o
POPULATION
-
Entire aggregation of the case where a
researcher is interested
Often, it is not feasible to include all
the members of the population in
research
o
A homogenous mixture is
that mixture in which the
components mix with each other,
and its composition is uniform
throughout the solution.
A heterogenous mixture is that
mixture in which the composition
is not uniform throughout, and
different components are observed.
SAMPLING
-
Process of selecting the sample or a
portion of the population
NON-PROBABILITY SAMPLING
-
SAMPLE
-
Subset of the population elements
REPRESENTATIVENESS
-
How well the sample represents the
population
An important characteristic of a sample
must be considered
REPRESENTATIVE SAMPLE
-
-
-
There is a form of bias in the selection
of sample
There is no assurance that each element
in the population has the same equal
chance of being selected
There is no assurance that each that
each unit in the population is properly
represented
The findings are limited to the sample
PROBABILITY SAMPLING
-
One whose key characteristics closely
approximate those of the population
-
There is random selection of sample
There is greater representation in each
unit in the population
Each element in the population has the
same equal chance of being selected as
a sample
The findings can be generalized to the
population
SAMPLING ERROR
-
-
-
Result to overrepresentation or
underrepresentation of some segment
of the population
Occurs if the selection of the sample
does not take place in the way it was
planned
Smaller sample size = bigger chance
of sampling error
The appropriate sample size also
depends in the heterogeneity
(Heterogenous group – bigger size) and
homogeneity (Homogenous group –
smaller size)
TYPES OF NON-PROBABILITY SAMPLING
Convenience Sampling
-
Selection of samples based on the
convenience of the researcher
Involves the most conveniently
available people to participate
Sometimes called Accidental Sampling
Snowball Sampling
-
-
“Referral System”
“Chain System”
Initial sample members are asked to
refer other people who meet the
criteria required by the researcher
People who share the same traits or
experiences know each other
Useful for participants who are hard to
find
Purposive Sampling
-
The selection of the sample is based on
the selective judgment of the researcher
Judgmental Sampling
Sets a criterion that is relevant to the
topic under study
Quota Sampling
-
-
Researcher identifies population
sections or strata and decide how many
participants are required from each
section
Allow better representation from each
unit in the population
Has requirements/criterion
unordered number in electing name
from the list.
Systematic Random Sampling
-
-
Uses the Kth interval formula
k=N/n
o k = sampling interval
o n = desired sample
o N = Population
Sampling interval is the standard
distance between elements chosen for
the sample
Stratified Random Sampling
-
-
-
Population is divided into subgroups or
strata
Just like quota sampling, stratification
is based on variables related to the
study
After stratification, appropriate number
of elements are selected from each
stratum at random
No requirements/criterion
Cluster Sampling
-
Useful when the population is large
and widely dispersed
Sampling is done in several stages
Is also called multi-stage sampling
The resulting design is described in
terms of the number of stages
TYPES OF PROBABILITY SAMPLING
Simple Random Sampling
-
-
-
Each member of the population has the
same equal chance of being selected as
a sample
Based on chance
Two methods:
Fishbowl – write each name on a card
and choose cards through a pure chance
selection.
Number Generated – known as
sampling frame; give a number to
member and then use randomized or
SAMPLE SIZE IN QUANTITATIVE STUDIES
In a quantitative study, the sample size is an
important aspect that must be carefully
considered. There are existing procedures that
can be used to estimate the appropriate sample
size, but statistical knowledge is required to
understand this procedure. There are no fixed
rules nor simple formulas that can tell us how
large a sample should be when conducting
quantitative studies, but there are
recommendations:
1. The larger the sample size, the better.
Smaller sample size tends to produce less
accurate estimates.
2. If the sample is homogenous, a small
sample size may be adequate. Homogeneity
means that the population elements were all
identical with respect to key attributes.
3. If there is reason to expect that the
independent and dependent variables will be
strongly related, then a relatively small sample
should be adequate to demonstrate the
relationship statistically.
4. For studies that will take a long time to
finish (longitudinal studies) researchers
should factor in anticipated of subjects over
time. Therefore, a larger sample size is
necessary. So, in case there will be a high
attrition or dropouts from the study, the
sample size will still be adequate.
IMPLEMENTING A SAMPLING PLAN IN
QUANTITATIVE STUDIES
Steps in Sampling Quantitative Studies ( Polit
and Beck, 2007) The steps to be undertaken in
drawing a sample vary somewhat from one
sampling design to the next, but a general
outline of procedures can be described.
1. Identify the population.
2. Specify the eligibility criteria.
3. Specify the sampling plan.
4. Recruit the sample.
INTRODUCTION TO STATISTICS
-
In statistics, before the main analysis,
the data are assuming to meet all the
requirements that a data should have, or a data
should undergo first some preliminary checking
to test if a certain statistical technique is
appropriate for the analysis
When performing a hypothesis test, a p
(probability) value helps to determine
the significance of the results. In
decision making, a p-value that has a
value which is less than 0.05 (a)
indicates significance.
RELATIONSHIP
HYPOTHESIS
NULL HYPOTHESIS “HO”
-
NEGATIVE RELATIONSHIP
-
A type of hypothesis which states that
there is no statistical
significance/relationship or effect
existed between two or more groups.
ALTERNATIVE HYPOTHESIS “Ha”
-
Also known as the research hypothesis,
it is the proposed hypothesis or
expected outcome of the research.
Also called as inverse relationship.
Aside from the test statistic having a
negative value, the correlation between
two variables is said to have a
negative/inverse relationship as the
amount of one variable increases, the
level of another variable goes down.
POSITIVE RELATIONSHIP
-
Or a direct relationship whereas the
amount of one variable increases, the
amount of a second variable also
increases.
LINEAR RELATIONSHIP
-
Statistical term used to describe the
relationship between two sets of data.
SAMPLE
-
NONPARAMETRIC STATISTICS
-
Type of statistics that should be use
when the data violated the requirements
for a parametric test (i.e., if the data are
not
*Normally Distributed; or if the
measurements of the data are on an
ordinal scale etc.)
Is a relatively small subset of people,
objects, groups, or events that is
selected from the population
TEST STATISTIC
-
It is considered as numerical summary
of a data – set that reduces the data to
one value that can be used to perform a
hypothesis test.
PARAMETRIC STATISTICS
-
-
P value
When the data are in interval level and
are *Normally Distributed, parametric
statistics is a type of statistics that
should be use.
Always superior to Nonparametric
counterpart for decidedly Normal
population.
VARIABLES
-
In research, it is a logical set of
attributes, factors that can be controlled
or change in experiment/research
TYPES OF VARIABLES
INDEPENDENT VARIABLE
-
It is the variable that the researchers
have control over, can be choose and
manipulated. Usually, it is what the
researcher think will affect the
dependent variable
-
DEPENDENT VARIABLE
-
Represented as “Y”, it is the response
variable or the presumed effect in a
study which can be measured in
interval or nominal scale.
-
RATIO SCALES
-
DIFFERENTIAL STATISTICS
-
It is the statistical procedures that the
researcher uses
To describe the population, they are
studying
INFFERENTIAL STATISTICS
-
It is the statistics that is concerned with
making predictions or inference
About a population from observations
and analysis using a sample and can
generalize it to the larger population
that the sample represents
Distances between data elements can be
determined at the interval level of
measurement. In other words, the
interval is the same. Oftentimes in
psychology things are measured by a
Likert scale in which one rates a
statement (often by how much they
agree with the statement).
Arbitrary zero (the starting point) (can
be negative/positive) (temperature)
-
Possess the advantage of all other
measurement scales for it is the only
measurement that can be analyzed with
the widest range of statistical methods
which makes it as the highest form of
measurement precision. It has all the
components of an interval scale but
here, the zero point is meaningful and
means the absence of whatever it is
measuring. Common examples are age,
height, weight, test, and heart rate.
Absolute Zero
NONMETRIC MEASUREMENT SCALES
MEASUREMENT SCALES
To accurately represent the concept of
interest, measurement of the variables is
essential and is instrumental in the selection of
the appropriate statistical method of analysis.
Based on the types of attributes or
characteristics the data represent, it can be
classified into one of two categories: nonmetric
and metric.
-
NOMINAL SCALES
-
METRIC MEASUREMENT SCALES
-
Data that are metrically are used when
subjects differ in amount or degree on a
particular attribute. Metric data reflects
relative quantity or degree and are
appropriate for attributes involving
amount or magnitude, i.e., Level of
satisfaction
INTERVAL SCALES
Describes differences in type or kind by
indicating the presence or absence of a
characteristics or property.
Nominal level (or categorical) data
refers to data that can only be put into
groups. It only represents categories or
classes and do not imply amounts of an
attributes or characteristics. Commonly
used examples of nominally scaled data
include many demographic attributes
(e.g., gender, religion, occupation).
ORDINAL SCALES
-
In the case of ordinal scales, variables
can be ordered or ranked in relation to
the amount of the attribute possessed,
Ordinal scales provide no measure of
the actual amount or magnitude in
absolute terms, only the order of the
values.
-
The researcher knows the order, but not
the amount of differences between the
values
SAMPLING
-
Method
From one population you will get
representatives on the in the way of
your sampling technique
MEASUREMENT OF SCALES
METRIC
-
INTERVAL – Arbitrary zero – possible
negative values (temperature, Likert
scale)
POPULATION
-
Entire aggregation
RATIO – Absolute zero – NO negative
values (test scores, salary)
SAMPLE
-
Representative
NONMETRIC
-
Equal chances
NOMINAL – categories
SIMPLE – Fish-bowl method
STRATIFIED – group/target number of
requirements
SYSTEMATIC – kth interval (n/N)
STATISTICAL ANALYSIS
-
CLUSTER – multistage sampling, the
population is too big (levels)
NON-PROBABILITY
-
Not Equal – biased
PURPOSIVE – Set of criteria
QOUTA – group/target number of
requirements/requirements
SNOWBALL – referral, chain system
Categories/ranking
ORDINAL – ranking, orders (birth
order, academic awards)
PROBABILITY SAMPLING TECHNIQUE
-
Specific values
What to use as statistical analysis based
on the group, measurement of scale,
etc.
CORRELATION
-
Measuring 2 Continuous Variables if
there is a relationship in the two
variables.
o Continuous Variable – both
variables are measurable
o Example:
ALLOWANCE
MOTIVATION
CONVENIENCE –
proximity/accessibility
Pearson’s Product Moment
Correlation
(PEARSON’S R)
COMPARATIVE
-
INDEPENDENT T-TEST
o Comparing two groups
Example:
Measuring the Motivation of:
1.
2.
Modular Students
Online Class students (OCR)
o
o
If P value is lower than (<)
significant value = reject Ho
If the P value is higher than (>)
significant value = accept Ho
Example:
P value – 0.001
Significance – 0.05
-
ONE-WAY ANOVA
o Comparing 3 or more groups
o
Reject Null Hypothesis (Ho) –
there is a significance
Example:
Comparing the 4 Strands in
SHS: STEM, HUMMS, GAS, ABM
P value – 0.73
Significance – 0.05
o
-
PRE-TEST & POST-TEST
o 1 group – experimentation and
want to know if the group will
improve
Example: Study about effects of
diet – if the weight will decrease?
o
o
The other test already has
experiment and the other test
have none
PAIRED T-TEST –
INTERVENTION (experiment,
pre-test, post-test)
MANUSCRIPT
RESEARCH LOCALE
-
Geographic
RESEARCH DESIGN
-
Approach/comparative or correlation
RESEARCH ETHICS
-
Ethical, citations, consent, etc.
RESEARCH INSTRUMENTS
-
Tools
ANALYSIS
-
Comparing the P VALUE and
SIGNIFICANT VALUE
Accept Null Hypothesis (Ho) –
there is no significance