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45 Cards in this Set

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sampling procedures:
-stratified:randomly sample from groups of interest
-cluster: sample group rather than individuals
-convenience: sample p's that are readily available
-quota: representative proportions
statistics vs. parameters
a set of mathematical procedures for organizing, summarizing, and interpreting information
a value that describes the population
ex. the average household income in the us (all in us)
descriptive vs. inferential statistics
descriptive - describing amt
inferential - describing an effect
variables vs. constructs
descriptive statistics
-used to simplify data
-describing amount
ex. avg. household income just saying what you see, not explain anything
inferential statistics
-used to make generalizations from sample to population
-describing an effect
-p value only provided for inferential
a characteristic or condition that changes or has different values for different individuals
-something that varies
-flipside of constant

---# of crimes committed
---age ranges
---recieving drug or placebo
---exposure to violent media or no media
-construct (usually psy construct)
---acceptance of gender roles
---aggression level
elements that the researcher claims to be measuring and manipulating
construct validity: the degree to which the study measures and manipulates the underlying psychological elements that the researcher claims to be measuring and manipulating
operational definitions
-how you measure it
-the measure
categorical vs. continuous variables
categorical variables
-separate categories only
ex. who is still alive in the sample
-all statistical analyses involve knowing the difference b/w categorical and continuous

-no values in b/w
-your in or not, male or female
-#'s designate outcome or category
-used in advanved
correlational studies
continuous variables
-based on a range of values
ex. hormone levels in the sample
-can have whole # or something b/w #'s
ex. temperatures
categorical vs. discrete variables
discrete variables
-separate categories or whole numbers
ex. # of ppl. in room, counting things up
-no "in betweens"
real vs. apparent limits of numbers
real limit = .5 below and .5 above
apparent limit = as it appears
real limits of numbers
the boundaries of scores that are represented on a continuous number line
-the real limit separating two adjacent scores is located halfway between the scores
-upper limit at the top of the interval
-lower limit at the bottom of the interval
ex. 1 = .5-1.5
apparent limits of numbers
ex. 3, 4, 5, 6, 7
- apparent limit = 3-7, not 2.5-7.5 (real limit)
statistical notation
-raw score = x
-set of raw scores = xi
---a number next to x regers to the order in which it appears in the data set
---so, xi is the first score in the set
-sum of raw scores = Σxi
-score multiplied by itself = x^2
-number of people in the population = N
-number of people in the sample = n
order of mathematical operations
-the most frequent score
-can be more than 1
-pro: uses a real number and picks up on existence of multiple groups
-cons: may not really be in the middle
- the middle score
-50% are above it and 50% are below it
-determine N, cf, and c% then find the score that contains 50 as the c%
-be able to hand calculate
-requires ordering the scores from the lowest to the highest
-if an odd # of scores, find the score the is exactly in the middle
-if an even # of scores, find the midpoint b/w the 2 middle scores
-the average score
-n=number of scores
-be able to hand calculate
-pro: accounts for clusters of scores
-con: affected by outliers
when do you use mode?
-when you have a categorical variable
---uses a nominal scale (0=male 1=female)
---discrete variables (ex. # of computers in a home)
-most common score actually exsists (ex. can't have 2.3 computers - want to see the most freq. # of comp. in home)
-helps describe the shape of the distribution
-useful when multiple groupings exsist (ex. bimodal distributions)
when do you use median?
-when weird scores exist
---extreme scores -> skewed dist.
---throws off the mean, not the median
-can calculate when undetermined scores exist
---ex. participant took too long to finish task so had to stop
-can calculate when open-ended categories exist
---ex. # of computers:1,2,3,4 or more
-more appropriate for ordinal scale (rank)
when do you use mean?
-typical measure of central tendency
-accounts for all scores in dist.
-related to measure of variability (distribution)
-useful for stats involving hypothesis testing (aka inferential statistics)
central tendency
a single sore that defines the center of a distribution (i.e. the most representative score)
shape of distribution
bell-curved or 2-humped
create a two-group histrogram
-draw normal curves for each group
-interpreting mean differences
simple vs. grouped frequency distributions
-simple-number of ppl with each score
-grouped-number of ppl in a particular subset
ex. A's, B's, C's, D's, F's on exam
sum of scores
obtaining Σxi from a distribution
add up all f's
obtaining the mean from a distribution
-number that appears the most - biggest frequency #
create a frequency dist. table, find the largest value(s) in the f column
proportions and percentages in distribution
determine N by adding up the frequencies, divide f by N to get the proportion (p), then multiply p by 100 to get the percentage (%)
real limits
-the boundries of scores that are represented on a continuous number line
-the real limit separating 2 adjacent scores is located halfway b/w the scores
-upper limit at the top of the interval
-lower limit at the bottom of the interval
=2.5-7.5 not 3-7
frequency distribution
an organized tabulation of the number of individules located in each category on the scale of measurement
frequency distribution graph (histogram)
-possible values on x-axis
-number of times calue appears on y-axis
how can graphs be misused?
toying with y-axis
-making it appear big deal or no big deal
shapes of frequency distribution
graph is skewed
positively skewed
most scores grouped at bottome but some are extremely high _/^\_____
negatively skewed
most scores grouped at top, but some scores are extremely low ___/^^^^\__
percentiles and percentile ranks
-finding them in frequency dist.
-interpolating a percentile using a formula
problem with inferential stats
-there will always be a difference between the sample statistic and the population parameter
-difference: sampling error (can be big or small; if big -> can't report as real finding)
-most often seen in political polls
ex. approval rating has a sampling error of +/- 3 percentage points (sampling error)

45% approval rating -> statistic
act. b/w 48-42% -> parameter
statistical dead heat -> sampling range overlap eachother

add this to the lower SCORE,
and you will have the desired percentile