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๐ PSYC1450 โ FINAL EXAM MASTER
CONCEPTUAL GUIDE (LECTURES 1โ6)
Every concept you must know, with the exact depth expected on the exam.
SECTION 1 โ FOUNDATIONS OF
PSYCHOLOGICAL SCIENCE
(Primarily from Lecture 1 + Review Sheet)
๐ 1.1 The Computer Metaphor of the Mind
Psychology used to model the mind as a computer-like information processor.
The metaphor includes:
โ InputโProcessingโOutput โ Mental processes transform sensory input through a series of steps โ Each step is sequential, rule-based, and structured This metaphor motivated early cognitive psychology: โ Reaction-time tasks โ Memory load tasks โ Automatic vs controlled processes
Why this matters:
The metaphor shaped early experimental logic :
โ If you manipulate inputs (stimuli), you can observe processing differences in outputs (behavior, RT, accuracy). โ You infer mental processes indirectly (because you canโt see them directly).
Stroop Task Example
(Stroop is a classic illustration of the computer metaphor) Goal: Show that automatic processes interfere with controlled ones. You see: RED but printed in blue ink. You must name the ink color (โblueโ), but your brain automatically reads the word โRED.โ
What Stroop shows:
โ Some cognitive processes are automatic (reading) โ They compete with control-demanding processes (color naming) โ Reaction time differences reveal interference โ evidence of dual-process theories The Stroop task is not about color; itโs about how mental processes reveal themselves via timing , which is fundamental to early experimental psychology.
SECTION 2 โ HOW TO ASK A GOOD
RESEARCH QUESTION
(Lectures 1โ3; Review Sheet)
๐ 2.1 Diffuse vs Specific Question (Q in
QuALMRI)
- Manipulated (true IV)
- Measured (in quasi experiments)
Levels:
Each IV has levels (conditions). Example: IV = Social environment Levels = Alone, Group
๐ 3.2 Dependent Variables (DVs)
What you measure. DVs must be: โ Reliable โ Valid โ Sensitive enough to detect differences Examples: โ Stress rating โ Reaction time โ Memory accuracy โ Emotion rating
Operational Definition
The exact measurable version of your concept. Example: โ โStressโ operationalized as a 1โ100 self-report rating โ โImplicit biasโ operationalized as IAT reaction time differences
โ โAttentionโ operationalized as response latency in a vigilance task Knowing how to evaluate operationalizations is a major exam skill.
๐ 3.3 The Logic of Experiments
(Core of Lecture 3) Psychologists do NOT just try things randomly โ they use deductive logic :
Deductive Logic Template:
โIf the hypothesis is true, then manipulating IV should change DV in a predictable way, relative to alternative explanations .โ Your professor cares that you know: โ Studies are not just methods โ They are logical arguments with predicted patterns For example: Hypothesis: Physical warmth activates warmth-related concepts. Logic: If this is true โ warm condition > cold condition on warmth traits. If halo effect โ warm condition > cold condition on ALL traits. If null โ no difference. You must show this ability on the exam โ it is directly tested.
SECTION 4 โ EXPERIMENTAL DESIGN
(B vs W designs + Confounds)
(Lecture 5 & Review Sheet)
๐ 4.3 ORDER EFFECTS
Happen in within-subject designs. Types: โ Practice effects โ Fatigue โ Carryover effects (previous trial influences next trial)
๐ 4.4 Counterbalancing
(Lecture 5 โ very testable) Purpose: control order effects.
Two types:
1. Full Counterbalancing Every possible order appears. Example for 2 conditions: AโB and BโA 2. Latin Square (Partial Counterbalancing) Every condition appears once in each position, but not all orders are used.
๐ 4.5 Confounds
A third variable that differs systematically between conditions. A confound must:
- Vary with the IV
- Affect the DV
Examples: โ Room temperature differs between conditions โ Experimenter uses different tones of voice โ A specific gender is overrepresented in one condition Confounds destroy causal claims.
๐ 4.6 Noise
A nuisance variable that adds variability but does not systematically differ across conditions. Noise โ confound. Examples: โ Some participants didnโt sleep well โ Some people are always anxious โ Random mood fluctuations Noise weakens power but does not bias results.
SECTION 5 โ VALIDITY
(Lecture 4 โ deeply important)
๐ 5.1 Construct Validity
Does your operationalization truly capture the construct? A DV lacks construct validity if:
๐ 5.5 Discriminant Validity
Does it not correlate with unrelated constructs? Example: Depression scale should not correlate strongly with IQ.
SECTION 6 โ RELIABILITY
(Lecture 5 & 6; Review Sheet) Reliability = consistency of measurement.
Types:
6.1 TestโRetest Reliability
If you give the same test twice, do you get the same score? Threats: โ Learning โ Mood changes โ Memory
6.2 Inter-Rater Reliability
When subjective judgment is involved, do raters agree? Measured with: โ Correlation โ Kappa statistic
6.3 Internal Consistency
Do items on a scale measure the same underlying concept?
Three forms:
1. Split-Half Reliability Divide the test โ are halves correlated? 2. Cronbachโs Alpha (ฮฑ) Most common statistic for internal consistency. Rules of thumb: โ ฮฑ > .70 = acceptable โ ฮฑ > .80 = good โ ฮฑ > .90 = excellent (but may be too redundant) 3. ItemโTotal Correlation Does each item correlate with the total score? Items with low correlations may need to be removed.
SECTION 7 โ DEMAND
CHARACTERISTICS & BLINDING
(Review Sheet)
๐ 7.1 Demand Characteristics
Participants pick up on cues about what the experimenter wants.
๐ 8.1 One-Sample t-test
Compare sample mean to a known value.
๐ 8.2 Independent-Samples t-test
Compare two groups (between-subjects). Example: Warm vs cold condition.
๐ 8.3 Paired-Samples t-test
Compare two conditions within same participants. Example: Before vs after mood induction.
What you must know:
โ Which test matches which design โ t-value interpretation: larger |t| โ stronger evidence โ p-value meaning (probability results occur by chance under null) โ Report in APA-like sentence: โParticipants rated the warm target as significantly warmer than the cold target, t(38)=2.45, p<.05.โ
SECTION 9 โ MAIN EFFECTS,
INTERACTIONS, & GRAPH
INTERPRETATION
(You already got the giant detailed lesson โ this summarizes what you must know.)
Main Effect
IV has an overall effect on DV.
How to see it:
โ Look at marginal means โ Compare averages
Interaction
The effect of IV1 depends on level of IV2.
How to see it:
โ Check if differences differ โ Check if lines on graph are non-parallel
Graphing Rules
โ X-axis = one IV โ Different bar colors = levels of second IV โ Large gap between bars = strong effect
