statistical techniques

1. Different Statistical
Techniques

2.  To identify and describe
different statistical techniques
Learning Objective

3.  Knowledge of the different statistical
techniques
Key Understanding
 What are the different statistical
techniques?
Key Question

4. Statistics - is a term that pertains to your acts of
collecting and analyzing numerical data.
 Doing statistics then means performing some
arithmetic procedures like addition, division,
subtraction, multiplication, and other
mathematical calculations.
 Statistics involves analysis, planning,
interpreting, and organizing data in relation to
the design of the experimental method you
chose.
 Statistical methods then are ways of gathering,
analyzing, and interpreting variable or fluctuating
numerical data.

5. 1. Descriptive Statistics
 This describes a certain aspect of a data set by
making you calculate the Mean, Median, Mode
and Standard Deviation.
 It tells about the placement or position of one
data item in relation to the other data, the extent
of the distribution or spreading out of data, and
whether they are correlations or regressions
between or among variables. This kind of
statistics does not tell anything about the
population.
Statistical Methodologies

6. 2. Inferential Statistics
 It is a branch of statistics that focuses on
conclusions, generalizations, predictions,
interpretations, hypotheses, and the like.
 There are a lot of hypotheses testing in this
method of statistics that require you to perform
complex and advanced mathematical operations.
Statistical Methodologies

7. Univariate Analysis – analysis of one
variable
Bivariate Analysis – analysis of two
variables (independent and dependent
variables)
Multivariate Analysis – analysis of multiple
relations between multiple variables.
Types of Statistical Data Analysis

8. 1. Correlation or Covariation (correlated
variation)
 Describes the relationship between two
variables and also tests the strength or
significance of their linear relation.
This is a relationship that makes both
variables getting the same high score or
one getting a higher score and the other
one, a lower score.
Statistical Methods
of Bivariate Analysis

9.  Covariance is the statistical term to measure the
extent of the change in the relationship of two
random variables.
 Random variables are data with varied values
like those ones in the interval level or scale
(strongly disagree, disagree, neutral, agree,
strongly agree) whose values depend on the
arbitrariness or subjectivity of the respondent.
Statistical Methods
of Bivariate Analysis

10. 2. Cross Tabulation
 Also called “crosstab or students-contingency table”
that follows the format of a matrix (plural: matrices)
that is made up of lines of numbers, symbols, and
other expressions.
 By displaying the frequency and percentage
distribution of data, a crosstab explains the reason
behind the relationship of two variables and the effect
of one variable on the other variable.
 If the table compares data on only two variables, such
table is called Bivariate Table.
Statistical Methods
of Bivariate Analysis

11. HEI MALE FEMALE Row Total
CEU 83
(10.2%)
101
(12.2%)
184
FEU 69
(8.5%)
93
(11.3%)
162
JRU 102
(12.6%)
120
(14.5%)
222
LA SALLE 79
(9.7%)
99
(12%)
178
MLQU 81
(10%)
79
(9.5%)
159
NU 61
(7.5%)
58
(7%)
119
OUP 59
(7.2%)
48
(5.8%)
107
UP 120
(14.8%)
98
(11.9%)
218
UST 152
(18.7%)
127
(15.4%)
279
Column Total 806
(100%)
823
(100%)
1,629
Example of a Bivariate Table
HEI Participants in the 2016 NUSP Conference

12. 1. Correlation Coefficient
• This is a measure of the strength and direction of
the linear relationship between variables and
likewise gives the extent of dependence between
two variables; meaning, the effect of one variable
on the other variable.
 Spearman’s rho– the test to measure the
dependence of the dependent variable on the
independent variable
Measure of Correlation

13.  Pearson product-moment correlation – measures the
strength and direction of the linear relationship of two
variables and of the association between interval and
ordinal variables.
 Chi-square – is the statistical test for bivariate analysis
of nominal variables, specifically, to test the null
hypothesis. It tests whether or not a relationship exists
between or among variables and tells the probability
that the relationship is caused by chance. This cannot
in any way show the extent of the association between
two variables.
1. Correlation Coefficient

14.  t-test – evaluates the probability that the mean of the
sample reflects the mean of the population from where
the sample was drawn. It also tests the difference
between two means: the sample mean and the
population mean. ANOVA or analysis of variance also
uses t-test to determine the variance or the difference
between the predicted number of the sample and the
actual measurement.
1. Correlation Coefficient

15. Types of ANOVA
a. One-way analysis of variance – study of the
effects of the independent variable
b. ANCOVA (Analysis of Covariation) – study of two
or more dependent variables that are correlated
with one another
c. MANCOVA (Multiple Analysis of Covariation) –
multiple analyses of one or more independent
variables and one dependent variable to see if the
independent variables affect one another.

16. 2. Regression
 Similar to correlation, regression determines the
existence of variable relationships, but does more
than this by determining the following:
1) which between the independent and dependent
variable can signal the presence of another
variable
2) how strong the relationship between the two
variables are
3) when an independent variable is statistically
significant as a soothsayer or predictor.
Measure of Correlation