Flashcards in Basic Terms and Definitions Deck (44):

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## Descriptive statistics

### summarize, understand, and describe a group of numbers from a research study

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## What are examples of descriptive stats

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1. measure of central tendency (mean, median, mode)

2. variability (range, varience, sd)

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## Inferential statistics

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1. draw conclusions and make inferences based on numbers from a research study, but go beyond these numbers

2. if there is a difference between groups

3. how sure we are about our conclusions

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## Variable

### condition or characteristic that can have different (varying) values (ex. male/female, ...22,23,24...,

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## Score

### the value of a particular person's answer (ex. male, 25yrs old, 15 sexual partners)

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## Variability

### how much change there is in a group of scores

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## Variance

### a specific measure of variability

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## Correlational Research Designs

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examines if there is a relationship between variables

- "what does knowledge of x tell us about y"

- naturally occurring variables

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## Experimental Research Designs

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goal is to determine why something happens

- "what causes X?"

- control and manipulation of variables

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## Independent variable

### variable that the researcher manipulates/changes

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## Dependent variable

### variable that changes because of IV

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## Components of an experiment

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1. Must have more than one level of IV

2. several, stable/reliable DVs

3. control variables

4. random assignment

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## Control variables

### keeping everything else constant

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## Random assignment

### purpose of random assignment is to create equivalent groups

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## Population

### set of all cases of interest, generally a theoretical concept (not always measurable)

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## Sample

### subset of population that is being studied; something to be concerned about is biased samples

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## Biased Samples

### sample that systematically underselects or overselects from certain groups in the population

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## Parameter

### Some characteristic of population

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## Statistic

### some characteristic of sample

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## Scales of measurement

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1. Nominal

2. Ordinal

3. Interval

4. Ratio

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## Nominal

### mutually exclusive categories (yes or no)

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## Ordinal

### placing variables in a "ranked" order (no, somewhat, yes)

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## Interval

### equal distances between points on the scale (temp., on scale from 1-10 how helpful)

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## Ratio

### exactly like interval scale, but has a true zero (#oof cigs smoked today)

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## IV measurement

### often at a nominal level

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## DV measurement

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Data analysis is limited when using nominal or ordinal data

- interval data is desirable

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## Unimodal data

### only one very high area (mode)

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## Bimodal data

### two very high areas

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## Multimodal data

### many very high areas

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## Rectangular data

### all values have about the same frequency

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## Symmetrical data

### many psychological variables (height, weight, attitudes, work productivity); distribution looks the same on both sides if you split it down the middle

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## Skewed data

### majority of responses are at low or high ends of the scale

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## Central tendency

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a measure that refers to the typical score in a distribution

- "center of data," "best representation"

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## Measures of central tendency

### mean, median, mode

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## mean

### average - interval/ratio data

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## median

### middle score - ordinal data (very skewed interval/ratio data)

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## mode

### most frequent - nominal data - bimodal distribution

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## Properties of the mean

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1. the value is the value around which all observed values are balanced

2. the point at which the sum of the squared deviations is minimized

3. influenced by extreme values (main disadvantage)

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## Variability

### the spread of scores in a distribution

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## Types of variability

### range, variance, standard deviation

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## Range

### the distance between the larges and smallest scores (only reflects two most extreme scores

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## Variance

### the sum of the squared deviations from the mean, divided by N-1

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## Steps for calculating the varience

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1. subtract the mean from each of the scores to get deviations

2. square each deviation

3. add all squared deviations

4. divide by the total number of scores minus 1

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