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Psychology Research method! > Key Words > Flashcards

Key Words Flashcards

(61 cards)

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1
Q

Null Hypothesis

A

Assumes no effect or difference. Example: “There is no difference between groups.”

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2
Q

Alternative Hypothesis

A

Predicts an effect or difference. Opposes the null hypothesis.

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3
Q

One-tailed Hypothesis

A

Predicts the direction of the effect (e.g., Group A will score higher than Group B).

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4
Q

Two-tailed Hypothesis:

A

Predicts a difference but not the direction (e.g., There is a difference between Group A and Group B).

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5
Q

Target Population

A

The entire group a researcher is interested in studying.

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6
Q

Snowball Sampling

A

Participants recruit other participants, useful for hard-to-reach groups.

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7
Q

Opportunity Sampling

A

Uses people who are readily available (e.g., people nearby).

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8
Q

Random Sampling

A

Everyone in the target population has an equal chance of being chosen.

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9
Q

Self-Selecting Sampling

A

Participants volunteer, often through ads or online calls.

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10
Q

Repeated Measures Design

A

The same participants take part in all conditions of the experiment.

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11
Q

Independent Measures

A

Different participants are used in each condition.

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12
Q

Matched Groups Design:

A

Different participants in each condition, but matched on important variables (e.g., age, IQ).

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13
Q

IV (Independent Variable)

A

The variable the researcher manipulates.

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14
Q

DV (Dependent Variable

A

The outcome or response measured in the experiment.

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15
Q

Control of Extraneous Variables:

A

Techniques used to minimize variables other than the IV that could affect the DV (e.g., randomization, counterbalancing).

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16
Q

Laboratory experiments

A

Conducted in a controlled environment. High control, but may lack realism.

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17
Q

Field Experiments

A

Done in a natural setting. More realistic but less control.

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18
Q

Quasi Experiments

A

IV is not manipulated by the researcher (e.g., gender, age groups); participants already belong to groups.

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19
Q

Structured Observation

A

Uses a set checklist or coding system to record behaviours.

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20
Q

Unstructured Observation

A

Records all behaviours freely without a fixed system.

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21
Q

Covert Observation

A

Participants don’t know they’re being observed.

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22
Q

Overt observation

A

Participants know they’re being observed.

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23
Q

Participant Observation

A

Researcher joins in with the group being studied.

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24
Q

Non - Participant observation

A

Non-Participant Observation: Researcher observes from a distance.

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25
Naturalistic Observation
Takes place in a natural setting without interference.
26
Controlled Observation
Done in a structured or lab setting with some control over variables.
27
Raw Data Tables:
Initial collected data in a basic table format.
28
Standard Form
A way to express very large or very small numbers (e.g., 3.2 × 10⁵).
29
Decimal Form:
Numbers written using base-10 decimal notation (e.g., 0.75).
30
Rounding:
Adjusting a number to fewer decimal places or nearest whole number.
31
Significant Figures (Sig. Figs)
Digits that carry meaning/precision (e.g., 2.34 has 3 sig. figs).
32
Estimations from Data:
Making quick, reasonable guesses based on patterns or averages.
33
Quantitative Data
Numerical, measurable data (e.g., scores, time).
34
Qualitative Data:
Descriptive data (e.g., opinions, themes).
35
. Primary Data:
Collected first-hand by the researcher.
36
Secondary Data:
Pre-existing data gathered by someone else.
37
Mean
Definition: Average of a set of numbers. How to calculate: Add all values, divide by number of values.
38
Median
Definition: Middle score in an ordered list. Note: If even number of scores, average the two middle ones.
39
Mode
Definition: Most frequent score in a dataset. Tip: Can be bimodal or multimodal.
40
Range
Definition: Difference between the highest and lowest values. Formula: Highest - Lowest.
41
Variance
Definition: Average squared deviation from the mean. Use: Indicates spread of data.
42
Standard Deviation (SD)
Definition: Square root of variance. Use: Shows how tightly values cluster around the mean.
43
Ratio
Definition: Comparison of two quantities. Example: 3:1 means three of one thing for every one of another.
44
Percentages
Definition: Proportion out of 100. Use: Useful for comparing different-sized groups.
45
Fractions
Definition: Part of a whole, expressed as one number over another. Example: ¾ = 75%.
46
Frequency Tables
Definition: Show how often each score appears. Use: Helpful in creating graphs.
47
Line Graphs
Use: Show trends over time or continuous data. Axes: X = IV, Y = DV.
48
Pie Charts
Use: Show proportions/percentages of a whole. Tip: Best for nominal data.
49
Bar Graphs
Use: Compare categories (discrete data). Bars: Gaps between bars = discrete data.
50
Histograms
Use: Show distribution of continuous data. Bars: Touching bars = continuous data.
51
Normal Distribution
Definition: Symmetrical bell-shaped curve. Features: Mean = median = mode.
52
Skewed Distribution
Positive Skew: Tail on right; mean > median. Negative Skew: Tail on left; mean < median.
53
Probability
Definition: Likelihood of an event occurring. Range: 0 (impossible) to 1 (certain).
54
Significance Levels (p-values)
Definition: Probability that results are due to chance. Common Level: p ≤ 0.05 = statistically significant.
55
Statistical Tables of Critical Values
Use: Compare calculated value (e.g., U or t) to critical value. If Calc ≥ Crit: Result is significant (depends on test direction).
56
Criteria for a Parametric Test
Interval/ratio data Normally distributed Homogeneity of variance
57
Criteria for Inferential Statistical Tests
Depends on: Level of measurement (nominal, ordinal, interval) Design (independent, repeated, matched) Test purpose (difference or correlation)
58
Type I Error (False Positive)
Definition: Rejecting the null hypothesis when it is actually true. Example: Concluding a drug works when it actually doesn't. Think: "Seeing an effect that isn’t really there."
59
Type II Error (False Negative)
Definition: Failing to reject the null hypothesis when it is actually false. Example: Concluding a drug doesn’t work when it actually does. Think: "Missing a real effect."
60
Reducing Type I Error
Use a stricter significance level (e.g., p ≤ 0.01 instead of 0.05). Increases chance of Type II error, though!
61
. Reducing Type II Error
Use a larger sample size Ensure power is high enough (statistical power = probability of detecting an effect if it exists)

Psychology Research method! (7 decks)

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