4. Quantitative Data Analysis
• is time consuming because it
involves series of examinations,
classifications, mathematical
calculations, and graphical
recording, among others.
5. Element #2
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Element #2
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simply add your own text
and description here.
Steps in Quantitative Data
Analysis
• Having identified the measurement scale or
level of your data means you are now ready
to analyze the data in this manner (Badke
2012; Letherby 2013; Mc Bride 2013)
6. Step 1: Preparing the Data
• Keep in mind that no data organization means no sound data analysis. Hence,
prepare the data for analysis by first doing these two preparatory sub steps:
1. Coding System
• To analyze data means to quantify or change the verbally
expressed data into numerical information. Converting the
words, images, or pictures into numbers, they become fit for any
analytical procedures requiring knowledge of arithmetic and
mathematical computations. But it is not possible for you to do
the mathematical operations of division, multiplication, or
subtraction in the word level, unless you code the verbal
responses and observation categories.
7. • For instance, as regards gender variable, give number
1 as the code or value for Male and number 2 for
Female. As to educational attainment as another
variable, give the value of 2 for elementary; 4 for high
school, 6 for college, 9 for MA, and 12 for PhD level.
By coding each item with a certain number in a data
set, you are able to add the points or values of the
respondents' answers to a particular interview
question or questionnaire item.
8. 2. Data Tabulation
• For easy classification and distribution of numbers
based on a certain criterion, you have to collate
them with the help of a graph called Table. Used
for frequency and percentage distribution, this kind
of graph is an excellent data organizer that
researchers find indispensable.
10. Step 2: Preparing the Data
• Data coding and tabulation are the two important things
you have to do in preparing the data for analysis. Before
immersing yourself into studying every component of the
data, decide on the kind of quantitative analysis you have
to use, whether to use simple descriptive statistical
techniques or advanced analytical. The first one that
college students often use tells some aspects of
categories of data such as: frequency of distribution,
measure of central tendency (mean, median, and mode),
and standard deviation.
11. • However, this does not give information about
population from where the sample came. The second
one, on the other hand, fits graduate-level research
studies because this involves complex statistical
analysis requiring a good foundation and thorough
knowledge about statistics.
• The following paragraphs give further explanations
about the two quantitative data-analysis techniques.
(De Mey 2013; Litchtman 2013; Picardie 2014).
12. 1. Descriptive Statistical Technique
• This quantitative data-analysis technique provides a summary
of the orderly or sequential data obtained from the sample
through the data-gathering instrument used. The results of the
analysis reveal the following aspects of an item in a set of data
(Morgan 2014; Punch 2014; Walsh 2010):
Frequency Distribution - gives you the frequency of distribution
and percentage of the occurrence of an item in asset of data. In
other words, it gives you the number of responses given
repeatedly for one question.
13. For example:
Question: By and large, do you find the Senators'
attendance in 2015 legislative sessions awful?
Measurement
Scale
Code Frequency
Distribution
Percent
Distribution
Strongly Agree 1 14 58%
Agree 2 3 12%
Neutral 3 2 8%
Disagree 4 1 4%
Strongly Disagree 5 4 17%
14. Measure of Central Tendency
• indicates the different positions or values of the items, such
that in a category of data, you find an item or items serving as
the:
Mean
• average of all the items or scores
• Example: 3+8+9+2+3+10+3=38
38÷7=5.43 (Mean)
Median
• the score in the middle of the set of items that cuts or divides
the set into two groups.
• Example: The numbers in the example for the Mean has 2 as
15. Mode
• refers to the item or score in the data set that has the
most repeated appearance in the set.
• Example: Again, in the given example above for the
Mean, 3 is the Mode.
Standard Deviation
• shows the extent of the difference of the data from the
mean. An examination of this gap between the mean and
the data gives you an idea about the extent of the
similarities and differences between the respondents.
16. There are mathematical operations that have to do to determine
the standard deviation. Here they are:
Step 1. Compute the Mean.
Step 2. Compute the deviation (difference) between respondent's
answer (data item) and the mean. The plus sign (+) appears before
the number if the difference is higher; negative sign (-), if the
difference is lower.
17. Step 3. Compute the square of each deviation.
Step 4. Compute the sum of squares by adding the
squared figures.
Step 5. Divide the sum of squares by the number of data
items to get the variance.
Step 6. Compute the square root of variance figure to get
standard deviation.
19. (Step 4) Sum of Squares: 321
(Step 5) Variance = 36 (321 +9)
(Step 6) Standard Deviation-6 (square root of 6)
2. Advanced Quantitative Analytical Methods
• An analysis of quantitative data that involves the
use of more complex statistical methods needing
computer software like the SPSS, STATA, or
MINITAB, among others, occurs among graduate-
level students taking their MA or PhD degrees.
20. Some of the advanced methods of quantitative data
analysis are the following (Argyrous 2011; Levin &
Fox 2014: Godwin 2014):
a. Correlation
• uses statistical analysis to yield results that
describe the relationship of two variables. The
results, however, are incapable of establishing
causal relationships.
21. b. Analysis of Variance (ANOVA)
• the results of this statistical analysis are sued
to determine if the difference in the means or
averages of two categories of data are
statistically significant.
• Example: If the mean of the grades of a
student attending tutorial lessons is
significantly different from the mean of the
grades of a student not attending tutorial
lessons.
22. c. Regression
• has some similarities with correlation, in that, it also
shows the nature of relationship of variables, but
gives more extensive result than that of correlation.
Aside from indicating the presence of relationship
between two variables, it determines whether a
variable is capable of predicting the strength of the
relation between the treatment (independent
variable) and the Outcome (dependent variable).
23. • Example:
If reviewing with music (treatment
variable) is a statistically significant
predictor of the extent of the concept
learning (outcome variable) of a person.
