2. This part of the thesis or dissertation
includes all research-related activities to be
undertaken in order to achieve the objectives of
the study and to offer some possible solutions to
the problem. It provides detailed description and
complete information on the preparation of the
questionnaire and the interview, revision and
dry-run of the questionnaire, details of the data
collection strategies and approaches to be done,
and approaches identifying the person/s
responsible for the administration and retrieval
of the questionnaire, and the conduct of the
interview.

3. In group data as illustrated below where 10
items of a given examination is administered
among 50 students with the following scores:
What is the mean of these scores?
9 4 9 8 9
3 4 9 7 8
4 3 7 7 6
5 4 6 4 6
6 6 4 4 4
7 7 3 6 3
6 8 4 4 4
4 9 4 4 4
5 4 4 3 5
3 9 5 3 6
To find the mean,
group the scores
first, tabulate and
add accordingly.
Multiply the scores
(X) with the total to
find the fX. Use
the formula next
slide

4. (X) Scores f (Tabulated) f (Total) f(x)
9 IIII-I 6 54
8 III 3 24
7 IIII 5 35
6 IIII-III 8 48
5 IIII 4 20
4 IIII-IIII-IIII-II 17 68
3 IIII-II
f = 50 Sf (x) =270
The calculated mean is 5.4. in the event that
the subject could not make a fractional score, the
mean is regarded as the nearest whole number. In
this case, it is 5.

5. Median (Md). The Median is the
middlemost score in mid-point of a distribution.
It is the value on each side of which 50 percent
of the cases where distribution falls. In the
example below, the median is 12.
9 10 11 12 13 14 15
= 400 ÷ 500
= 0.80 x 100
= 80%
What percentage was not collected?
Computation: 100% - 80% = 20%

6. Ranking is the process of arranging items or
persons in line or in a regular formation in
order to have a position in relation to others.
Example: Rank the following students with
their test scores.
STUDENTS SCORES RANKS
1. Lesli Ann Ancheta 100 1
2. Abdulkadir Sugal 99 2
3. Bryan Sangalang 98 3
4. Joseph Cajote 96 4
5. Tenny Francis 95 5

7. When two or three students or items have the
same scores, normal ranking should be
computed by adding the rank occupied
divided by the number of students or items
getting the same scores.
Students Scores Ranks
1. Michael John Sison 100 1
2. Myra G. Dulay 99 2.5
3. Marilou Peralta 99 2.5
4. Reynaldo E. Nocho 96 4
5. Ma. Clarisa Lachica 95 5
Computation: 2 + 3 = 5 divided by 2 – 2.5

8. Handling of rank-ordered data is considered a
strength of non-parametric tests. Gibbons
(1993) observed that ordinal scale data are
very common in social science research and
almost all attitude surveys use a 5-point or 7-
point Likert scale. But this type of data are not
ordinal rather than interval. In Gibbons' view,
non-parametric tests are considered more
appropriate than classical parametric
procedures for Likert-scaled data 1/
http://www.creative-wisdom.com/teaching/WBI/parametric_test.shtml

9. 1/ Today very seldom researchers use a single Likert scale
as a variable. Instead, many items are combined as a
composite score if Cronbach Alpha verifies that the items
are internally consistent and factor analysis confirms that
all items could be loaded into one single dimension. By
using a composite score, some social scientists believe
that the ordinal-scaled data based upon a Likert-scale
could be converted into a form of pseudo-interval-scaled
data. To be specific, when 50 five-point Likert-scaled
items are totaled as a composite score, the possible
range of data value would be from 1 to 250. In this case,
a more extensive scale could form a wider distribution.
Nonetheless, this argument is not universally accepted.
http://www.creative-wisdom.com/teaching/WBI/parametric_test.shtml

10. The issue regarding the appropriateness of ordinal-
scaled data in parametric tests was unsettled even in the
eyes of Stevens (1951), the inventor of the four levels of
measurement: "As a matter of fact, most of the scales
used widely and effectively by psychologists are ordinal
scales ... there can be involved a kind of pragmatic
sanction: in numerous instances it leads to fruitful
results." (p.26) Based on the central limit theorem and
Monte Carlo simulations, Baker, Hardyck, and
Petrinovich (1966) and Borgatta and Bohrnstedt (1980)
argued that for typical data, worrying about whether
scales are ordinal or interval doesn't matter

12. Two General Types of Test of
Significance
the parametric non-parametric

13. In statistics, parametric and nonparametric
methodologies refer to those in which a set of data
has a normal vs. a non-normal distribution,
respectively. Parametric tests make certain
assumptions about a data set; namely, that the data
are drawn from a population with a specific (normal)
distribution. Non-parametric tests make fewer
assumptions about the data set. The majority of
elementary statistical methods are parametric, and
parametric tests generally have higher statistical
power. If the necessary assumptions cannot be made
about a data set, non-parametric tests can be used

14. There are two types of test data and
consequently different types of analysis. As
the table below shows, parametric data has an
underlying normal distribution which allows
for more conclusions to be drawn as the shape
can be mathematically described. Anything
else is non-parametric.
http://changingminds.org/explanations/research/analysis/parametric_
non-parametric.htm

15. Parametric Non Parametric
Assumed distribution normal Any
Assumed variance homogenous Any
Typical data Ratio or interval Ordinal or nominal
Data set relationships independent Any
Usual central measure mean Median
Benefits Can draw more conclusions Simplicity; less affected by
Outliers
TESTS
Choosing Parametric test Non parametric test
Correlation test Pearson Spearman
Independent measures, 2
groups
Independent measures t-test Mann –Whitney test
Independent measures, > 2
groups
One way, independent
measures ANOVA
Kruskai – Wallis test
Repeated measures, 2
conditions
Matched – pair- t-test Wilcoxon test
Repeated measures, 2 > 2
conditions
One way repeated measures
ANOVA
Friedman’s test
Changingminds.org

16. The parametric tests are usually
used for testing the significance of the
samples obtained which must be based
upon an assumption of a normal or
symmetrical curve in the population
under study. The most commonly used
parametric are as follows:
Parametric Tests

17. The Z-Test is a general parametric test used to determine
the randomness of samples from a population obtained from
the sample mean with expected population mean. A Sample
Mean is a computed average from raw scores randomly taken
from a well-defined population while Population Mean, which
is the mean itself, is the computed average from the raw scores
obtained from the respondents’ target population. This is called
Z-Test because the sample mean (x) has to be transmitted to a
standard score called Z.
In this test, the symbol P>.05 and P<.05 are used to express
the probability of the condition under study. If P >.05, the null
hypothesis is rejected and if P <.05, the null hypothesis is
accepted.
a) Z-Test

18. The test for Independent Sample Means is
used to determine if the observed difference
between the mean of two groups is statistically
significant. It is, therefore, a test for the
observed difference between two sample
means not correlated with each other. It is
used to compare the difference between the
average of cases of control and experimental
groups and to determine if there is a difference
between the averages of two intact groups.
b) t-Test for Independent Sample Means

19. The test for Dependent Sample Means is a
more precise test with its use limited to scores
that are correlated and involving the pre-test
and post-test. The t-value is obtained from the
table of critical t-value using the appropriate
degrees of freedom. If the computed t is
greater than the tabular t, the hypothesis of
no difference between the pre-test and post-
test is rejected.
c) t-Test for Dependent Sample Means

20. The F-Test is a one-way analysis of variance
or one-way ANOVA or Analysis of Variance. It is
used when the study compares the means of two
or more groups. An F is a ratio of two variances or
mean squares and is expected to be equal to one
if the two population variances are equal. F
values with varying degrees of freedom are found
in the F-Table with, usually, F values of .05 and
.01 level of significance. In a two group
comparison the obtained F is equal to t.
d) F-Test.

21. This analysis of variance is also called “factoral
analysis of variance” used when the researcher wishes
to know the specific effects of a treatment variable on
criterion variables obtained from specific subjects
classified by demographic factor. In short, it is used to
determine the main effect and interaction effects and
interaction effects of two independent factors.
that is, those who got high in one factor are the ones
who got low in the other factor and those who got low
in one factor got high in the other factor.
e) Two Way ANOVA or Two Way Test

22. Non
Parametric
Test

23. The Chi-Square Test. These are known as non-
parametric statistics.
There are two types of Chi-Square, X2, test that
could be used in research, namely:
(1) The Chi-Square Goodness of Fit Test
(2) The Chi-Square Test of Association
These are used when data are of the ordinal or
nominal levels of measurements. Generally, the
X2 test describes the difference between
expected and observed frequencies.

24. The Chi-Square Goodness of Fit Test is
employed to find if an observed data or
frequency distribution on a variable differs
significantly from an expected or theoretical
distribution. The theoretical frequency
distribution is dictated by the current state of
knowledge on how the frequencies on the
different levels of a variable are distributed.
The computation calls for data on either
nominal or ordinal level.
Chi-Square Goodness of Fit Test

25. Whether the chi-Square test of association is
used to find or correlated or not 2 variables
are associated on dependent or correlated
with each other and appropriate for nominal
ordinal types of data.
Nominal refers to very small data compared to
usual expectation while ordinal is expression
of succession in a series of order

26. Table /Graph
Presentation

27. a) The Table Number which is usually written in Arabic is
placed at the center, above the title numbered
consecutively throughout the research study.
b) Table Title describes the content or the data presented in
the table written below the table number. It is
represented in V-shaped or inverted pyramid form.
c) The Content contains all the information written in the
rows and columns.
d) A Head Note which is written below the title and is
usually enclosed in parentheses, explaining anything
that is not clear in the table.
e) A Source Note is generally written below the table or
head note that indicates the origin of data presented in
the table.

28. After the table is completed, the data recorded in the table are described and
presented in narrative form. The use of the following phrases will help in the
presentation of the data in the table.
1. Table_____shows...
2. Table_____lists the...
3. Table_____presents the...
Accountability Transparency Predictability Participation
Pearson
r/Biserial Sig
Pearson
r/ Biserial Sig
Pearson r/
Biserial Sig
Pearson r/
Biserial Sig
a. Date of
registration
-.472 .075 -.220 .432 -.324 .238 -.364 .183
b. employees .134 .635 .050 .858 .099 .725 .151 .592
c. members .301 .276 .130 .643 .250 .368 .342 .212
d. assets .192 .494 .073 .795 .167 .553 .243 .383
e. deposits .241 .386 .147 .600 .235 .399 .291 .292
f. Articlea -.176 .530 -.183 .515 -.257 .354 -.273 .325
Table 30
Correlations
a- This is dichotomous so point biserial was used

29. The important parts of the graph are the following:
a. Number – usually written as “Figure 1”, “Figure
2” etc., placed at the bottom of the graph.
b. Title – usually written above the graph describing
what the graph is all about.
c. Scale – indicates the length or height unit
representing a certain amount of the variables
being used in the graph.
d. Source – written below the chart to indicate the
origin of the graph.
The graph is described and presented or explained
in narrative form.

30. The following phrases
may be helpful in the
presentation and
description of the
graphs/phrases.
1. “Figure___is a...”
2. “Figure___shows the...”
3. “Figure___presents...”
4. “Shown in
Figure___are...”
5. “From Figure___it can
be seen that...”

31. The estimates of poverty
incidence in the Philippines per
province as of 2012. The national
average is 22.3%, virtually
unchanged from 2006's 23.4%

32. Knowledge about computer technology will
enable the researcher to produce creative and
interesting graphical presentations of the needed
results of the study. Using the internet explorer and
searching for “create a graph” can take the
researcher to this website which explains, illustrates
and gives easy-to-follow instructions on how to
produce the needed graph/s innovative presentation
of the data in the study.

34. Textual Presentation uses statements or sentences
with numerals in order to describe the data purposely
to invite attention to some significant data. The
presentation usually precedes the table or the graph
with the mixture of words and numbers in the form of
statements. Textual presentation should be complete
and detailed as possible and followed by an analysis
and interpretation of the implication of the data. It
includes comparative statements on the findings of
other related studies to make some generalizations.
This presentation always strengthened by the related
studies and literature presented in Chapter 2, by the
results of the interviews and observations done by the
research.

35. Rivera, Jr M and Rivera Roela Victoria Practical Guide to Thesis
and Dissertation Katha Publishing Inc. Quezon City, Philippines
(2007)
Chong-ho Yu, Ph.Ds Parametric Tests
http://www.creative-wisdom.com/teaching/WBI/parametric_test.shtml
accessed Sept 7,2014
Judy Tyler/ What Are Parametric and Nonparametric Tests?
http://www.ehow.com/info_8574813_parametric-
nonparametric-tests.html. Accessed Sept 7, 2014