METHODOLOGIES IN SAMPLING AND COLLECTION OF QUANTITATIVE DATA

METHODOLOGIES IN SAMPLING AND COLLECTION OF QUANTITATIVE DATA LECTURER: WILLIAM ANGKO

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 sub-populations 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=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 • Editing • Coding • Classification • 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 YOUEND OF LECTURE

METHODOLOGIES IN SAMPLING AND COLLECTION OF QUANTITATIVE DATA