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32 Cards in this Set
- Front
- Back
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null hypothesis vs. alternative hypothesis
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null: one that the researcher wants to reject because it proposes there will be no change in behavior
alternative: the "hunch" that the researchers want to test |
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hypothesis must be...
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1) falsifiable
2) operationally defined (measurable, allows for replication) |
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2 basic strategies of research
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1) Observational: naturalistic observation, case studies, surveys, correlational studies
2) Experimental: only one you can prove, cause and effect, isolating a single factor |
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Independent vs. dependent variables
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independent: the "cause". variable that is manipulated by the investigator, researcher has control
DEPENDENT: variable that is measured, counted, or recorded. It depends on the variable, the "effect" |
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Quantitative data
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tells an amount or measure of something, a number
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Qualitative data
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a word or a code that represents a class or category
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SCALES OF MEASUREMENT
1) Nominal (qual) |
must be in one and only one category
qualitative, mutually exclusive and exhaustive ex. sex, religion, major |
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SCALES OF MEASUREMENT
2) Ordinal (qual) |
like an nominal scale BUT they may be ranked in order of magnitude
rank your rank. greater than relationships, BUT no implication of how much greater ex. freshman, sophomore, etc. medals, military rank |
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SCALES OF MEASUREMENT
3) Interval (quant) |
like an ordinal scale BUT the distance between each rank is given but it has the same meaning anywhere on the scale
ex. temperature 32' 33' 34' IQ |
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SCALES OF MEASUREMENT
4) Ratio |
like interval scale BUT there IS AN ABSOLUTE ZERO point
a ration between measures becomes meaningful can you go negative? |
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descriptive statistics
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the numbers used to describe the dependent variable, its purpose is to summarize, organize and simplify data
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inferential statistics
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its purpose is to draw a conclusion about conditions that exist in a population from study of a sample
"lazy wat to not get data from everyone" |
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POPULATION
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population: includes all members of a group,
"mu" = mean of a population "sigma" = stand. dev. of a population N= total # of scores of a population |
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SAMPLE
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a representative part of a population
M= mean of a sample S= standard deviation of a sample n= total number of scores in a sample |
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histogram
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NO GAPS between bars
shapes: normal, bimodal, negative, positive "the tail is the skew" where the tail is pointing |
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mode
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the most frequent score
Pros: good for nominal data, good when there are 2 "typical scores", easiest to commute, score comes from data Con: ignores most of the info, small samples may not have one |
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median
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the middle value when the observations are ordered
pro: not influenced by extreme scores, good with ordinal data con: may not exist in the data, doesn't take actual account of the values |
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mean
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the sum of the scores / the # of scores
pro: mathematical center, good for interval and ratio data, doesn't ignore any data con: influenced by extreme scores, may not exist in data |
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normal vs. skewed distribution
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normal --> mean, median, and mode all the same
skewed --> the mean is pulled toward the tail |
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variance
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the average squared distance from the mean
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standard deviation
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the square root of the variance
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interquartile range
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range of the middle half of the score
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mean of a population formula
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= x
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mean of a sample formula
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M = x
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VARIANCE
Definitional Formula Population |
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VARIANCE
Definitional Formula Sample |
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VARIANCE
Computational Formula Population |
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VARIANCE
Computational Formula Sample |
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STANDARD DEVIATION
Definitional Formula Population |
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STANDARD DEVIATION
Definitional Formula Sample |
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STANDARD DEVIATION
Computational Formula Population |
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STANDARD DEVIATION
Computational Formula Sample |
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