?
Which individuals can never go near an MRI scanner
What is the spatial resolution of the different types of MRI scans discussed in lectures?
What types of conditions can structural and diffusion MRI scans be used to help diagnose clinically.
What are they key differences between structural, diffusion and functional MRI SCANS?
How does water diffuse in an axon, and why does this help us to understand what is happenind in underlying white matter tracts using diffusion imaging.
Why do we need to take lots of fast MRI scans for both diffusion and functional imaging, and what does this make them susceptible to?
What does the BOLD signal measure, and what causes it?
Describe the haemodynamic response function.
What is the temporal resolution of functional MRI scans? i.e. How long does it take to measure one functional picture of the brain?
If we wanted to use functional MRI to analyse the brain’s response to a fearful face on a screen, what might be a helpful control condition if we want to isolate the response to fear alone?
Why is it important that we have rest periods in a functional MRI task?
Can MRI scans give you a definitive answer for whether one group is different from another group, or is the answer based on deciding whether the groups are significantly different from each other, based on statistical tests?
What can a correlation analysis tell you? What can it not tell you?
What type of statistical analysis do we use to analyse the functional MRI scans recorded when a participant was completing a task?
What does this type of analysis help us to find in the brain?
What are the key limitations for each of structural, diffusion and functional MRI?
What are some complementary techniques for structural, diffusion and functional MRI?
What signal are they measuring?
How are these different in their spatial and temporal resolution compared to structural MRI?
Are there any other key differences or limitations?