Intro To Statistics, Central Tendency, Frequency Distributions

Front 1

sampling procedures:

Back 1

-random
-stratified:randomly sample from groups of interest
-cluster: sample group rather than individuals
-convenience: sample p's that are readily available
-quota: representative proportions

Front 2

statistics vs. parameters

Back 2

stat-concrete
para-abstract

Front 3

statistics

Back 3

a set of mathematical procedures for organizing, summarizing, and interpreting information

Front 4

parameters

Back 4

a value that describes the population
ex. the average household income in the us (all in us)

Front 5

descriptive vs. inferential statistics

Back 5

descriptive - describing amt
inferential - describing an effect

Front 6

variables vs. constructs

Back 6

..

Front 7

descriptive statistics

Back 7

-used to simplify data
-describing amount
ex. avg. household income just saying what you see, not explain anything

Front 8

inferential statistics

Back 8

-used to make generalizations from sample to population
-describing an effect
-p value only provided for inferential

Front 9

variables

Back 9

a characteristic or condition that changes or has different values for different individuals
-something that varies
-flipside of constant

-descriptive
---# of crimes committed
-categorical
---age ranges
---race
---gender
---recieving drug or placebo
---exposure to violent media or no media
-construct (usually psy construct)
---age
---acceptance of gender roles
---aggression level

Front 10

constructs

Back 10

elements that the researcher claims to be measuring and manipulating

Hint 10

construct validity: the degree to which the study measures and manipulates the underlying psychological elements that the researcher claims to be measuring and manipulating

Front 11

operational definitions

Back 11

-how you measure it
-the measure

Front 12

categorical vs. continuous variables

Back 12

..

Front 13

categorical variables

Back 13

-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

Front 14

continuous variables

Back 14

-based on a range of values
ex. hormone levels in the sample
-can have whole # or something b/w #'s
ex. temperatures

Front 15

categorical vs. discrete variables

Front 16

discrete variables

Back 16

-separate categories or whole numbers
ex. # of ppl. in room, counting things up
-no "in betweens"

Front 17

real vs. apparent limits of numbers

Back 17

real limit = .5 below and .5 above
apparent limit = as it appears

Front 18

real limits of numbers

Back 18

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

Front 19

apparent limits of numbers

Back 19

ex. 3, 4, 5, 6, 7
- apparent limit = 3-7, not 2.5-7.5 (real limit)

Front 20

statistical notation

Back 20

-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

Front 21

order of mathematical operations

Back 21

PEMDΣAS

Front 22

mode

Back 22

-the most frequent score
-can be more than 1
-1,2,2,3,3,3,3,4,4,5,5,5
m=3
-pro: uses a real number and picks up on existence of multiple groups
-cons: may not really be in the middle

Front 23

median

Back 23

- 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

Hint 23

-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

Front 24

mean

Back 24

-the average score
-Σxi/n
-n=number of scores
-be able to hand calculate

Hint 24

-pro: accounts for clusters of scores
-con: affected by outliers

Front 25

when do you use mode?

Back 25

-when you have a categorical variable
---uses a nominal scale (0=male 1=female)
---discrete variables (ex. # of computers in a home)

Hint 25

advantages:
-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)

Front 26

when do you use median?

Back 26

-when weird scores exist
---extreme scores -> skewed dist.
---throws off the mean, not the median

Hint 26

advantages:
-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)

Front 27

when do you use mean?

Back 27

-typical measure of central tendency

Hint 27

advantages:
-accounts for all scores in dist.
-related to measure of variability (distribution)
-useful for stats involving hypothesis testing (aka inferential statistics)

Front 28

central tendency

Back 28

a single sore that defines the center of a distribution (i.e. the most representative score)

Front 29

shape of distribution

Back 29

bell-curved or 2-humped

Front 30

create a two-group histrogram

Back 30

-draw normal curves for each group
-interpreting mean differences

Front 31

simple vs. grouped frequency distributions

Back 31

-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

Front 32

Σxi

Back 32

sum of scores

Front 33

obtaining Σxi from a distribution

Back 33

add up all f's

Front 34

obtaining the mean from a distribution

Back 34

-number that appears the most - biggest frequency #
create a frequency dist. table, find the largest value(s) in the f column

Front 35

proportions and percentages in distribution

Back 35

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 (%)

Front 36

real limits

Back 36

-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
-3,4,5,6,7
=2.5-7.5 not 3-7

Front 37

frequency distribution

Back 37

an organized tabulation of the number of individules located in each category on the scale of measurement

Front 38

frequency distribution graph (histogram)

Back 38

-possible values on x-axis
-number of times calue appears on y-axis

Front 39

how can graphs be misused?

Back 39

toying with y-axis
-making it appear big deal or no big deal

Front 40

shapes of frequency distribution

Back 40

graph is skewed
-bell-curved
-2-humped

Front 41

positively skewed

Back 41

most scores grouped at bottome but some are extremely high _/^\_____

Front 42

negatively skewed

Back 42

most scores grouped at top, but some scores are extremely low ___/^^^^\__

Front 43

percentiles and percentile ranks

Back 43

-def
-finding them in frequency dist.
-interpolating a percentile using a formula

Front 44

problem with inferential stats

Back 44

-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

Front 45

interpolation

Back 45

Int=(Dsco*Dper)/D%

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