This document outlines statistical tests and analyses based on sample size and data distribution. It discusses parametric vs non-parametric tests, with parametric tests requiring sample sizes of 30 or more and normal data distribution, while non-parametric tests are for smaller sample sizes or non-normal data. Various statistical analyses are described including regression, correlation, paired and independent samples tests. Both linear and non-linear models are covered.
This presentation explains the concept of ANOVA, ANCOVA, MANOVA and MANCOVA. This presentation also deals about the procedure to do the ANOVA, ANCOVA and MANOVA with the use of SPSS.
“Variable” is a term frequently used in research projects. It is pertinent to define and identify the variables while designing quantitative research projects. A variable incites excitement in any research than constants. It is therefore critical for beginners in research to have clarity about this term and the related concepts. This presentation explains the different types of variables with suitable illustrations.
This is the basic explanation on what are ANCOVA and MANCOVA in research study in which provides the definitions and the illustration on how can these both be use in SPSS tool analysis. If you's like to get practice file, do not hesitate to contact me.
Analysis of Variance - Meaning and TypesSundar B N
This vides briefed the meaning, Introduction, Definition, Application, Classification and Types of ANOVA.
Video link https://youtu.be/YLHGYVMH2T4
Subscribe to Vision Academy
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
This presentation explains the concept of ANOVA, ANCOVA, MANOVA and MANCOVA. This presentation also deals about the procedure to do the ANOVA, ANCOVA and MANOVA with the use of SPSS.
“Variable” is a term frequently used in research projects. It is pertinent to define and identify the variables while designing quantitative research projects. A variable incites excitement in any research than constants. It is therefore critical for beginners in research to have clarity about this term and the related concepts. This presentation explains the different types of variables with suitable illustrations.
This is the basic explanation on what are ANCOVA and MANCOVA in research study in which provides the definitions and the illustration on how can these both be use in SPSS tool analysis. If you's like to get practice file, do not hesitate to contact me.
Analysis of Variance - Meaning and TypesSundar B N
This vides briefed the meaning, Introduction, Definition, Application, Classification and Types of ANOVA.
Video link https://youtu.be/YLHGYVMH2T4
Subscribe to Vision Academy
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
A brief description of F Test and ANOVA for Msc Life Science students. I have taken the example slides from youtube where an excellent explanation is available.
Here is the link : https://www.youtube.com/watch?v=-yQb_ZJnFXw
A brief description of F Test and ANOVA for Msc Life Science students. I have taken the example slides from youtube where an excellent explanation is available.
Here is the link : https://www.youtube.com/watch?v=-yQb_ZJnFXw
Inferential Statistics- Dr Ryan Thomas WilliamsRyan Williams
Chi-square test – tests whether two categorical variables are associated
Bivariate Correlation – to what extent are two variables related
Linear Regression- how well does a set of variables ‘predict’ the value of another (dichotomous) variable
ANOVA- check if the means of more than two groups are significantly different from each other
T-test- statistical significance in means of two groups
Identification and installation of user written commands stata
Flow of statistical analysis full version
1. *n ≤30 & n>30
*Data should not be normally distributed
Flow of Statistical Analysis
Selection of samples/cases/observation/subjects
Modes of statistical tests
*n ≥ 30
*Data should be normally distributed
Parametric tests Non-Parametric tests
Normal Distribution Non-Normal /Free Distribution
Linearity-Homogeneous mode of variations Non-Linearity-Heterogeneity mode of variations
Probability and Non-Probability modes of
samplings and distributions
Probability and Non-Probability modes of
samplings and distributions
Continuous variables (ratios and intervals)
Continuous variables (ratios and intervals) that
are not normally distributed, ordinal and
nominal variables(Categorical variables)
Regression analysis (Y-X)(continuous variable-
categorical & continuous variables)
A) Linear regression
(i) DV=Continuous variable, and IV=Continuous
variables/categorical variables/mixed structure
[Parametric forms of assumptions]
-Simple Linear Regression (Two variable case)
*may not be applicable in reality – residuals
might be higher –efficiency reduction
-Multiple Linear Regression(Multi variable case)
*applicable in reality-residuals might be
lower-expansion of efficiency – should be tied up
with diagnostic testing
Regression Analysis
A) Non-Linear regression
(i) DV=Continuous variable, IV=Continuous
variables/categorical variables/mixed
structure
Simple version of Non-Linear regression
(i)Scatterplot smoothing
(ii)Smoothing splines
(iii) Non-linear mode of regression
Multiple version of Non-Linear regression
(i) Additive/Polynomial regression
Zero/Low forms of correlations between
independent variables. (Lower power of multi-
collinearity) Zero/Low forms of correlations between
independent variables. (Lower power of multi-
collinearity)
Ordinary Statistical Tests
Ordinary Statistical Tests
2. & Ordinal variables)-Categorical & Continuous variables)
Binary Probit Regression(Dichotomous variable-categorical
& continuous variables)
-residuals are normally distributed –linearity mode –
homogeneity
Multinomial Probit Regression(Multi choices/Ordinal
variable – categorical & continuous variables)
-residuals are normally distributed –linearity mode –
homogeneity
& Ordinal variables)-Categorical & Continuous variables)
Binary Logit Regression(Dichotomous variable-categorical
& continuous variables)
-residuals are not normally distributed –non-linearity mode
–heterogeneity
Multinomial Logit Regression(Multi choices/Ordinal
variable – categorical & continuous variables)
-residuals are not normally distributed –non-linearity
mode –heterogeneity
Parametric mode of paired samples(dependent
samples)test
Involves related samples
n ≥ 30
measures significant differences between means
of same persons/objects/subjects at different
time points
Indicates modes of improvements and
decrements
Similar to repeated measures of testing
Matched and unmatched paired samples t-test(2
levels)
Independent variable involves two levels
(nominal)
Normal distribution & Linearity mode
Repeated measure of ANOVA(more than 2 levels)
Non-Parametric mode of paired sample test
Involves related samples
n < 30
measures significant differences between ranks
of means of same persons/objects/subjects at
different time points
Indicates modes of improvements and
decrements
Similar to repeated measures of testing
Independent variable involves two or more than
two levels
Non-normal distribution & non-linearity mode
Wilcoxon test, sign test, McNemar test, and
Marginal test of homogeneity.(2 levels)
Friedman test, Kendall’s W test, and Cochran’s
test(more than two levels)
Parametric mode of independent samples test
Involves unrelated samples
n ≥ 30
measures significant differences between means of
two persons/objects/subjects.
Independent variable involves two groups and more
than two groups(categorical variable)
Normal distribution & Linearity mode
Dependent variables deal with continuous variables
Independent sample t-test/Levene version of t-
test(2 groups)
One way of ANOVA test (>2 groups)
Non-Parametric mode of independent samples test
Involves unrelated samples
n < 30 / n ≥ 30
measures significant differences between ranks
of means of two persons/objects/subjects.
Independent variable involves two or more than
two groups (categorical variable)
Non-Normal distribution & Non-Linearity mode
Dependent variables deal with categorical
variables
Mann Whitney test (2 groups)
Kruskal Wallis rank test (>2 groups)
Parametric mode of correlation
Measures the strength of association between variables
that are normally distributed (Continuous variables) -
Pearson correlation, Pearson distance, stepwise linear
regression, Auxiliary mode linear regression - No/zero
correlation (r=0.00), Weak correlation ( 0.01 ≤ r ≤ 0.39),
Moderate correlation ( 0.4 ≤ r ≤ 0.69), High correlation (0.7
≤ r ≤ 0.79), Very high correlation ( 0.8 ≤ r ≤ 0.99), and
Perfect correlation( r=1)
Parametric mode of correlation
Measures the strength of association between variables
that are normally distributed (Categorical variables) -
Spearman and Kendall’s tau b ranks correlations tests,
stepwise non-linear regression, Auxiliary mode of non-
linear regression- No/zero correlation (r=0.00), Weak
correlation ( 0.01 ≤ r ≤ 0.39), Moderate correlation ( 0.4 ≤ r
≤ 0.69), High correlation (0.7 ≤ r ≤ 0.79), Very high
correlation ( 0.8 ≤ r ≤ 0.99), and Perfect correlation( r=1)