Instructor: Siti Nor Binti Yaacob
Department of Human Development and Family
Studies
Faculty of Human Ecology
Universiti Putra Malaysia
Contact #: 012-284-1844
Email: sitinor@putra.upm.edu.my
Population and Sampling


Probability Sampling
Non-probability Sampling
Definition
A group of potential participants to
whom you want to generalize the
results of a study.
Generalize :
The key to a successful study;
because it is only the results that
can be generalized from a sample
to a population; that research
results have meaning beyond the
limited setting.
Not generalize : The sample
selected is not an accurate
representation of the
population.
 Population
the a group of people or things you are
interested in.
 Census is
a measurement of all the units in the
population
 PP
= number that results from measuring all the units in
the population.
 Statistic = number that results from measuring all the
units in the sample; statistics from samples
are used to estimate PP.
 SF
= specific data from which sample is drawn,
e.g., a phone book.
 UA
= type of object of interest, e.g., arsons, fire
departments, firefighters.
 Is
a list or quasi list of the members of a
population.
 Resource used in the selection of a sample.
 A sample’s representativeness depends directly on
the extent to which a sampling frame contains all
the members of the total population that the
sample is intented to represent.
The
data for this research were obtained
from a random sample of parents of
children in the third grade in
government primary schools in Selangor.
Definition :
Sample is a subset of the population.
 Good sampling
: include maximizing the
degree to which this selected group
represent the population.
POPULATION
Sample
Sample
Types of sampling
Probability sampling
2. Non probability
sampling
1.




Allows use of statistics, tests hypotheses.
Can estimate population parameter.
Eliminates bias.
Must have random selections of units.
Exploratory
research, generates hypotheses.
Population parameters not of interests.
Adequacy of sample unknown.
Cheaper, easier, quicker to carry out.
Cant generalized findings.
Non-representative.
A
type of sampling where the
likelihood of any one member of
the population being selected is
known.
Commonly
used because the
selection of participants is
determined by chance.
 e.g., if there are 4,500 students in
the Faculty of Human Ecology, and
if there are 1,000 seniors, the odds
of selecting one senior as part of
the sample is 1000:4,500 or 0.22.
 Where
the likelihood of selecting any one
member from the population or where the
probability of selecting a single individual is
not known.
 e.g., if you do not know how many
seniors in the Faculty of Human
Ecology, the likelihood of anyone
being selected cannot be
computed.
1.
2.
3.
4.
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
1. Simple
Random Sampling
When the population’s members
are similar to one another.
http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
Adv:
Ensures a high degree of
representativeness
Disadv:
Time consuming and tedious
 Let's
assume that we have a population of 185
students and each student has been assigned
a number from 1 to 185. Suppose we wish to
sample 5 students (although we would
normally sample more, we will use 5 for this
example).
 Since
we have a population of 185 and 185 is a
three digit number, we need to use the first
three digits of the numbers listed on the chart.
 We
close our eyes and randomly point to a spot on
the chart. For this example, we will assume that
we selected 20631 in the first column.
 We
interpret that number as 206 (first three
digits). Since we don't have a member of our
population with that number, we go to the next
number 899 (89990). Once again we don't have
someone with that number, so we continue at the
top of the next column.
 As
we work down the column, we find that the first
number to match our population is 100 (actually
10005 on the chart). Student number 100 would be
in our sample. Continuing down the chart, we see
that the other four subjects in our sample would be
students 049, 082, 153, and 005.
http://www.google.com/imgres?imgurl=http://www.gifted.uconn.edu/sieg
le/research/Samples/RANTBLE.JPG&imgrefurl
2. Systematic Sampling
When the population’s members
are similar to one another.
http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
Adv :

Ensures a high degree of
representativeness; no need to use a
table of random numbers.
Disadv :

Less truly random than simple
random sampling
3. Stratified Random Sampling
When the population is heterogeneous in
nature and contains several different groups.
http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
Adv :
 Ensures a high degree of
representativeness of all the strata
in the population.
Disadv :
 Time consuming and tedious
 Proportionate
SRM
 Non-Proportionate SRM
 Sampel
selected is in proportion to the size of each
stratum in the population
 Population
= 100
 Layer 1 = 40 males
 Layer 2 = 60 females
 For a sample size of 10, you will take 4 males + 6
females.
 Selection of
sample is not according to size of stratum
in the population
 Population
= 100
 Layer 1 = 40 males
 Layer 2 = 60 females
 For a sample size of 10, you will take 5 males + 5
females.
4. Cluster
Sampling
When the population consist of units rather
than individuals.
http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
Adv :

Easy and convenient
Disadv :

Possibility that members of units are
different from one another,
decreasing the sampling’s
effectiveness
1.
2.
3.
4.
Convenience Sampling
Quota sampling
Purposive Sampling
Snowball sampling
1. Convenience Sampling
When the sample is captive.
 Adv :

convenient and inexpensive
 Disadv :

results in questionable representativeness.
2. Quota sampling
When strata are present, and stratified,
sampling is not possible

Adv :


Ensures some degree of representativeness of all the strata
in the population
Disadv :

Results in questionable representativeness
3. Purposive Sampling

Researcher uses own judgment in the selection of
sample members

Sometimes called a judgmental sample.
4. Snowball
sampling
A technique often used in rare
populations; each subject interviewed
is asked to identify others.
 Lack
of fit between the sample and the
population.
 The
difference between the characteristics of
the sample and the characteristics of the
population from which the sample was
selected.
Reducing sampling
error is the major
goal of any selection technique.
Larger
sample, lower sampling error.





How big?
Depends on type of research design.
Desired confidence level of results.
Amount of accuracy wanted.
Characteristics of population of interest.
 Big
enough to answer research question.
 But not so big that the process of sampling becomes
uneconomical.
 Heterogeneous sample
= bigger size
 Homogeneous sample = smaller size
General
Rule of Thumb
30 participants/ respondents in
each group.
1.
2.
Larger sample, smaller sampling error, better
representativeness.
If using several subgroups, starts with large
enough subjects to account for the eventual
breaking down of subject groups.
3.
4.
If mailing out surveys or
questionnaires, increase sample
size by 40-50% to account for lost
mails or uncooperative subjects.
Big is good, but appropriate is
better.
 Students will
Lecture 8.
discuss and state what they have learned in
 Procedure for
assigning symbols, letters,
or numbers to empirical properties of
variables according to rules.
 Difficulty in
measuring concepts directly (e.g.,
academic achievement)
 Usually measure indicators
of concepts (e.g.,
CGPA)
 Level of
measurement determines the type of
statistical analysis.
 The level of
measurement you use depends on
how you want to measure an outcome.
 Nominal
 Ordinal
 Interval
 Ratio
 Latin
word nomin (name)
 Variable categorical in nature
 Differ in quality not quantity (numbers have no
meaning only label)
 Characterizes
observation into one (and only
one) category  mutually exclusive
 Solely qualitative
 No obsolute zero (0)
 Matematical operation not possible.
 Describes
variables that can be ordered along
some type of continuum.
 Not only categories, order as well.
 Ranking according to various outcomes, e.g.,
big & little.
 No obsolute ‘0’, only relative position; e.g., Zul
is taller than Sheereen and Sheereen is taller
than Rozumah (no information on how much
taller).
 Matematical operation not possible.
Latin word intervalum (spaces between walls).
 Describes variables that have equal intervals btw
them.
 Allow us to determine the difference btw points along
the same type of continuum (e.g., the difference btw
300 and 400 is the same as the difference btw 700 and
800; i.e., 100).
 0 is arbitrary (subjective, temporary).
 Simple matematical operation.
 More precise & convey > info than nominal & ordinal;
but must be cautious in interpreting.

 Latin
word ratio (calculation).
 Describes variables that have equal intervals btw
them & have absolute 0.
 Most precise.
 Complex matematical operation.
 Highest level of measurement.
1. Nominal
2. Ordinal
3. Ratio
A.
B.
4. Interval
C.
D.
Have a true zero
(highest level).
Categorical in
nature (lowest
level)
Have equidistant
points along some
underlying
continuum.
Are ranked.
TYPES OF
MEASUREMENT
ON HEIGHT
1.
2.
3.
4.
Nominal
Ordinal
Ratio
Interval
A.
B.
C.
d.
A is 5 inches taller than B
(know precise difference).
Precise height measured on
a scale with true zero.
Categorize people into A
and B (people different in
height).
Tall and Short (have some
meaning, but nature of
difference is not known).
 Nominal
level variables are categorical in nature
(lowest level)
 Ordinal -- are ranked.
 Interval -- have equidistant points along some
underlying continuum.
 Ratio -- have a true zero (highest level).
Reliability and validity are the hallmarks of good
measurement.
Assessments tools must be reliable and valid,
otherwise the research hypothesis may be
incorrectly rejected.
 Reliability
is a practical measure of how consistent and
stable a measurement instrument or a test might be.
 Reliability
is often measured using the correlation
coefficient.
 Dependency
 Consistency
 Stablility
 Trustworthiness
 Predictability
 Faithfulness
1.
2.
3.
4.
Test-retest
Parallel forms
Inter-rater
Internal consistency
A
measure of stability.
 Examines consistency over time.
 Administer the same test/measure at two
different times to the same group of participants.
 Coefficient: rtest1.test2
A
measure of equivalence.
 Examines consistency between forms.
 Administer different forms of the same test to the
same group of participants.
 Coefficient: rform1.form2
A
measure of agreement.
 Examines consistency across raters.
 Have two raters, rate behaviors and determine the
amount of agreement between them.
 Coefficient: % of agreement.
A
measure of consistently each item measures
the same underlying construct.
 Examines reliability within a particular set of
item.
 Correlate performance on each item with
overall performance across participants.
 Coefficient: Chronbach’s alpha
 Is the
quality of a test doing what it is designed to
do.
 The test or
instrument you are using actually
measures what you need to have measured.
 Truthfulness,
 Accuracy
 Authenticity
 Genuineness
 soundness
1.
2.
Content
Criterion
i.
ii.
3.
Concurrent
Predictive
Construct
A
measure of how well the items represent the entire
universe of items
 Established by
asking expert if the items assess what
you want them to.
 History
test = test items ask questions on history not
Science.
i.
Concurrent validity
 A measure of how well a test estimates a
criterion.

Established by selecting a criterion and
correlate scores on the test with scores on
ther criterion in the present.

Good student = test result + reports by
lecturers.
ii. Predictive Validity

A measure of how well a test predicts a criterion.

Select a criterion and correlates scores on the test
with scores on the criterion in the future.

High merit on STPM/Diploma = Score high CGPA.

Pass driving test = Good driver.

A measure of how well a test assesses some
underlying construct.

Assess the underlying construct on which the test is
based and correlate these scores with the scores.

Theoretically and practically sound.

Intelligence test actually measures intelligence.
A
test can be reliable without being valid but
the reverse is not true.
 A test can be reliable, but not valid, but a test cannot
be valid without first being reliable.


Reliablity is a necessary, but not sufficient, condition of
validity.
You are answering questions on simple addition, but we
called it spelling test! Obviously it is not a test on spelling 
lack of validity, does not affect reliability.
Data Collection
Methods In Information
Gathering





Observation
Interview
Questionnaire
 Gathering information
phenomenon.
about a situation, problem or
1.
Secondary Data

2.
Information required is already available & need only
be extracted.
Primary Data

Information must be collected.
 Documents
 Government publications
 Earlier research
 Census
 Personal records
1.
Observation


2.
Participant
Non-participant
Interviewing
Structured
 Unstructured

3.
Questionnaire


Mailed questionnaire
Collective questionnaire
 Is
a purposeful, systematic, and selective way of
watching and listening to an interaction or
phenomenon as it takes place.
 Appropriate in situations where full and/or
accurate information cannot be elicited by
questioning.
1.
2.
Participant observation
Non-participant observation
 Researcher participates in
the activities of the group
being observed in the same manner as its members,
with or without knowing that they are being
observed.
 Researcher does
not get involved in the activities
of the group but remains a passive observer,
watching, & listening to its activities and drawing
conclusions from this.
 Respondent may
be aware & change behavior.
 Observer bias.
 Interpretation btw
observer inconsistent.
 Possibility of incomplete observation and/or
recording.
1.
Natural

2.
Does not intervene.
Controlled

Introduce stimulus to observe reactions.
 Narrative
 Scales
 Categorical
recording
 Recording on mechanical devices
 Take
brief notes first
 Soon after makes detailed notes
 Adv: provides deep insight into the interaction.
 Disadv: observer bias & incomplete recording.
 Develop scale to
rate interactions or
phenomenon.
 Adv: quick, easy to record.
 Disadv: does not provide in-depth information
about interaction.
 Depend on
classification develop by researcher;
e.g. passive/active, etc.
 Adv: quick, easy to record.
 Disadv: does not provide in-depth information
about interaction.
 Observation recorded on
a video tape and then
analyzed.
 Adv: can watched it many times b4 making
conclusion; can invite expert to view to make right
conclusion.
 Disadv: respondent uncomfortable, or behave
differently.

Person-to-person interaction
with specific purpose.


Most common method.
2 types:
1.
2.
STRUCTURED
UNSTRUCTURED




Known as in-depth interview.
Use interview guide/framework; no specific set
questions.
+ spontaneous questions.
Can be conducted in …….
One-to-one
2. Group interview (focused group)
1.
 Use for
in-depth information.
 Or when lack of information.
 Flexibility on what to ask of a respondent; elicit
rich information.
 Thus, sometimes used to contruct structured
instrument.
 Disadv.:




No specific set question, comparability difficult.
Questions may keep changing; info at beginning may
be different from later.
Freedom may lead to interviewer bias.
More skill needed to use interview guide than
structured interview.
 Pre-determined
set questions in interview
schedule:


Same wording
Same order of questions
 Interview




schedule/research instrument:
Written list of questions
Open-ended/ closed
For use by interviewer
In person-to-person interaction (face-to-face, by
telephone, or by other electronic means)
 Adv:
provides uniform info, which ensures
comparability of data.
 Required fewer interviewing skills than
unstructured interviewing.
 Is
a written list of questions; answer recorded by
respondents.
 Respondent read the questions, interpret & write
down answers him/herself.
 Different from interview, where interviewer asks
qn & write respondents replies on interview
schedule.
Rules for questionnaire:
 Questions must be
clear & easy to understand.
 Layout is easy to read, pleasant to the eye, sequence
of qn easy to follow.
 Interactive style – as if someone talking to
respondent.
 Sensitive qn – prefaced with statement of explanation
(use different font for preface to distinguish them
from acual question).
Depends on:
 Nature

of investigation
Sensitive questions, questionnaire better.
 Geographical

Respondents scattered, use questionnaire – cheaper.
 Type

distribution of study population
of study
Illiterate, very young or very old, or handicapped – use
interview schedule.
Mailed questionnaire
1.
•
•
•
•
•
Send out to prospective rspdnt
Must have addresses
Prepaid self-address envelope
With covering letter (brief explanation of study,
indicate confidentiality & participation is voluntary, +
other impt qn).
A Major problem --- low response rate.
2. Collective questionnaire
•
•
•
•
•
Captive audience (e.g., students in lecture hall)
High response rate coz few will refuse.
Can explain purpose & importance of study face-toface + can clarify qn.
Quickest was of collecting data
Save money
3. Administration in public place
•Approach & request participation of
potential rspdnt
•More time consuming
•Adv same as collective qnn.
 Adv &
Disadv of Questionnaire
 Adv & Disadv of Interview
 Adv:
 Less expensive
 Greater anonymity
 Disadv:
 Limited application (only for those who can read &
write)
 Low response rate if mailed.
 Self-selecting bias (only those with good attitudes or
motivations will response; may not be
representative of study population).
 Spontaneous response not allowed for.
 Response to a question may be influenced by response
to other questions.
 Possible to consult others.
 A response cannot be supplemented with other
information.
Adv:
 More
appropriate for complex situations.
 Useful for collecting in-depth information.
 Information can be supplemented (from
observations of non-verbal reactions).
 Questions can be explained.
 Interviewing has a wider application.

Any type of population – children, illiterate, young &
old.
Disadv:
 Time-consuming & expensive.
 Quality of interaction can influence
quality of
data.
 Quality of interviewer can influence quality of
data.
 Quality of data vary when many interviewers are
used.
 Researcher may introduce his/her bias (e.g., in
framing the question).
 Interviewer may be biased (e.g., in the way of
questioning).
 Form
& wording of questions may affect type &
quality of information obtained.
 Types of question:
Open-ended
Close-ender
 Possible responses are
not given.
 Respondent writes the answer (for questionnaire)
 Interviewer record the respondents’ answers
(verbatim or summary)
 Useful for seeking opinions, attitudes or
perceptions.
 Possible answers
given.
 Respondent or interviewer tick the answer.
 Useful for eliciting factual information
Adv:
 Provide in-depth & wealth of info.
 Provide opportunity for respondent to express
their opinion, resulting in more variety of info.
 Allow respondents to express themselves freely;
eliminate the possibility of investigator bias.
Disadv:
 Analysis more difficult (must do content analysis in
order to classify the data).
 Some respondents may not be able to express
themselves, so information may be lost.
 Greater chance of interviewer bias.
Adv:
 Ready-made categories; help ensure info needed
is obtained.
 Easy to analyse.
Disadv:
 Info lacks depth & variety.
 Investigator bias – may list answer he/she is
interested in.
 Given response could
condition thinking of
respondents
 May create tendency among respondents and
interviewers to tick a category/ries without
thinking through the issue.
 Always use simple &
everyday language.
 Do not use ambiguous questions.
 Do not ask double-barrelled questions.
 Do not ask leading questions.
 Do not ask questions that are based on
presumptions.
 Is
anyone in your family having ‘HN1N1?
 Is difficult for you to be a student and a wife?
 Are you happy with your university?
 How often and how much time do you spend
visiting your lecturer?
 In your opinion, eating lemang with rendang or
peanut sauce is nice?
 Smoking
is bad, isn’t it?
 ‘Ponteng kuliah’ is bad, isn’t it?
 How many cigarettes do you smoke in a day?
 What handphone do you use?
Sources of Data:
 Government or semi-government publications
 Earlier research
 Personal records
 Mass-media
 Validity
& reliability
 Personal bias
 Availability of data
 Format
 Students will
state what they have learned in Lecture 10.
134




DATA ANALYSIS
PROCESSING DATA
Editing Data
Process for coding
135




DATA ANALYSIS
PROCESSING DATA
Editing Data
Process for coding
136
 Ways
to use/organize/manipulate data in order to
reach research conclusions.
137
1.
2.
3.
4.
EDITING DATA
CODING DATA
DEVELOPING A FRAME OF ANALYSIS
ANALYSING DATA
138
 Data
Cleaning
 Checking the completed instruments; to
identify
and minimize
errors
 incompleteness
 inconsistencies
 misclassification
 etc. (illegible writing)

139
2 Considerations for Coding:

Measurement of a variable (scale?, structure –
open/closed ended?).

Communication of findings about a variable
(measurement scale?, type of statisitical procedures?)
(e.g., Ratio scale – mean, mode, median)
140
 For
analysis using computer, data must be coded
in numerical values.
 The coding of raw data involves 4 steps:




Developing a code book (master-code book)
Pre-testing the book
Coding the data; and
Verifying the coded data.
141
 Develop
from beginning of research and
evolve continuously to end.
 Frame
of analysis:
Identify variable to analyse
 Determine method to analyse
 Determine cross-tabulations needed
 Determine which variable to combine for
constructing major concepts or develop indices
 Identify which variable for which statistical
procedures

142
143
1.
2.
3.
UNIVARIATE ANALYSIS
BIVARIATE ANALYSIS
MULTIVARIATE ANALYSIS
144
 Is
the examination of the distribution of cases
on only one variable at a time.



Distributions
Central tendency
Dispersion
 Can
be generated thro’ Descriptive statistics in the
SPSS.
 Purpose of univariate analysis is purely descriptive.
145
 The full
original data usually difficult to interpret.
 Data reduction is the process of summarizing the
original data to make them more manageable;
while maintaning the original data as much as
possible.
146
 Attribute of
each each case under study in terms
of the variable in question.
 Reporting marginals
 E.g., how many respondents, what % of them fall
under a certain variable.

500 of 1000 FEM students have CGPA = 3.5
& above.
 50% of 1000 FEM students.
147
 Shows
the number of cases having each of
the attributes of a given variable.
148
 Reporting
summary
 In term of averages



Mode (most frequent attribute)
Mean (arithmetic mean)
Median (middle attribute)
149
Measure
Level of
Measurement
Examples
Mode
Nominal
Eye color, party affiliation
Median
Ordinal
Rank in class, birth order
Mean
Interval & ratio
Speed of response, age in
years
150
 Spread
of raw data/info of a variable.
 Detailed information of distribution of a variable.
 Range (simplest measure)
 Percentile
 Standard deviation (more sophisticated)
151
 Range:
distance separating the highest from the lowest value.
(e.g., the respondents mean age is 22.75 with a range
from 20 to 26).
152
A
number or score indicating rank by telling what
percentage of those being measured fell below
that particular score.
scored 75th percentile, means 75% of the
other people scored below your score and 25%
scored at or above your score.
 e.g.,
153
 Is
a measure of the average amount the scores in a
distribution deviate from average (mean) of the
distribution.
 Observation near
mean, small SD. Observation far
from mean, large SD.
154
 Focuses on
the relationships/association between
two variables.
 Among
the many measures of bivariate
association are eta, gamma, lambda, Pearson’s r,
Kendall’s tau, and Spearman’s rho.
155
 Is
a method of analyzing the simultaneous
relationships among several variables and may be
used to understand the relationship between two
variables more fully.
 e.g.,
multiple regression, factor analysis, path
analysis, discriminant analysis.
156
1)
2)
Descriptive Statistics
Inferential Statistics
A
medium in describing data in manageable forms
(dealing with collection, tabulation, and
summarization of data so as to present
meaningful information).
 Quantitative descriptions
Describe single variables
 Describe the associations that connect one
variable with another

1.
2.
3.
Data Reduction
Measures of Association
Regression Analysis
 Reduction of
data from unmanageable details to
manageable summaries.
 e.g.,
for 100 respondents you may get data on 100
different ages; these data can be summarize to
manageable form by coding it into 3-4 categories.
 Provides information
on the nature and extent of
the relationship between any two variables.

Measures of association for two nominal variables =
Lambda,

For two ordinal variables = Gamma,

For two interval or ratio variables = Pearson’s productmoment correlation (r).

0.0 = no linear relationship btw the 2 variables

+ 1.0 = Strong positive linear relationship; as X
increases in value, Y also increases and vice versa.

- 1.0 = Strong inverse linear relationship; as X increases
in value, Y decreases in value; as X decreases in value,
Y increases in value.
 Represents the relationships between variables in
the form of equations, which can be used to
predict the values of a dependent variable on the
basis of values of one or more independent
variables.
 The
basic regression equation – for a simple linear
regression:
Y = a + bx + e
Y
= value estimated of the dependent variable
 a = constant variable / alpha or intercept
 b = slope, numerical value (multiplied by X, the
value of the independent variable)(beta
coefficient).
 e = error
 Simple
linear regression model does not
sufficiently represent the complexity of social life.
A
social phenomenon (DV) is normally affected
simultaneously by several IVs.
 Multiple
regression equation:
Y = a + b1x1 + b2x2 + bi xi + e
Y
= value estimated of the DV
 a = constant variable
 X1 to Xi = predictors
 b = slope (beta coefficient) for X
 e = residual (error)
 Typically
it involves drawing conclusions about a
population from the study of a sample drawn from
it.
 i.e.,
Generalizing your findings to a broader
population group.
Infer from sample (statistic) to population (parameter)
POPULATION
Sample
 Techniques that
allow us to determine if
hypothesis is supported, while considering
sampling error  hypothesis testing.
 Inferential statistics can
help us estimate or predict
population parameter from sample statistics.
 Population
value = parameter
 Sample value = statistics
 Inferential statistics are
based on the assumption that
population distributions of variables from which
samples are selected are normal in shape (Normal
Curve/Distribution).
 Represents how
variables are distributed.
 Characteristics: Bell-shaped; unimodal, symmmetric
and asymptotic.
Characteristics of Normal Curve:
 Unimodal
= mean, median & mode same value.
 Symmetrical = left & right halves of curve are
mirror images.
 Asymptotic = tails of curve get closer to X axis, but
never touch it.
 See
diagram on normal curve.
 The area
under the curve is very important in
inferential statistics.
 Accuracy of
inference depends on representativeness
of sample from population.
 Random

selection
Equal chance for anyone to be selected makes sample
more representative
 Inferential
statistics help researchers test
hypotheses and answer research questions, and
derive meaning from the results.
 A result found to be statistically significant by testing the
sample is assumed to also hold for the population from
which the sample was drawn.
 The ability to make such an inference is based on the
principle of probability.
 Researchers set the significance level of
each statistical test they conduct.

By using probability theory as a basis for their
tests, researchers can assess how likely it is that
the difference they find is real and not due to
chance
 What
inferential statistics does best is allow
decisions to be made about populations based on
the information about samples.
 One of the most useful tools for doing this is a test
of statistical significance
 Inferential statistics test the
likelihood that the
alternative (research) hypothesis (H1) is true and
the null hypothesis (H0) is not.

In testing differences, the H1 would predict that
differences would be found, while the H0 would predict no
differences.

By setting the significance level (generally at .05), the
researcher has a criterion for making the following
decision:
 If
the .05 level is achieved (p is equal to or less
than .05), then a researcher rejects the H0 and
accepts the H1.
 If
the .05 significance level is not achieved, then
the H0 is retained.
.05
.01
.001
Alpha levels are often written as the
“p-value”.
e.g., p =.05; p < .05; (p less than .05)
p < .05 (p equal to or less than)
(the chance of making 5 in 100 or 1 in 20 of making
an error)
 Df
are the way in which the scientific tradition
accounts for variation due to error.

It specifies how many values vary within a statistical test.

It specifies how many values vary within a statistical
test



Scientists recognizes that collecting data can never be errorfree
Each piece of data collected can vary, or carry error that we
cannot account for
By including df in statistical computations, scientists help to
account for this error
 If
reject H0 and conclude groups are really
different, it doesn’t mean they’re different for the
reason you hypothesized

May be other reason
 Since H0
is based on sample means, not
population means, there is a possibility of making
an error or wrong decision in rejecting or failing to
reject H0
Type I error
 Type II error

 Type I
error – rejecting H0 when it was true (it
sound have been accepted)

If alpha = .05, then there’s a 5% chance of Type 1 error.
 Type II
error – accepting H0 when it should have
been rejected

If increase alpha, you will decrease the chance of Type
II error
variable  One-way chi-square
 Two variables
 One
( 1 IV with 2 levels; 1 DV)  t-test
Two
variables
( 1 IV with 2+ levels; 1 DV)  ANOVA
 Three or
 See
more variables  ANOVA
handouts for more other examples of
inferential statistics
 Students will
state what they have learned in Lecture 13.
WRITING QUANTITATIVE REPORTS


Using the APA styles
9 Major Components
1.
2.
3.
4.
5.
6.
7.
8.
9.
Title Page
Abstract
Introduction (Chapter 1)
Review of the Literature (Chapter 2)
Method (Chapter 3)
Results (Chapter 4)
Discussion (or Summary, Conclusion, &
Implications) (Chapter 5)
Bibliography
Apendices (letters, instruments)
 Summarize the
main topic
 About 10 -12 words
Write in Top Heavy style
PREDICTORS OF MATERNAL BEHAVIOR AND THEIR
EFFECTS ON THE ACHIEVEMENT OF CHLDREN
Bottom Heavy
PREDICTORS OF MATERNAL BEHAVIOR AND
THEIR EFFECTS ON THE ACHIEVEMENT OF CHLDREN
 Comprehensive summary
 About
120 words
 For manuscript submitted for review, typed on separate
page.
 Begin
with current scenario, country data /
statistics, what are the symptoms in the society
that make you want to study the problem. Place
the problem in the context of other research
literature
 Statement of the Problem
 Purpose of the Study (May incorporate under
Statement of Problem, check with your supervisor)
 Research
Objectives
 Theoretical Framework
 Conceptual
Model
 Conceptual and Operational Definitions
 Rationale for the Present Study (May include
under Statement of Problem, check with your
supervisor)
i.
ii.
iii.
iv.
v.
Inform reader about previous research conducted on
the topic being research.
Also reflect how knowledgeable writer is on the topic.
Review studies which have focused on the DV.
Indicates the theory (if any) on which the study is based;
critique and weigh studies as theory is built.
Identify knowledge gap.
vi.
Present review in logical and comprehensive
manner. Organize with reference to the objectives
of the study.
vii.
Write a summary paragraph which identifies all the
major variables found to influence or related to the
DV. Add a statement to show how your research
topic flows from or adds to the research reviewed.
 Location
of Study
 Sampel (number, selection, characteristics)
 Measures (Instrumentation)
 Procedure / Data Collection
 Results of
data analysis and statistical significance
testing
 Include tables and figures.
 Interpret and
evaluate your results
 State whether hypotheses were supported.
 Answer basic questions



what your study contribute?
how study helped to solve study problem?
what conclusion and theoretical implications can be
drawn from your study?)
 Students will
state what they have learned in Lecture 14.