Finals Flashcards
observational research
- no direct manipulation of variables
- the investigator looks at relationships
- gives a weak level of causation
experimental reserach
- direct manipulation of variables
- depending on the type of experimental can have a stronger level of causation
discrete variable
limited to certain values
- whole numbers or categories
continuous variables
can theoretically assume any value
- specific values or calculations
what are the types of scales that are discrete
nominal and ordinal
nominal scale
mutually exclusive categories with no logical order
- no direction, no magnitude, no proportion
ordinal scale
ordered rankings but no indication of size or difference
- has direction, no magnitidue or proportion
what are the types of scales that are continous
interval and ratio scales
inverval scale
equal intervals but no absolute zero
- has direction and magnitude but no proportion
ratio scale
equal intervals and has an absolute zero
- has direction, magnitude, and proportion
validity
how well a study can be used to represent a relationship between two variables in a study
external validity
ability for results to be applied to the general population
population vs sample
population: all individuals/objects with a common set of characteristics
sample: a sportion of the larger population that is assumed to represent the population
standard score
expresses how many SD points away from the mean a data point is
what is a standard score also known as
a z score
how to asign percentile to an appropriate quartile, quintile, decile
percentile is by 1 incriments so given a percentile place it in the relative range
how to calculate the percentile of a specific raw score for a rank order or simple requency distribution
P = (n/N)*100
n = scores at or below desirec percentile score
N = total number of values
calculate percintile from raw score for grouped frequency distribution
P = [(((X-L)/i)f+c)/N)*100
X = raw score
L = lower limit
i = interval size
f = frequency of below interval
N = total scores
measures of central tendency
values that describe the middle or central characteristics of a dataset
calculate arithmetic mean
sum of all data/number of score
calculate median
the middle score in a rank ordered list of scores
calculate mode
the score that occurs the most requently in a dataset
measures of variability
quantify the dispersion or spread within a dataset
calculate range
difference between max and min scores