Standard Deviation
Standard deviation is a measure of the dispersion of a
set of data from its mean. If the data points are further from the mean, there
is higher deviation within the data set. Standard deviation is calculated as
the square root of variance by determining the variation between each data
point relative to the mean.
Type 1 Error
Type 1 Error
Type 1 error, known as a “false
positive”. The error of rejecting a null hypothesis is when it is actually
true. In other words, this is the error of accepting an alternative hypothesis (the
real hypothesis of interest) when the result can be attributed to chance.
Plainly speaking, it occurs when we are observing a difference when in truth
there is none (or more specifically- no statistically significant different).
Alpha
Alpha
Alpha set the standard for how extreme the data must
be before we can reject the null hypothesis. The alpha level is the probability
of rejecting the null hypothesis when the null hypothesis is true. Once you have
chosen alpha, you were ready to conduct your hypothesis test.
Reliability
Reliability
Reliability of a scale indicates how free it is from
random error. Two frequently used indicators of a scale reliability are
test-retest reliability it refer to temporal stability and internal
consistency.
Validity
Validity
The validity of a scale refers to the degree to which
it measure what it is supposed to measure. Unfortunately, there is no one
clear-cut indicator of a scale’s validity. The validation of a scale involves
the collection of empirical evidence concerning its use. The main types of
validity are content validity, criterion validity and construct validity.
Repeated measure
Repeated measure are collected in a longitudinal study
in which change over time is assessed. Other (non-repeated measure) studied
compare the same measure under two or more different condition.
ANOVA
ANOVA
The one-way analysis of variance (ANOVA) is used to
determine whether there are any statistically significant differences between
the means of two or more independent (unrelated) groups (although ten to see
when are a minimum of three, rather than two groups).
T-test
T-test
There are two of different types
of t-test available in IBM SPSS that is:
- Independent-samples t-test, used when to compare the mean score of two different groups of people or condition.
- Paired-sample t-test, used when to compare the mean scores for the same group of people on two different occasions, or when matched pairs.
MANOVA is an extension of analysis of variance for
more than one dependent variable. Dependent should be related in some way, or
should be some conceptual reason for considering together. Independent variable
should consist of two or more nominal or categorical, independent group.
MANOVA
MANOVA
MANOVA test for the difference in two or more vector
of means. MANOVA is useful experimental situations where at least some of the
independent variable are manipulated.
By - alias su'aidah
References
Book
Pallant, J. (2013). SPSS
survival manual: A step by step guide to data analysis using SPSS (5th
ed.). Maidenhead: Open University Press/McGraw-Hill.
Website
http://userwww.sfsu.edu/efc/classes/biol710/manova/MANOVAnewest.pdf
No comments:
Post a Comment