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nominal
ordinal
interval
ratio
4 levels of measurement
nominal variable
Variables that represent different groups or categories without a specific order or ranking
ordinal variable
a property whereby members of a particular group are ranked.
interval variable
a property defined by an operation which pertains making of statements of equality of intervals rather than just statements of sameness or difference and greater than or less than.
It does NOT have a “true” zero point; although 0 maybe arbitrarily assigned.
ratio variable
Numbers on a — indicate the actual amounts of the characteristics being measured
This is the only scale that has an absolute or natural zero, the point of origin being a fixed one
probability sampling
a method wherein every unit of the population is given an equal chance of being chosen for the sample
non-probability sampling
there is no random selection of the cases from the population. Is a method of selecting units from a population using a subjective method
simple random sampling
systemic sampling
stratified sampling
cluster sampling
multistage sampling
types of probability sampling
simple random sampling
it gives each member or item in the population and equal chance of being selected as a sample
systematic sampling
a list of all members of the population is necessary. To determine the sample to be taken from the population, you can select every 𝑘th element in the population for the sample, with the starting point determined at random from the first 𝑘 elements.
stratified sampling
divide the total population into strata. Each stratus is composed of a more or less homogeneous sub- population group but they differ from stratum to stratus in the total population.
cluster sampling
population is grouped into clusters or small units composed of population elements, and the number of these population clusters are chosen by simple random sampling or by systematic sampling with random start
multistage sampling
elements are grouped into hierarchy of units and sampling is done successively
convenience sampling
quota sampling
purposive sampling
snowball sampling
types of non-probability sampling
haphazard or convenience sampling
the sample consists of elements that are most accessible or easiest to contact. This usually includes friends, acquaintances, volunteers, and subjects who are available and willing to participate at the time of the study.
quota sampling
nonprobability sampling version of stratified sampling.
refers to the practice of assigning quotas or proportions of areas to the interviewer assistants of research
purposive sampling
simply pick out the persons whom you think are representative of the population to which you want to make inference to, for the purposes of the study.
-simply rely on the researcher’s expertise in identifying the criteria of a representative sample
snowball sampling
a chain referral sampling conducted in stages
Researchers use this technique when the sample size is small and not easily available
paired sample t-tes
significant difference between 2 groups
dependent variable (pre & post)
parametric test
wilcoxon signed-rank test
significant difference between 2 groups
dependent variable (pre & post)
non parametric test
t-test for independent samples
significant difference between 2 groups
independent variable (eg. male, female)
parametric test
mann whitney u test
significant difference between 2 groups
independent variable (eg. male, female)
non parametric test
repeated meadure one-way ANOVA
significant difference between 3 or more groups
dependent variable (e.g time1, time2, time3)
parametric test
friedman test
significant difference between 3 or more groups
dependent variable (e.g time1, time2, time3)
non parametric test
one-way analysis of variance (One-way Anova)
significant difference between 3 or more groups
independent variable (e.g catholic, protestant, muslim)
parametric test
kruskal-wallis test
significant difference between 3 or more groups
independent variable (e.g catholic, protestant, muslim)
non parametric test
pearson-r
significant relationship (correlation/association)
e.g height, weight
parametric test
spearman rho
significant relationship (correlation/association)
e.g height, weight
non parametric test
chi-square, gamma, cramer’s v
significant difference/association
non parametric test
multiple regression
significant predictor
parametric test
effect size
The strength of the difference between groups, or the influence of the independent variable
power analysis
•gives an indication of how much confidence you should have in the results when you fail to reject the null hypothesis
• The higher the power, the more confident you can be that there is no real difference between the groups
level of measurement
sampling technique
independence
normality
homogeneity of variance
assumptions for parametric test
william sealy gosset
discovered t-test
t-test
is used to determine whether a process or treatment actually has an effect on the population of interest or whether two groups are significantly different from one another
Two sample t-test (Independent-samples t-test)
compare the mean scores of two different groups of people or conditions
Paired-samples t-test (t-test for dependent samples)
compare the mean scores for the same group of people on two different occasions \
One sample t-test
compare the mean scores to a known or hypothesized value of the mean in the population
ronald aylmer fisher
he discovered ANOVA
ANOVA
compares the variability in scores between the different groups and the variability within each group
used to determine whether or not there is a statistically significant difference between the means of three or more groups with different subjects
repeated measure ANOVA
used to determine whether or not there is a statistically significant difference between the means of three or more groups in which the same subjects show up in each group.
pearson-r
provides a numerical summary of the direction and the strength of the linear relationship between two variables (interval).
Note
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