LECTURER: WILLIAM ANGKO
7/31/2017
1
Content of Presentation
 Probability sampling techniques
 Data types and importance
 Tools and techniques of data collection
 Questionnaire
 Observation (with guided check list)
 Techniques of data analysis
 Measures of central tendencies
 Measures of dispersion
 Trend analysis
 Presentation of analyzed data
 Frequency tables, bar charts, pie charts, histograms etc
Gathering information through SURVEY
 A complete enumeration/counting of all items in the
‘population’ is known as a census
 A survey is representative of the total population as
possible in order to produce cross-section results
 In TTFPP, we are interested in Survey and not Census
Sample design
 A sample design is a definite plan for obtaining a
sample from a given population.
 It refers to the technique or the procedure the
researcher would adopt in selecting items for the
sample
 Sample design is determined before data are collected
TYPES OF SAMPLE DESIGNS
 A sample may be probability sampling or non-
probability sampling.
 Probability sampling is based on the concept of
random selection,
 whereas non-probability sampling is ‘non-random’
sampling.
Cont…
 Non-probability sampling:
 Non-probability sampling is that sampling procedure
which does not afford any basis for estimating the
probability that each item in the population has of being
included in the sample.
 Non-probability sampling is also known by different
names such as deliberate sampling, purposive sampling
and judgement sampling
Cont…
 the sample are selected deliberately by the researcher
 His choice concerning the items remains supreme
Cont…
 Probability sampling:
 Probability sampling is also known as ‘random sampling’
or ‘chance sampling’.
 Under this sampling design, every item of the
population has an equal chance of been included in the
sample.
 It is, so to say, a lottery method in which individual units
are picked up from the whole group not deliberately but
by some mechanical process
Cont…
 Random sampling from a finite population refers to
that method of sample selection which gives each
possible sample combination an equal probability of
being picked up
 Each item in the entire population to have an equal
chance of being included in the sample.
Cont…
 Major implications of random sampling
 It gives each element in the population an equal
probability of getting into the sample; and all choices
are independent of one another.
 It gives each possible sample combination an equal
probability of being chosen.
Types of samples
 Systematic sampling:
 In some instances, the most practical way of sampling is
to select every ith item on a list. Sampling of this type is
known as systematic sampling.
 An element of randomness is introduced into this kind
of sampling by using random numbers to pick up the
unit with which to start.
 This is possible if you are a register
Cont…
 Stratified sampling:
 If a population from which a sample is to be drawn does not
constitute a homogeneous group, stratified sampling
technique is generally applied in order to obtain a
representative sample.
 Under stratified sampling the population is divided into
several sub-populations that are individually more
homogeneous than the total population (the different subpopulations are called ‘strata’)
 then we select items from each stratum to constitute a
sample.
Cont…
 Cluster sampling:
 If the total area of interest happens to be a big one, a
convenient way in which a sample can be taken is to divide
the area into a number of smaller non-overlapping areas
 then to randomly select a number of these smaller areas
(usually called clusters),
 the ultimate sample consisting of all (or samples of) units
in these small areas or clusters.
Cont…
 Sequential sampling:
 This sampling design is some what complex sample design.
 The ultimate size of the sample under this technique is not
fixed in advance, but is determined according to
mathematical decision rules on the basis of information
yielded as survey progresses.
 This is usually adopted in case of acceptance sampling plan
in context of statistical quality control
Formula
 Sample size
N
n
2
1  Ne
 N=targeted population (number of households or
individuals in community or organisation)
 e =margin of error/ level of significance =0.05 or 5%
Simple random
 simple random sampling ensures that every individual
in the population has a chance to be included in the
sample selected.
 It is used in the selection of all the other probability
samples
 The researcher randomly selects any individuals in the
population
Purposive sampling
 Purposive sampling is used where the researcher
targets a particular segment of the population
 Researcher uses his/her own discretion to select
sample for convience
Snowball sampling
 Under this technique, the researcher selects the first
individual that he/she meets and after interviewing he
asks if the respondent knows any other person within
that category
Tool and techniques for data collection
 Describe how instruments/tools were developed
 Specify how the format was determined
 How pre-testing or pivot survey of instrument were
done
 Indicate how reliable instrument is
 Describe strengths and weaknesses
Types of Instruments
 Questionnaire
 Interviewer administered
 Self-administered
 Telephone or email administered
 Open-ended questionnaire
 Closed-ended questionnaire
Interviews
 Personal interviews or In-depth interviews
 Focus group discussion
 Key informant interviews
Other methods
 Personal observation
 Transect walk
Operations in Data processing
1. Editing
2. Coding
3.
Classification
4. Tabulation
Analysis of Data
 We mean the computation of certain indices or measures
 Also involves searching for patterns of relationship that
exist among the data groups.
 Involves estimating the values of unknown parameters of
the population and testing of hypotheses for drawing
inferences.
Cont…
 Analysis may, therefore, be categorized as;
 Descriptive analysis and
 Inferential analysis or often known as statistical
analysis.
Descriptive Analysis
 “Descriptive analysis is largely the study of distributions
of one variable”
 This study provides us with profiles of companies, work
groups, persons and other subjects on any of a multiple of
characteristics such as size. Composition, efficiency,
preferences, performance
 may be in respect of one variable __uni-dimensional or
directional analysis),
Cont…
 or in respect of two variables ___bivariate analysis
 or in respect of more than two variables _ multivariate
analysis)
 we use this to work out various measures that show
the size and shape of a distribution(s)
 Also involves measuring relationships between two or
more variables.
Cont…
 In measuring the relationship between various, we are
concern of correlation analysis and causal analysis.
 Correlation analysis studies the joint variation of two
or more variables for determining the amount of
correlation between two or more variables
 While Causal analysis is concerned with the study of
how one or more variables affect changes in another
variable
 Usually the following analyses* are involved when we
make a reference of multivariate analysis;
 1. Simple regression
Deals with one dependent variable as a function of one
independent variable
2. Multiple regression analysis:
This analysis is adopted when the researcher has one
dependent variable which is presumed to be a function of
two or more independent variables.
The objective of this analysis is to make a prediction about
the dependent variable based on its covariance with all
the concerned independent variables.
Inferential analysis
 Concerned with the various tests of significance for
testing hypotheses in order to determine with what
validity data can be said to indicate some conclusion or
conclusions.
 It is also concerned with the estimation of population
values.
 It is mainly on the basis of inferential analysis that the
task of interpretation (i.e., the task of drawing inferences
and conclusions) is performed.
STATISTICS IN RESEARCH
 There are two major areas of statistics viz., descriptive
statistics and inferential statistics.
 Descriptive statistics concern the development of certain
indices from the raw data, whereas inferential statistics
concern with the process of generalization
 Inferential statistics are also known as sampling statistics
 mainly concerned with two major type of problems:


the estimation of population parameters,
the testing of statistical hypotheses.
Cont…
 The important statistical measures* that are used to
summarise the survey/research data are:
 (1) measures of central tendency or statistical averages;

Mean, median, mode,
 (2) measures of dispersion;

Range, variance, standard deviation
 (3) measures of asymmetry (skewness);
 (4) measures of relationship;
 (5) other measures.

Frequencies, tabs and cross-tabulation
Cont…
 Central tendencies
 most important ones are the arithmetic average or
mean, median and mode

Geometric mean and harmonic mean are also sometimes used
 measures of dispersion
 variance, and its square root—the standard deviation are
the most often used measures.
 Other measures such as mean deviation, range
Cont…
 measures of skewness and kurtosis
 measure of skewness based on mean and mode or on
mean and median.
 Other measures of skewness, based on quartiles or on
the methods of moments, are also used sometimes.
 Kurtosis is also used to measure the peakedness of the
curve of the frequency distribution.
Cont…
 Measures of relationship
 Karl Pearson’s coefficient of correlation is the frequently
used measure in case of statistics of variables,
 whereas Yule’s coefficient of association is used in case of
statistics of attributes.
 Multiple correlation coefficient, partial correlation
coefficient, regression analysis, etc.,
 Other measures: Index numbers, analysis of time series,
coefficient of contingency, etc…
THAN K YOU
END OF LECTURE