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Statistics I and II summary

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This is the summary you need for statistics. This summary can be used for both statistics I and statistics II. I studied this and passed both with more than a 32/40.

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  • 26 mei 2019
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  • 2018/2019
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seminckursula
CHAPTER 1

1.3 Research process

 research process
o collect data
o analyse data

1.5 Generating and testing theories and hypotheses

 theory
o good theory allows us to make statement about state of world

 hypothesis
o = explanatory statement about something, it is not itself observable

 prediction
o = something derived from hypothesis that operationalizes it so that you can observe
things that help you to determine plausibility of hypothesis
o not hypothesis

1.6 Collecting data: measurement

1.6.1 Independent and dependent variables

 independent variable

o = variable thought to be cause of some effect

o usually used in experimental research to describe variable that experimenter has
manipulated

 dependent variable

o = variable thought to be affected by changes in independent variable

o can think of this variable as an outcome

 predictor variable
o = variable thought to predict an outcome variable
o Basically another way of saying ‘independent variable’

 outcome variable

o = variable thought to change as function of changes in predictor variable

, o term could be synonymous with ‘dependent variable’


 variables can change or vary, btwn people, locations, or time


1.6.2 Levels of measurement

 Level of measurement
o relationship btwn what is being measured and # that represent what is being measured

 variables can be split into categorical and continuous, and within these types there are
different levels of measurement

 categorical variable
o = entities are divided into distinct categories
o binary variable
o nominal variable
o ordinal variable

 continuous variable
o = entities get a distinct score
o interval variable
o ratio variable

 binary variable
o = there are only two categories
o e.g., dead or alive

 nominal variable
o = there are more than two categories
o e.g., whether someone is an omnivore, vegetarian, vegan, or fruitarian

 ordinal variable
o = same as a nominal variable but the categories have a logical order
o e.g., whether people got a fail, a pass, a merit or a distinction in their exam

 interval variable
o = equal intervals on variable represent equal differences in property being measured
o e.g., difference between 6 and 8 is equivalent to the difference between 13 and 15

 ratio variable
o = same as interval variable, but ratios of scores on the scale must also make sense
o e.g., score of 16 on anxiety scale means that person is, in reality, twice as anxious as
someone scoring 8
o for this to be true, scale must have meaningful zero point

,1.6.3 Measurement error

 Measurement error
o = discrepancy btwn number we use to represent thing we’re measuring and actual
value of thing we’re measuring
o self-report measures: produce larger measurement error

1.6.4 Validity and reliability

 way to ensure measurement error is kept to minimum
o determine properties of measure that give confidence that it is doing its job
o 1st property: validity
 = whether instrument measures what is sets out to measure
 criterion -
 concurrent -
 predictive -
 content -
o 2nd property: reliability
 = whether instrument can be interpreted consistently across  situations
 test-retest reliability

1.7 Collecting data: research design

 correlational or cross-sectional research
o observes what naturally goes on in world without directly interfering w it

 experimental research
o manipulate one variable to see its effect on another

1.7.1 Correlational research methods

 longitudinal research
o measuring variables repeatedly at  time points

 limitation correlational research
o tertium quid
o = 3rd person or thing of indeterminate character

1.7.2 Experimental research methods

 Experimental methods
o want to provide comparison of situations (called treatments or conditions) in which
proposed cause is present or absent

, 1.7.3 Two methods of data collection

 between-groups/between-subjects/independent design

 within-subject/repeated-measures design

1.7.4 Two types of variation

 unsystemic variation
o due to experimenter doing something in one condition but not in other condition

o systemic variation
o due to random factors that exist btwn experimental conditions

1.7.5 Randomization

 randomization
o to determine in which order conditions are completed

 practice effects

 boredom effects

1.8 Analysing data

1.8.1 Frequency distributions

 frequency distribution or histogram

 normal distribution
o A frequency distribution can be either a table or a chart that shows each possible score
on a scale of measurement along with # of times that score occurred in the data

 Scores are sometimes expressed in a standard form known as z-scores.

 To transform a score into a z-score you subtract from it the mean of all scores and divide
the result by the standard deviation of all scores.

 The sign of the z-score tells us whether the original score was above or below the mean;
the value of the z-score tells us how far the score was from the mean in standard
deviation units.

 Two main ways distribution can deviate from normal
o skew: lack of symmetry
o kurtosis: pointyness

 Skew

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