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104 Cards in this Set
- Front
- Back
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signifcance threshold p<0.05
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less than 5% probability random chance
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can only generalise findings if sample is.....
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..... representative
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probability sampling: SSC
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simple random=everyone equal chance of being selected
stratified=reflecting characteristics but other than that random 70% male n 30% female cluster= select units in population representing wider population |
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non probabiliy sampling QSOP
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quota= collect data until reach numbers needed
snowball= start with a group and then meet similar ppl (good investigating illegalities) opportunity= common when happen to be there at the time purposive=specific, women aged between 30-35 |
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normal distribution characteristics
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1. theory stats based on the observation of many naturally occuring interval level variables
2. distribution is bell shaped 3. symetrical around mid-point at whch mode, median and mean all are equal 4. tails indefinite never meet horizon 5.areas unders the curve between the midpoint, 1, 2, 3 SDeviations all known |
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standard deviation SD
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measure of dispersion/spread of data from the mean in a sample
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z scores
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how many SD's a value is away from the mean.
z= 0 = the mean z = +1= 1 SD above the mean z= -1= 1 SD below the mean *info on score of individual compared to rest of sample *standardise scores so can compare across studies and samples |
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type I error
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hypothesis is true but reject
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type II error
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hypothesis false but accept
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p<0.05 5% chance....
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.... 1 in 20 that there is random chance and not a genuine diff
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chi square tests...
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.... 2 categorical variables
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correlation tests...
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.... 2 continuous variables
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regression/multiple tests...
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... Continuous DV's and multiple IV's
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logistic regression tests...
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.. dichotomous DV
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T-Test tests....
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.... 2 groups continuous DV's
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ANOVA tests...
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... 3 + groups continuous DV
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pretests on data...
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.... ensure assumptions are met
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pearsons chi square...
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.... association between categorical data
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crosstabulations...
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.... compare distributions of frequencis within particular categorical conditions
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degress of freedom
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extent to which data are free to vary. as complexity increases df increases
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assumptions chi square data
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data categorical. at least 5 expected frequencies within each cell
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p<0.001
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less than 1 in 1000 chance
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SD 1, 2, 3, 4
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50%, 34.13%, 13.59%, 2.15%, 0.13%
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sampling distributions
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always normally distributed as ave of sample any extremes already pulled in
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standard deviation when individual data
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standard error when in groups
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standard erro of the mean
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standard deviation of sampling distribution of the mean
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95.44% of sample means will have the population mean within..
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.... 2 standard error of the means
therefore population mean likely to lie within 2 std errors of the sample mean, 95.44 times out of 100 |
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1 sample T test..
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... is sample significantly diff from wider population
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parametric tests
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T-tests ANOVA (homogenity of variance assumptions apply with independant tests)
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Levenes is a pre-test in T test statistics
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if sig assumptions of homogenity of variance violated and equal variances not assumed. have to dobefore interpreting t test
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ANOVA
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analysis of variance test diff lele interval data (extension of t test)
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ANOVA... example
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DV test scores and 3 unis as IVs
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the higher the f statistic the greater the variance between groups rather than within groups`
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higher f more confident we are that groups are different
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f(2,21)
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2= DF between 21= DF within
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TUKEY HSD adjust for family wise error rate
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shows honestly exactly which groups are sig diff.
divides 0.05 by 3 to avoid type 1 error rate |
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tkey post hoc test
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after event analysis
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lavenes test, pre test
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if sig then interpret welch
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pearsons correlation
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interval level data testing correlations between 2 variables of interval/ratio level data
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pearson correlation magnitude
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0.1-0.3 = weak
0.3-0.59=moderate 0.6-0.99=strong 1=perfect |
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r square
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proportion of variance in Dv accounted for by IV expressed as a percentage (r2=0.25 = 25%)
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r square is high 80-90%...
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... good thing as line fits well and a larger value means not much left to explain about y other than x using its relationship with y.
if closer to x then need other variable to explain y other than one already tried |
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assumptions of regression
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normailty of variation around line of regression
independance variation around line of regression |
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reporting findings of multiple regression:
significance of model, variance accounted for, interpreting describing findings |
f stat
r2 beta |
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beta coefficient howell 2002
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the unique contribution of each variable to the predictions of Y (DV)
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N cant be less than 50
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sample size cant be less than 50, recommendation is 100
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logistic regression
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IV's are categorical, targets met or not met
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logistic regression assumptions
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IV's continuous or dichotomous
NOT assume continuous IVs normally distributed assumes multicolinearity DV must be coded 0 or 1 (predict likelyhood being 1s rather than 0s) |
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zero order correlation aka
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pearson r coefficient
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parametric assumptions
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assumptions about characteristics population
tests=t tests and anova 1. random sampling 2. no bias 3. normal distribution 4.minimum interval level data 5. homogenity of variance of diff samples (only with independant tests not related tests) |
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parametric assumptions important...
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... tests are attempting to estimate unkown population parameters by using sample statistics. parameters constranied by assumptions
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para assump overcoming paradox sampling....
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.... misleading unless representative of population how can u tell if representative unless already know what need to know
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insufficient questions
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wording
clarity of terminology scales of response, likert appropriateness of topics |
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construct
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concept trying to measure
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indicator
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what we use to tap into that construct
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single indicators are poor measures
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prone to measurement error unreliable
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composite measures..
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... constructed multiple indicators in form of an ave same q asked in diff ways over again
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face validity
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looking like will measure construct
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criterion validity
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does it demonstrate it measures constuct validity
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factor analysis
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assesses criterion and discriminant validity (appropriate multi indicator measures and latent constructs)
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factor loadings indicate
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strength of assoiations between indicator and latent construct
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latent constructs
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themes underlying all questions
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FOWLER surveys answers of interest...
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... not intrinsically but because of relationship something is supposed to measure
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reliability...
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.... making sure that diff in answers stems from diff among respondents not diff in stimuli exposed to
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bad wording
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age last birthda
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defining breakfast
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meal before 10am
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nt asking multiple questions
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wanna be rich and famous
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avoid why questions
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?
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validity subjective states
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cannot be verified, opinions cant be verified
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nominal
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ppl sorted into unordered categories
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ordinal
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ppl ordered into category along single dimension, good, bad
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interval
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numbers ttached providing meaningful info about distance between stimuli
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ratio
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ratios between value meaningful as well as intervals between them, distance weight
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open q's good..
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.... obtain answers unanticipated
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closed q's good...
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... more reliable when alternatives
if an answer scale can compare acorss groups and time |
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ask about events in last six months
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even if want to know about last year as reporting error
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sensitive or embarrassing q's...
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... emphasise no judgement
self administered rather than face to face confidentiality and anonymity |
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reducing measurement error in question design
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one of least costly ways of improving survey estimate
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5 key characteristics of theoretically normal distribution
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1. theory of statistics based upon observation of many naturally occurring interval level variables
2. distribution is bell shaped 3. curve symmetrical around single mid point at which mode/median/mean all fall and equal to each other 4. tails indefinite never quite meet beyond horizon 5. areas under the curve between midpoint, 1, 2, 3 SD's known |
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reading z scores, how many SD's any 1 sample is away from the mean
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0=50% 1= 34.13% 2=13.59% 3=2.15% 4=0.13%
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sampling distribuiton
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collecting the mean a number of different times from a number of different samples
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standard deviation=measure of dispersion/spread away from mean
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standard error of the mean= SD of sampling distribution of the means
diff is SD individuals spread of data Standard error of the means is samples spread of data |
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approaches of surveying/collecting a sample:
probability:SSC simple random stratified cluster |
non-probability= QSOP
quota snowball opportunity purposive |
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simple random
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everyone in pop equal chance of being asked
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stratified randomw
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reflecting characteristics randomly
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cluster
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large population cant do SS so unit in pop represent wider pop
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quota
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collecting data until reach targets
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snowball
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start with a small group and find similar ppl
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opportunity
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happen to be there at time
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purposive
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women aged between 35-39
specific |
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confidence intervals
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where most samples lie in relation to wider population 68.26%of sample means have population mean within 1 SD
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95.44% confidence within 2 Standard error means
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94.55 times out of 100 the population mean will lie between -1 or +1 standar errors of the sample mean
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parametric assumptions apply in t-tests and ANOVAs assumptions concerning underlying populations where samples come from
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1. random sampling
2.no bias 3.minimal of interval level data(weight/test score) 4.homogenity of variance of diff sample means (only for independant tests not related tests) 5. normal distribution |
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if levenes IS SIG
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NOT homogenous variances do NOT assume equal variance
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need parametric tests as tests...
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... attempt to estimate unkown population parameters by using sample statistics
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paradox need to know about population
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without being able to test whole population
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pearsons correlation coefficient aka
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zero order coefficient
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reprort pearsons as..
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... r=0.97, p<0.001
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r2 variance accounted for in other variables
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variance in DV accounted for by IV
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significance of model f stat
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f(1,(df regression)7(df residual)=122.930 (F in anova) p<0.001
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reporting a beta
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beta=0.973, p<0.001
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MINIMUM OF 50 CASES NEEDED
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RECOMMEND AT LEAST 100. N can be NO less than 50
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f parametric statistic as ANOVA
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variance in DV accounted for by IV's in relation to unaccounted for (are there IV's missing from the model that could explain the DV better)
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beta coefficient
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indication of importance of particular IV's in prediction of a DV indicates magnitude of importance ad direction of relationship
TESTED FOR SIG using a T-TEST |
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crosstabulations/contingency tables
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compare distributions of frequencies within particular categorical conditions
display a relationship between 2 or more dichotomous variables |
