1) The document discusses techniques for collecting quantitative data, including observation, surveys using questionnaires and interviews, experiments, content analysis, and psychological and physiological measures. It emphasizes the importance of using accurate and appropriate data collection techniques. 2) Hypothesis testing is covered, including the types of hypotheses (descriptive and statistical), testing for significance, and the steps involved which include stating the problem, hypothesis, statistical tool, analyzing data, and interpreting results to draw conclusions. 3) The differences between one-tailed and two-tailed hypothesis tests are explained. One-tailed tests specify the direction of the relationship while two-tailed tests do not. The level of significance and determining critical values are also important aspects of hypothesis

1. PRACTICAL
RESEARCH
A Presentation by: GROUP 6

2. Collecting data &
the Research
Hypothesis
LESSON 1 : Quantitative data colletion
Techniques in collecting quantitative data
Measurements scales for quantitative data
Hypothesis testing
T-table
Welcome

3. CONTENTS
1
2
Quantitative data collection
Hypothesis testing

4. Quantitative data
collection
Clean Introduction Minimal

5. Techniques in collecting
quantitative data
Collecting data is one major component of
any type of research .undermining its
importance would result in the production of
inaccurate data suffecient to render your
research study invalid hence in collecting
quantitative data ,stress is given to the
accuracy or appropriateness of your data-
gathering technique as well as of the right
instrument to collect the data.

6. 1. Observation
using your sense organs,you gather facts or information about
people,things,places,events,and so on by watching and listening to them; then
record the results of the fuctioning of your eyes and ears .expressing these
sensory experiences to quantitative data,you record them with the use of numbers
For instance ,watching patients lining up at a medical clinic,instead of centering
your eyes on the looks of the people you focus your attention on
number,wieght,and hieght of every patient standing up at the door of medical
clinic.

7. Observation
according to cristobal & cristobal [2016] there are two types of observation [1]
structured ; the researcher uses a checklist as a data collection tool .the checklist
as a data collection tool. This checklist specifies expected behavoirs of interest
and the researcher records the frequency of the occurrences of these behavoirs
.and [2] unstructured the researcher observes things as they happen.the
researcher conducts the observation without any preconceived ideas about what
will be observed.

8. 1. SURVEY –is a data-gathering technique that makes
you obtain facts or information about the subject or
object of your research through data gathering
instruments of review or questionnaire .this is the most
popular data-gathering technique in quantitative and
qualitative researcher studies for the researchers are
free to use not just one survey I n s t r u m e n t but
also these two following data-gathering instrument [1]
questionnaire
it is a witten or printed form containing the objectives
of the study. This is administered on the respondents to
elicit the required data based on the procedures made
by the researcher.

9. It is the most convenient and also the easies way
gather data [2] Interview it involves face-to-face
contacts between the inerviewer and interviewee. In
research the interviewer is the researcher and the
interviwee is the client or respondent.sometimes a
researcher interviews to confirm data obtained data
from the questionnaire however ,interviews alone woul
be enough for gathering data ,depending on the nature
of the problem. This data gathering technique is quite
laborious especially when the population is large.the
reseacher should personally

10. 3. Experiment
An expirement is a scientific method of collecting
data where by you give the subject sort of
treatment or condition then results to find out the
manner by which the treatment affected the
subject to discover the reasons behind the effects
of such treatment on the subjects .
This qualitative data-gathering technique aims at
manipulating or controlling condtitions to show
Condition or treatment has effect on the subjects
and to determine how much condition and giving
the subjects post-test to determine how how much
condition or treatment operates or functions to
yield a certain outcome .

11. cccvff
3. Content analysis
Content AnalysisContent analysis is another quantitative
data-collection technique that makes you search through
several oral or written forms of communication to find
answers to your research questions. This data-collection is
not only for examining printed materials like photographs,
films, video tapes, paintings, drawings, and the like. Here,
you focus your study on a single subject or on two entities
to determine their comparative features. Any content
analysis you want to do is preceded by a thorough
understanding of your research questions because these are
the questions to guide you in determining which aspect of
the content of the communication should you focus on to
find the answers to the main problem of your the collection
of physical data from the subjects . It is considered for more
accurate and object objective than other data collection
research..

12. 3. Pyschological measures
Physiological MeasureThe techniques
applied for physiological measure
involves ethods. However, skills and
expertise are needed to enable the
researcher to use and manipulate the
measurement devices. Examples used
to collect physiological measures are
the following their thermometer,
stethoscope, and weighing scale.

13. 3. Pyschological test
.. Psychological TestsThese include personality
inventories and projective techniques .
Personality inventories are self-reported
measures that assess the differences in
personality traits, needs or values of people.
They involve gathering information from a
person through questions or statements that
require responses or reactions. Examples of
these are the Minnesota MultiphasicPersonality
Inventory (MMPI) and the Edwards

14. Measurements scales for
quantitative data
In quantitative research, measurements
of data expressed in numerical forms in
scale or one that consists series of
graduated quantities,values
,degree,numbers and so on .

15. EXAMPLES;
In quantitative research, measurements of data expressed in
numerical forms in scale or one that consists series of graduated
quantities,values ,degree,numbers and so on .
Nominal Scale – categorizing people based on gender,
religion, positon, etc.(one point for each)
Religion- Catholic, Buddhist, Protestant, Muslim
Gender- male, female
Positon- CEO, Vice-president, director, Manager,
Master teacher
2. Ordinal Scale – ranking or arranging the classifed
variables to determine who should

16. be the 1st, 2nd, 3rd,4th, etc. in the group
3. Interval Scale – showing equal intervals or diferences
of people’s views or attudes
3. Interval Scale – showing equal intervals or diferences
of people’s views or attudes
like this one example of a scale called Likert Attude Scale.
Reading is important
(Strongly Agree, Agree, Undecided, Disagree, Strongly
Disagree)
How ofen does your professor come late?
(Always, Most of the tme, Sometmes, Rarely, Never)
How would you rate your teacher’s performance?
(Very Poor, Poor , F air, Good, Excellent)
4. Rato Scale – ratng something from zero to a certain
point. Performance in Practcal research subject-a grade of
89%(from 0 to 100%)

17. LESSON 2
Hypothesis
testing
A Presentation by: GROUP 6

18. hypotheses
A hypothesis is a working guide in research, the expected outcome of the
study.Hypothesis is defined as an “educated guess”. Hypotheses are based on the
statement of the problem and sub_x0002_problems. The number of sub-problems is also
thenumber of hypotheses in the study( Vizcarra,2003). According to Varaceros(2016), it
is a tentative explanation or an answer to a question about variables, their relationships,
and other facts involved in the research.

19. Hypotheses
There are two types of hypotheses- descriptive and statistical .a
descriptive hypothesis is for descriptive questions while a
statistical hypothesis is for questions on the relationship or
differences of the data obtaind in descriptive questions
[vizcarra,2003]
Table shows the example 12.1 shows the examples of hypotheses
and sub-problems

21. Testing of significance
Inferential parametric and nonparametric statistics are used to answer problems regarding
significant relationships or differences between the variables involved in the study. In the
measures of correlation, statistics like Pearson r and it’s other special cases (point biserial , phi ,
Spearman, etc.), use testing of Significance after determining the magnitude of association
between the variables considered to be known if there is a significant relationship among the
variables in the study. Before their application, the problems are stated in be it in a null hypothesis
or an alternative. The hypothesis will serve as a guide in drawing decisions or conclusions about
the given research problem. The null hypothesis (Ho) is stated in a negative statement like there
is no significant relationship, difference, or correlation. It is subjected to testing in which the
decision is either to accept or reject it.

22. Examples ;
Ho: There is no significant relationship between the reasons for using
alternative medicine and the level of comfort of the patients.
Ho: There is no significant difference between the effects of animal
manure and urea on the height of plant, color of the leaves and the yield
produced by plant.
Ho: Liberal arts and education students do not differ significantly on
their level of assertiveness

23. After the data is gathered..
After the data is gathered and computed according to the appropriate statistical tool, the
hypothesis is accepted or rejected. This will be the basis for drawing decisions or
conclusions
about the problem. The following is a step-by-step process in hypothesis-testing:
1.State the specific sub-problem of the study.
2.state the research hypothesis (null or alternative hypothesis).
3.Determine the statistical tool appropriate to the problem.
4.Gather, tabulate, and analyze the data.
5.Compute the data based on the statistical tool needed in the study or problem

24. 6.Determine the degree of freedom. The method of locating the
degree of freedom depends on the type of
test.
7. Determine the level of significance and locate the
critical value.
8. Compare the computed value with the critical value.
9. Interpret the results, and
10. State the conclusions.

25. Write a closing statementIn almost all parametric and non-parametric tests, the degree of freedom is the guide
in determining the critical value as well as the level of significance. The method of locating degree
of freedom depends on the type of statistical to be used. The level of significance or confidence
level in within the control of the researcher. The .05 level of significance allows a 5% error or 95%
degree of accuracy, while .01 allows a 1% degree of confidence or accuracy. In social research, .05
is advisable while .01 is allowed in experimental studies. The level of acceptance may either be
on one-tailed or two-tailed tests (Vizcarra, 2003).
The word tails refer to the ends of the statistical distribution such as the familiar bell-shaped
normal curve that is used to test a hypothesis. One tail represents a positive effect or association.
The other , a negative effect. A negative effect. A one-tailed hypothesis is used if the sample size
is small but for bigger sample size it is better to use two-tailed hypothesis (Chico & Matira, 2016) or call-to-
action here.

26. The one-tailed test
The one-tailed test is to be used in a directional
hypothesis. It only uses one tail of the sampling
distribution. It specifies the direction of the
association between the independent and the
dependent variables.

27. EXAMPLE;
Ha: Plants treated with animal manure fertilizer will have higher percentage of yields.
Ha: The types of promotional campaigns positively affect the level of patronage of
customers
Ha: The higher the intake of fiber, the lower is the weight of the physical mass.
In a one-tailed test, you have two options for the null and alternative hypothesis, which
correspond to where you place the critical region. You can choose either of the following
sets of
generic hypotheses
>Null : The effect is less than or equal to zero
> Alternative: The effect is greater than zclosing statement or call-to-action here .

28. Two-Tailed Test illustration
Two-tailed Test Illustration
The graph displays a sampling distribution for t-values. Two shaded regions cover the two-tails of the distribution .05 level of
significance is illustrated as:

29. Examples
For example, the computed t value is .80, and
the critical value at .05 level of significance
with
20 degrees of freedom is 2.086. This data
explains that there is no significant
difference.

30. Non- directional hypothesis
Non-directional hypothesis does not stipulate the direction of the relationship between
the independent and dependent variables.
Examples:
Ha: Plants treated with animal manure will have more or less yield.
Ha: The sources of stress are related to the different coping mechanisms among teachers.
Ha: The number of hours spent in reviewing is associated with the level of performance
in periodical exams.You will use your t value in a Hypothesis to compare against a calculated t
score. This helps you to decide if you should support or reject a null hypothesis. Table 12.2 shows
the Critical Values of the Distribution.

31. Table 1 2.2 critical values of the t distribution
Write a closing statement or call-to-action here.

32. decision errors in hypothesis testing

33. Table 1 2.3 summary of four possible
outcomes in testing hypothesis
Write a closing statement or call-to-action here.

34. THANKYOUU!!!