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STATISTICS FOR COMMUNICATION RESEARCH

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Title: STATISTICS FOR COMMUNICATION RESEARCH Author: FBMK Last modified by: Valued Acer Customer Created Date: 6/26/2004 12:41:12 AM Document presentation format – PowerPoint PPT presentation

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Title: STATISTICS FOR COMMUNICATION RESEARCH


1
STATISTICS FOR COMMUNICATION RESEARCH
  • PROF. MADYA DR. JUSANG BOLONG
  • JABATAN KOMUNIKASI
  • 03-8946 8780
  • jusang_at_fbmk.upm.edu.my

2
OBJEKTIF KURSUS
  • Pada akhir kursus ini pelajar dapat
  • Menerangkan peranan statistik dalam penyelidikan
  • Menerangkan perbezaan dan kaitan antara statistik
    deskriptif dengan statistik inferensi
  • Mengenalpasti dan menerangkan teknik yang boleh
    digunakan untuk menganalisis data kuantitatif
    dalam penyelidikan komunikasi
  • Memilih teknik yang sesuai untuk menganalisis
    data dan membuat tafsiran yang betul daripada
    hasil analisis data.

3
KANDUNGAN KURSUS
  1. Definisi, jenis dan peranan statistik
  2. Jenis data, tahap pengukuran, sampel dan populasi
  3. Statistik deskriptif dan persembahan data
  4. Indeks kecenderungan memusat dan serakan
  5. Statistik inferensi dan taburan normal

4
KANDUNGAN KURSUS (Samb.)
  • 6. Ujian hipotesis jenis hipotesis, jenis
    ralat, paras keertian, langkah-langkah ujian
    hipotesis dan keputusan
  • 7. Ujian signifikan satu sampel satu pemboleh
    ubah
  • 8. Ujian perbandingan Membandingkan kumpulan dan
    membandingkan pemboleh ubah
  • 9. Ujian perkaitan dan analisis regrasi

5
Penilaian Kursus
  • Kerja Kursus (4 tugasan) 40
  • Peperiksaan Pertengahan 20
  • Peperiksaan Akhir 40

6
Statistics
  • Scientific methods for collecting, organizing,
    summarizing, presenting, and analyzing data 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

7
Purpose of Statistics
  1. To describe phenomena,
  2. To organize and summarize our result more
    conveniently and meaningfully,
  3. To make inference or make certain predictions,
  4. To make explain, and
  5. To make conclusion.

8
Type of Statistics
  • 1. Descriptive Statistics
  • - Concerned with summarizing the distribution of
    single variable or measuring relationship between
    two or more variables (Eg Frequency
    distribution, measure of central tendencies,
    measures of dispersion, correlation coefficient
    and deriving regression equation (prediction
    equation)

9
Type of Statistics (cont.)
  • 2. Inferential Statistics
  • - Concerned with making generalization from
    sample to population (Eg T-test, Analysis of
    Variance and Chi-square).

10
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)

11
Concepts in Statistics (Cont.)
  • Sample
  • The sub-group of population
  • Generalizations based on samples can accurately
    represent the population

12
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

13
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

14
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

15
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

16
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.

17
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

18
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

19
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)

20
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)

21
Level of Measurement (Type of Data)
  • 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.

22
Descriptive Statistics
  • Frequency distribution
  • Measure of central tendency
  • Measure of dispersion
  • Measure of association

23
Data Presentation
  • Basic function of statistics to organize and
    summarize data
  • Frequency table
  • Graphic presentation
  • - Pie chart
  • - Bar Chart
  • - Histogram
  • - Polygon
  • - Line graph

24
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

25
  • 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.

26
  • 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

27
Inferential Statistics
  • 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.

28
Two main procedures of Inferential Statistics
  • Estimates
  • Hypothesis Testing

29
Statistical Assumption
  • 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

30
  1. Random sample
  2. Characteristics are related to true population
  3. Multiple random sample from same population yield
    similar statistics that cluster around true
    population parameters
  4. Can calculate the sampling error associated with
    a sample statistics

31
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

32
  • 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.

33
Hypothesis Testing
  • Hypothesis is a tentative statement about
    something.
  • Statement concerning
  • Differences between groups
  • Relationship or association between variables
  • Changes that occurs

34
  • Statement related to our prediction about
    population characteristics or relationship
  • Statement related to research question
  • Statement must be testable or verifiable

35
  • Hypothesis statement and testing help us on
  • Drawing conclusion
  • Making implication
  • Making suggestion

36
  • We are not going to prove the hypothesis is true,
    but we are to prove that is not true or false
  • Statistical test is to test the hypothesis
  • Two types of hypothesis
  • Null Hypothesis (Ho)
  • Alternative or Research Hypothesis (Ha or H1)

37
  • 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)

38
  • Two forms of hypothesis
  • 1. Directional Hypothesis. Eg
  • Ha µ gt230 or
  • Ha µ lt 230
  • 2. Non-directional Hypothesis. Eg
  • Ha µ 230

39
FIVE STEP Model for Hypothesis Testing
  • Step 1making assumption
  • Samples selected randomly
  • Defined population
  • Interval-ratio data
  • Sampling distribution normal

40
  • Step 2 State the null and research hypothesis
  • Step 3 Selecting the appropriate distribution
    such as z, t, f and ?² 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

41
  • 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

42
  • 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

43
Level of Significance (Alpha Level)
  • 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.

44
Critical Region
  • 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.

45
  • The null hypothesis is rejected when the value of
    test statistics exceed the critical value and
    lies in the critical region

46
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 gt b (One-tailed)
  • Ha a lt b

47
Two-tailed Test
  • 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

48
One-tailed Test
  • 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.
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