Download STATISTICS FOR COMMUNICATION RESEARCH

STATISTICS FOR
COMMUNICATION
RESEARCH
ASSOCIATE PROF. DR. JUSANG
BOLONG
JABATAN KOMUNIKASI
03-8946 8780
[email protected]
OBJEKTIF KURSUS

Pada akhir kursus ini pelajar dapat:
1.
menghurai konsep statistik untuk
penyelidikan (C5);
2.
mengukur statistik deskriptif dan
statistik inferensi (P5);
3.
melaksanakan teknik statistik untuk
menganalisa dan menginterpretasi data
(A5, CTPS);
4.
merancang dan menyelaras kerja
penyelidikan secara berkumpulan (TS,
LS)
KANDUNGAN KURSUS
1.
2.
3.
4.
5.
Definisi, jenis dan peranan statistik
Jenis data, tahap pengukuran,
sampel dan populasi
Statistik deskriptif dan
persembahan data
Indeks kecenderungan memusat
dan serakan
Statistik inferensi dan taburan
normal
KANDUNGAN KURSUS (Samb.)
6. Ujian hipotesis – jenis hipotesis, at, paras
keertian, langkah-langkah ujian hipotesis
dan keputusan
8. Ujian perbandingan: Ujian T dan ANOVA
9. Ujian perkaitan: Ujian Chi-Square dan
Korelasi
10. Analisis regrasi: Ujian Multi-regrasi
Penilaian Kursus


Kerja Kursus (6 tugasan)
Peperiksaan
80%
20%
Statistics
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
Scientific methods for collecting,
organizing, analyzing data, summarizing,
and presenting, as well as with drawing
valid conclusions and making reasonable
decision on the basis of such analysis.
A branch of applied mathematics that
specializes in procedures for describing
and reasoning from observations of
phenomena
Purpose of Statistics
1.
2.
3.
4.
5.
To describe phenomena,
To organize and summarize our
result more conveniently and
meaningfully,
To make explain,
To make inference or make certain
predictions, and
To make conclusion and suggestion.
Type of Statistics
1. Descriptive Statistics
- Concerned with summarizing the
distribution of single variable. (Eg:
Frequency distribution, measure of central
tendencies, measures of dispersion).
Type of Statistics (cont.)
2. Inferential Statistics
- Concerned with making
generalization from sample to
population. Test of differences and
relationship between variables (Eg:
T-test, Analysis of Variance and Chisquare, correlation coefficient and
deriving regression equation
(prediction equation).
Concepts in Statistics
Population
- The entire group being observed, almost
always assumed to be infinite in size
- The total collection of all cases in which
the researcher is interested and wishes to
understanding.
- Group or set of human subjects or other
entities (Ex: all student at the UPM, all
members at Jabatan Komunikasi)
Concepts in Statistics (Cont.)
Sample
- The sub-group of population
- Generalizations based on samples
can accurately represent the
population
Concepts in Statistics (Cont.)
Population
• Basic unit of
interest
• Known as universe
• Large in numbers
• Difficult to
observed
• Dynamic
Sample
• A portion of
defined population
• Small in numbers
• Observable
• Can draw inference
about population
Concepts in Statistics (Cont.)
Variable
- As an observable characteristic of an
object or event that can be described
according to certain classification or
scales of measurement
- Independent Variable: In bivariate
relationship, the variable is taken as
cause, normally represented by
symbol X
Concepts in Statistics (Cont.)
-
-
Dependent variable: In a bivariate
relationship, the variable is taken as
the effect, normally represented by
symbol Y
Continuous variable/data: A
variable/data with a unit of
measurement that can be subdivided
infinitely. Eg: Height = 150.3 cm
Concepts in Statistics (Cont.)
Discrete variable/data: A variable with
a basic unit of measurement that
cannot be subdivided.
Eg: sex
1 = Male
2 = Female
Measurement
-
The process of assigning a number to
object, place or person
Level of Measurement
- The mathematical characteristic of a
variable as determined by the
measurement process. A major
criterion for selecting statistical
procedures or techniques.
Level of Measurement
(Type of Data)
1. Nominal
Sorting elements with respect to
certain characteristics
Sort into categories that are at
homogenous as possible
Lowest level of measurement
classification, naming, labeling
Level of Measurement
(Type of Data)
2. Ordinal
- Grouping or classification of
elements with degree of order or
ranking
- May not be able say exactly how
much they possess
- Can be arrange or placed in single
continuum
- Eg: Likert scale
Level of Measurement
(Type of Data)
3. Interval
- Ordering elements with respect to
the degree to which they possess
certain characteristics
- Indicates the exact distance between
them
- Zero does not means absence
- Eg: 0 degrees Celsius (Suhu rendah)
Level of Measurement
(Type of Data)
4. Ratio
- Ordering elements with respect to
the degree to which they possess
certain characteristics
- Indicates the exact distance between
them
- Zero means absence – absolute
Eg: RM0 (tiada pendapatan)
Level of Measurement
(Type of Data)

1.
2.
These four scale of measurement
can be generalized into two
categories:
Non-metric: includes the nominal
and ordinal scales of measurement.
Metric: include interval and ratio
scales of measurement.
Descriptive Statistics
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

Frequency distribution
Measure of central tendency
Measure of dispersion
Data Presentation

1.
2.
Basic function of statistics to organize
and summarize data:
Frequency table
Graphic presentation
- Pie chart
- Bar Chart
- Histogram
- Polygon
- Line graph
General guides


Use mode when variable are
nominal; you want to present quick
and easy measure for ordinal,
interval and ratio data/variables.
Use median when variable are
ordinal; you want to report the
central score and the scores
measured at interval and ratio levels
have badly skewed distribution

Use mean when variables are
interval or ratio (except for badly
skewed distribution); you want to
report the typical score and you
anticipate additional statistical
analysis.
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

Range: The highest score minus the
lowest score
Standard Deviation: The square root of
the squared deviation of the score around
the mean divided by N (number of cases).
Represented by the symbol ‘s’
Variance: The squared deviations of
scores around the mean divided by N.
Represented by the symbol ‘s²’
Inferential Statistics
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

To enable researcher to make statement
or summary or decision about the
population based on the sample
To enable researcher to make statement
or summary or decision on the unseen
data based on the empirical data
To enable researcher to make statement
or summary or decision on the large group
based on data from the small group.
Two main procedures of Inferential
Statistics
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
Estimates
Hypothesis Testing
Statistical Assumption
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

A set of parameters, guidelines
indicating the conditions under which
the procedures can be most
appropriately used.
Every test has own assumption that
should not be violated
Four main assumption of Inferential
Statistics
1.
2.
3.
4.
Random sample
Characteristics are related to true
population
Multiple random sample from same
population yield similar statistics
that cluster around true population
parameters
Can calculate the sampling error
associated with a sample statistics
Normal Distribution


The normal probability distribution is a
continuous probability distribution (Ref.
Equation pg 70)
Data in the normal distribution are measured in
terms of standard deviation from mean and are
called standard scores or Z score.
Characteristics of Normal Distribution:
1. It is a continuous probability distribution
2. Symmetrical or bell-shaped with the mode,
median and mean are equal

3. The distribution contains an infinite
number of cases
4. The distribution is asymptotic – the
tails approach abscissa: range from
negative to positive infinity
5. About 95% of distribution lies within
2 standard deviation from the mean.
Hypothesis Testing
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
a.
b.
Hypothesis is a tentative statement
about something.
Statement concerning:
Differences between groups
Relationship or association between
variables
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Statement related to our prediction
about population characteristics or
relationship
Statement related to research
question
Statement must be testable or
verifiable

a.
b.
c.
Hypothesis statement and testing
help us on:
Drawing conclusion
Making implication
Making suggestion


a.
b.
Statistical test is to test the
hypothesis
Two types of hypothesis:
Null Hypothesis (Ho)
Alternative or Research Hypothesis
(Ha or H1)


Null Hypothesis : A statement of no
difference or no association (among
variables, samples etc)
Alternative or Research hypothesis:
A statement asserting that there is
difference or association (among
variables, samples, etc)
Two forms of hypothesis:
1. Directional Hypothesis. Eg:
Ha: μ >230 or
Ha: μ < 230

2. Non-directional Hypothesis. Eg:
Ha: μ = 230
FIVE STEP Model for Hypothesis
Testing
Step 1:making assumption
• Samples selected randomly
• Defined population
• Interval-ratio data
• Sampling distribution – normal
Step 2: State the null and research hypothesis
Step 3: Selecting the appropriate distribution such as z, t, f
and χ² and establishing the level of significance as well as
critical region.
Step 4: Calculate the test statistics
Step 5: State the level of significance and critical region
•
Level of significance or alpha level commonly used 0.05
•
Critical region will determine the rejection or failure to
reject the null hypothesis
Step 6: Making decision
If test statistic falls in the critical
region, reject the null hypothesis.
If test statistic does not fall in the
critical region, we fail to reject the
null hypothesis at predetermined
alpha level
Step 7: State the conclusion
Type I and Type II Error (Ref: Pg. 86-module)
Type I Error (Alpha Error):
The probability of rejecting a null hypothesis that is
in fact true
Type II Error (Beta Error)
The probability of failing to reject the null
hypothesis in fact false
Level of Significance (Alpha Level)
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The probability of area under the sampling
distribution that contains unlikely sample
outcomes given that the null hypothesis is
true. Also, the probability of type I error
Commonly expressed as 90%, 95% or
99% or written as alpha = 0.10, 0.05 or
0.01
95%, refers to alpha 0.05 which means
that we are 95% sure of making the right
decision and 5% error.
Critical Region
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The area under the sampling distribution
that, in advance of the test itself, is
defined as including unlikely sample
outcome given that the null hypothesis is
true.
Critical value of the test statistic to reject
null hypothesis
Critical value is defined from the test
statistic table corresponding to its level of
significance and degree of freedom.

The null hypothesis is rejected when
the value of test statistics exceed the
critical value and lies in the critical
region
One-tailed and Two-tailed Test
Critical region on one side or both
sides of the distribution depending
on the nature of alternative or
research hypothesis.
Eg: Ho: a = b (Two-tailed)
Ha: a ≠b
Ha: a > b (One-tailed)
Ha: a < b

Two-tailed Test
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A type of hypothesis test used when
direction of difference between
variables or samples cannot be
predicted (Non-directional
hypothesis)
Two-tailed test has a two critical
regions on both sides of the
distribution
One-tailed Test
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
A type of hypothesis test used when
the direction of the difference
between variables or samples can be
predicted (Directional hypothesis)
One-tailed test has a one critical
region that correspond to the
direction of the research hypothesis.