Session 1a Flashcards

1
Q

The use of statistics

A

Answer research questions

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2
Q

Population

A

All entities/individuals of interest. Research questions are often about populations. A parameter is a value that describes the population (like the mean, the variance, etc.)

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3
Q

Smaple

A

A subset of individuals from the population. This is the data that is examined during a study. An estimate is a value that describes the sample (ex. mean, variance)

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4
Q

Descriptive statistics

A

Summarize/describe properties of the sample (or the population if we gather data from the entire population)

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5
Q

Inferential statistics

A

Draw conclusions/inferences regarding the properties of the population, but based only on sample data

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6
Q

How to decide what statistical analyses to do

A

The appropriate analyses depend on:
1. Study design / research question
2. Type of variables (level of measurement, distribution)
3. Whether assumptions of the analyses are met

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7
Q

Variable

A

A characteristic that varies across observations (people, location, time, etc.). It’s often a single column in the dataset.

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8
Q

Independent Variable (IV)

A
  • Predictor (or covariate)
  • Factors in an experimental design
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9
Q

Dependent Variable (DV)

A
  • Outcome/Response
  • Predicted variables
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10
Q

Correlational research

A

IV is measured by the researcher. It’s food for ecological validity (generalizing research findings to the real world), but not good for inferring causality.
IV and DV may have a relationship due to a 3rd (confounding) variable, or they have a common cause.

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11
Q

Experimental resarch

A

IV is manipulated by the researcher. It’s good for inferring causality. Manipulating IV in lab settings may sometimes feel detached from the real world.
The statistical methods to analyze data may be the same/similar for correlational and experimental designs.

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12
Q

Between-subjects design

A
  • Each participant in only one experimental condition (e.g., control or treatment)
  • If random assignment is used, groups should be approximately equal on any confounding variables
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13
Q

Within-subjects design

A
  • Each participant does more than one experimental conditions (e.g. control and treatment). DV measured multiple times
  • Vulnerable to practice effects and fatigue/boredom effects as alternative explanations for differences between conditions. Counterbalancing is used to help rule out these alternative explanations.
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14
Q

Types of variables (levels of measurement)

A
  • Quantitative (high level): ratio, interval
  • Categorical (low level): ordinal, nominal
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15
Q

Nominal variables

A

Classifies objects
- Are two observations the same or different on some attribute?
- Not quantitative, though we can use numbers to index the categories
- When dichotomous: two categories (ex. treatment vs. control)

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16
Q

Ordinal variables

A

Rank data
- Does one observation have more or less of an attribute than a second observation?
- Relative standing of two observations on the attribute
- Does not say by how much the observations differ

17
Q

Interval variables

A

Ranking data (equal distances). Assigned numbers have meaningful units, and these unit sizes remain constant.

18
Q

Ratio intervals

A

Interval, but with an absolute 0 point or meaningful origin
- 0 means lack of the attribute
- Comparisons such as “2 times as much” of something or “half as much” make sense

19
Q

Analysis of variance (ANOVA)

A

Most used statistical method used by experimental psychologists
- Categorical IVs (often 2 or more)
- Can have more than one IV
- A single continuous DV
- Usually assumed normally distributed

20
Q

Mean

A

The average. Computation: add together values for a variable, divide by sample size (N). Vulnerable to extreme values (outliers)

21
Q

Median

A

Value in the middle. Computation:
- if there are an odd number of observations, find the middle value
- If there are an even number of observations, find the middle two values and average them
Less vulnerable to extreme values than the mean.

22
Q

Mode

A

Value that occurs the most frequently. Not affected by extreme values. Used for either numerical or categorical data. There may be no mode or several modes.

23
Q

Which measure of central tendency to use?

A
  • Mean is most commonly used, unless extreme values (outliers) exist
  • Median is often used if extreme outliers are present
  • Mode is often used with categorical (nominal) data
24
Q

Measures of variation

A

Measures of variation give information on the spread or variability of data values:
- Range
- Variance
- Standard deviation

25
Q

Range

A
  • Simplest measure of dispersion
  • Difference between the alrgest and the smallest observations
26
Q

Variance

A

(Approximate) average of ‘squared’ deviations of values from the mean. Computation: sum of squared deviations over the degrees of freedom.

27
Q

Standard deviation

A
  • Most commonly used descriptive measure of variation
  • Shows variation about the mean
  • Has same units as the original data
  • Square root of variance
28
Q

SD and z-score

A

Standard deviation (SD) is in the metric of the original measurement instrument.
We can have better interpretation of SD if scores are converted to z-scores: how far away is a score from the mean (in SD units)?
If the distribution can be assumed normal (or close to it), standard deviations (and z-scores) can tell us about about how many scores are above/below a particular score

29
Q

Shape of a distribution

A
  • Symmetric or skewed
  • Mean < median: negatively skewed
  • Median > mean: positively skewed
30
Q

Normal (Gaussian/Bell-shaped) distribution

A

In many statistical techniques for experimental designs, the dependent variable is assumed to be continous and normally distributed. If normally distributed:
- Mean = Median = Mode
- Mean (µ) and Standard deviation (σ) are sufficient to describe a normal distribution
- Density: height of bell curve at different values of X
- µ ± σ contains around 68% of all values (µ ± 2σ = 95% and µ ± 3σ = 99.7%)