Data analysis is the process of evaluating data using analytical and logical reasoning to examine each component and answer research questions. There are two types of data: qualitative data involving non-numerical information and quantitative data involving numerical information. The steps in data analysis include determining the method, processing the data, analyzing and interpreting findings. Descriptive analysis describes sample characteristics through measures like frequency distributions, measures of central tendency, and measures of variability. Inferential analysis uses statistical tests to test relationships among variables and hypotheses.

1. Data Analysis
BY: ROMMEL LUIS C. ISRAEL III

2. Data Analysis
is a process of
evaluating data using
analytical and logical
reasoning to examine
each component of
the data provided.
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3. Purpose of
Data Analysis
to answer the
research questions
and to help determine
the trends and
relationships among
the variables.
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4. Two Types of
Data
1. Qualitative Data –
comprises information that
isn’t collected in the form of
numbers, and is suited to
influencing subjective
perception of a thing.
2. Quantitative Data –
comprises information that
is collected in the form of
numbers, and is suited to
influencing occurence of an
objective outcome.
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5. Steps in Data
Analysis
Before data collection, the
researcher should accomplish
the ff:
 Determine the method of
Data Analysis
 Determine how to process
the data
 Consult a statistician
Prepare dummy tables.
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6. Steps in Data
Analysis
After data collection :
 Process data
 Prepares tables and
graphs
 Analyze and interpret
findings
 Consult again a statistician
 Prepares editing
 Prepares presentation
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7. Kinds of
Data
Analysis
Descriptive
Analysis
Inferential
Analysis
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8. Descriptive
Analysis
- refers to the description
of the data from a particular
sample.
- hence, the conclusion must refer
only to the sample.
- In other words, these summarize
the data and describe sample
characteristics.
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9. Classification of Descriptive
Analysis
A. Frequency Distribution – a systematic arrangement of numeric
values from the lowest to the highest or highest to the lowest.
Formula : Ef = N
where :
E = sum of
f = frequency
N = sample size
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10. Classification of
Descriptive
Analysis
B. Measure of Central Tendency – a
statistical index that describes the
average of the set values.
Kinds of Averages
• Mode – a numeric value in a
distribution that occurs most
frequently.
• Median – an index of average position
in distribution of numbers.
• Mean – the point on the score scale
that is equal to the sum of the scores
divided by the total number of scores.
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11. Classification of Descriptive Analysis
B. Measure of Central Tendency
Formula : x = ∑
___
n
where :
X = the mean
∑ = the sum of
X = each individual raw score
n = the number of cases
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12. Classification
of Descriptive
Analysis
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13. Commonly
used
measures of
variability Ex.The range for Learning Center A
is 500 (750-250) and the range for
Learning Center B is 300 (650-350).
1. Range – the distance between
the highest score and the lowest
score in a distribution.
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14. Commonly
used
measures
of
variability
2. Standard Deviation –
the most commonly
used measures of
variability that |
indicates the average
to which the scores
deviate from the mean.
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15. Classification
of Descriptive
Analysis
D. Bivariate
Descriptive
Statistics
– derived from the
simultaneous analysis of
two variables to examine
the relationship between
the variables.
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16. Commonly used bivariate descriptive analysis
1. ContingencyTable – is
essentially a two-
dimensioned frequency
distribution in which the
frequencies of two variables
are cross-tabulated.
2. Correlation – the most
common method of
describing the relationship
between two measures.
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17. Inferential
Analysis
- the use of
statistical tests,
either to test for
significant
relationships
among variable or
to find statistical
support for the
hypothesis.
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18. Inferential
Statistics
- are numerical values
that enable the researcher to
draw conclusion about a
population sample.
- this is based on the laws of
probability.
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19. Uses of
Inferential
Analysis
cited some statistical test for
inferential analysis
• t-test – is used to examine the
difference between the means of two
independent groups.
• Analysis of variance (ANOVA) – is used
to test the significance of differences
between means of two or more groups.
• Chi-square – this is used to test
hypothesis about the proportion of
elements that fall into various cells of a
contingency table.
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20. Hypothesis
–Testing
Procedure
•
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21. Steps in testing
hypothesis
 Determine the test statistics to
be used
 Establish the level of
significance
 Select a one-tailed or two-tailed
test
 Compute a test statistic
 Calculate the degrees of
freedom
 Obtain a tabled value for
statistical test
 Compare the test statistics to
the tabled value.
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22. Other Importance
of Data Analysis
It helps in structuring the
findings from different
sources of data collection
Acts like a filter when it
comes to acquiring
meaningful insights out of
huge data-set.
Provides meaningful base
to critical decision.
It helps in keeping human
bias away from research
conclusion with the help of
proper statistical treatment.
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