STATISTICAL
TREATMENT
LEARNING COMPETENCY: CS_RS12-IId-g-2
▹ To discuss statistical treatments
used to analyze data to describe and
decide if there are differences and
relationships among variables in
studies requiring bivariate analysis
Objectives:
After the discussion the students will
be able to:
1. classify data according to levels of
measurement;
2. determine the specific descriptive
or inferential statistical tools to use
for a bivariate study.
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If you guess after 1 clue, you win 10 points
If you guess after 2 clues, you win 6 points
If you guess after 3 clues, you win 4 points
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1st clue for 10 points!
It is the lowest level of variable
measurement which includes assigning a
numerical value to a characteristic of a
variable for the purpose or categorization
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2nd clue for 6 points!
The numerical value assigned to the
variable indicates whether the respondents
have same or different characteristics or
category, but it cannot be treated
mathematically
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3nd clue for 4 points!
▹ Example of this scale is : Marital status
The types of marital status are coded as follows for
easy classification:
married =1
single =2
separated =3
widowed =4.
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▹ Nominal Scale of
Measurement
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1st clue for 10 points!
The highest level of scale
of measurement.
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2nd clue for 8 points!
This scale of measurement the
number has its value and zero
means nothing.
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3rd clue for 4 points!
Example of variables measured in
this scale of are the following:
Height
Weight
Distance
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Ratio scale.
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1st clue for 10 points!
A descriptive statistic is used to
determine the number of occurrence
of a value
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2nd clue for 8 points!
This descriptive statistic is used to
analyze nominal variables.
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3rd clue for 4 points!
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Frequency Distribution
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1st clue for 10 points!
A descriptive statistical tool that
is being described as the average.
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2nd clue for 8 points!
A descriptive statistical tool that
is being described as most useful
estimate of central tendency.
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3rd clue for 4 points!
A descriptive statistical
tool that can be solved by
sum of the actual scores
divided by the perfect
score.
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Mean
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1st clue for 10 points!
A inferential statistical tool that is used
to determine the relationship in nominal
or ordinal variables.
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2nd clue for 8 points!
A inferential statistical tool that is used
to prove the null hypothesis : “There is
a significant relationship between
gender and income”.
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3rd clue for 4 points!
It is a statistical tool to determine
the relationship between
observed and expected
occurrences .
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Chi-square test
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1st clue for 10 points!
An inferential statistical tool that
is used to compare two means.
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2nd clue for 8 points!
It aims to determine the difference to
two means from different samples or
same sample. ( for independent
samples or paired samples)
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3rd clue for 4 points!
Used to test the
hypothesis “There is no
significant difference
between the pre-test
score and the post test
score.”
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T-test
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STATISTICAL TREATMENT
Used to properly test the hypothesis
Answers the research questions
WHAT IS THIS?
Present the results of the study in clear and
understanding manner
It is the end point of a long process of formulating the hypothesis,
constructing the instrument and collecting data.
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HELLO!
This is a body of knowledge and techniques in
collecting, organizing, presenting and interpreting
data.
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Types of variable measurement
1. Nominal scale of measurement - the number
assigned has no meaning, purely for
categorization only.
VARIABLE CATEGORIES
SEX MALE , FEMALE
BLOOD TYPE TYPE A, TYPE B, TYPE O TYPE AB
SKIN COLOR FAIR, BROWN, BLACK
number is assigned for ranking
purposes.
“
2. Ordinal scale of measurement – a
VARIABLES RANKING
ACADEMIC HONORS With Honors, With High Honors, With Highest
Honors
LEVEL OF SATISFACTION Not Satisfied, Slightly Satisfied, Moderately
Satisfied, Highly Satisfied
FREQUENCY Never, Rarely, Sometimes, Often, Always
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Types of variable measurement
3. Interval level of measurment –there is true value
of the number assigned to the variable. Zero does
not mean nothing
Example of Variables :
Temperature, pH, IQ level
a true zero point
Example :
“
2. Ratio scale of measurement – it has
Height
Weight
Age
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DESCRIPTIVE STATISTICS
Used to describe,
synthesize, and
determine trends
in your data
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FREQUENCY
DISTRIBUTION
AND
PERCENTAGE
Frequency distribution –
identifying the number of
occurrence of cases per score.
Percentage – What part of
the total sample size has
obtained the particular score
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Measure of Central
Tendency
MODE – the most frequently
occurring score in a
distribution.
MEDIAN – value that divides
the score exactly in half.
MEAN – the sum of score
divided by the number of
scores (this is the most useful
estimate of central tendency)
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VARIABILITY OF
DISTRIBUTION
RANGE – based on the highest
and lowest score in the distribution.
STANDARD DEVIATION-
indicates the amount of deviation of
the values from the mean.
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INFERENTIAL STATISTCS
Statistical tools to test
the hypothesis.
Results of inferential
statistics will enable
the researcher to
accept or reject the
hypothesis
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Testing For Difference Between Means
T-TEST
“There is no significant difference
between the mean score of group A
and the mean score of group B”
T-test for independent groups
“ There is no significant difference
between the mean pre-test score and
the mean post test score”
Paired t-test
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Testing For Difference Among Means
One Way Analysis of Variance
“There is no significant
difference among the means
test score of three groups”.
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Testing For Correlation for Nominal
Variables
Chi-square
“There is no significant
relationship between
demographic profile and
electrical saving practices of
the village residents”
Testing For Correlation of interval and ratio variables
Pearson Product Moment
Correlation
“ There is no relationship
between the parent’s income
and licensure exam scores
among 2019 Teacher’s board
passers”.
Testing For Correlation of two rank variables
Spearman’s Rank Order
Correlation Coefficient
“ There is no relationship between
the birth order and academic
ranking among G12 students”.
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THANKS!
Any questions?
You can find me at @username & user@mail.me
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