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Overall basics on research methodology

Overall basics on research methodology

- 1. Research Methods Dr.A.Mariammal.,MBA.,M.Com.,M.Phil.,P.hD Assistant Professor Department of Business Administration Sadakathullah Appa College ( Autonomous)
- 2. Research • What is Variable?- Types • What is Hypothesis? Types • How to make the sampling process? Various types • What is research tools? - types
- 3. • The universe contains unknown facts. • Through the learning process the man is searching the unknown facts. This searching process is known as ‘Research’. • Research means, search for knowledge. • Knowing the unknown facts from the universe.
- 4. • Research is base for development of a nation. • Today’s progress is based on yesterday’s research. • Continuous research in any field provides fruitful aspects for the future. • Government and NGO sector • Business sector • Social sector
- 5. Variables • If a characteristic of an observation (participant) is the same for every member of the group i.e. it does not vary, it is called a constant. • If it is differs for group members it is called a variable. • Variable: is a concept or abstract idea that can be described in measurable terms. E.g qualities, traits, or attributes • Anything that can vary can be considered a variable. E.g age, Income. • A variable is not only we measure, but also we can manipulate and something we can control for.
- 6. E.g Intelligence, Mental health, Motivation etc..
- 7. E.g Achievement test score
- 8. - Stimulus - Control - Response - Controlled
- 9. Quantitative and Qualitative Variables Quantitative variables: Interval, and ratio variables are quantitative. It is also called continuous variables because they have a variety (continuum) of characteristics. E.g Height in inches and test scores etc.. Qualitative variables: They are sometimes referred to as categorical variables because they classify by categories. Ordinal, Nominal variables are qualitative. • Nominal variables such as gender, religion, or color are categorical variables.
- 10. Continuous and Discontinuous Variables • Continuous variable: If the values of a variable can be divided into fractions then we call it a continuous variable. Such a variable can take infinite number of values. Income, temperature, age, or a test score are examples of continuous variables. • Discontinuous variable: Any variable that has a limited number of distinct values and which cannot be divided into fractions, is a discontinuous variable. Such a variable is also called as discrete variable.
- 11. Demographic Variables
- 12. Extraneous variable • Extraneous variable: It happens sometimes that after completion of the study we wonder that the actual result is not what we expected. In spite of taking all the possible measures the outcome is unexpected. It is because of extraneous variables. Variables that may affect research outcomes. • Extraneous variables that are not recognized until the study is in process, or are recognized before the study is initiated but cannot be controlled, are referred to as confounding variables. These variables interferes the results of the existing activity.
- 13. HYPOTHESIS Anticipated outcome or Possible solutions to the research questions
- 14. Types of Hypothesis (H) • Simple • Complex • Empirical • Question form • Null • Directional • Non-Directional • Associative
- 15. It predicts an associative relationship between the independent and dependent variable When there is a change in any one of the variables, changes also occur in other variable
- 16. What is Population? Definition: The group of individuals The group to which you want to generalize your findings. The larger group you are representing with your sample. Census -- the entire population
- 17. What is Sample? Definition A subset of the population A portion of the population (e.g., 10% or 25%) Sample is the raw material for the researcher.
- 18. SAMPLING… Target Population Study Population Sample When you sample the entire population? • When your population is very small • When you have extensive resources
- 19. Characteristics of a good sample • True representative • Free from bias • Objective • Maintained accuracy • Comprehensive in nature • Economical - energy, time, and money point of view
- 20. 1. Define the population 2. Identify the sampling frame 3. Select a sampling design or procedure 4. Determine the sample size 5. Draw the sample Steps in Sampling Process
- 21. Sampling Process
- 22. Determining sample size • Sample size is an important factor in research study. • How many sample I need to collect? Common question. ‘It depends’ – nature of research, population, research design etc., • It is defined by different experts in different ways. E.g: some are suggested 5% of the population others stated 10%, 25% etc., • Hence there is no hard fast rule. • It should be neither too small nor too large. It should be Optimum size. • If the researcher wants to study intensively of a problem, it is better to select small sample.
- 23. Types of Samples • Probability (Random) Samples - Simple random sample – Systematic random sample – Stratified random sample – Cluster sample • Non-Probability Samples – Convenience sample – Purposive sample – Quota – Snowball
- 24. Simple random sampling: All members of the population has a chance of being included in the sample Ex. Lottery sampling & Throwing dices
- 25. Stratified random sampling • Entire population divided into a number of homogeneous groups or types or class called strata
- 26. Example-Teachers in Tirunelveli District 4000 2400 1600 Primary school teachers High school Teachers Higher secondary school teachers
- 27. Population – Homogeneous or Heterogeneous In case of a homogeneous population, even a simple random sampling will give a representative sample. If the population is heterogeneous, stratified random sampling is appropriate.
- 28. NON PROBABILITY SAMPLING • Non probability sampling: The member of the population being chosen in unknown. (these are sometimes referred to as 'out of coverage'/'undercovered'). • The probability of selection can't be accurately determined. It involves the selection of elements based on assumptions. Hence, because the selection of elements is nonrandom. (e.g. an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls)
- 29. Non-Probability Sampling Definition The process of selecting a sample from a population without using (statistical) probability theory. Note: • each element/member of the population DOES NOT have an equal chance of being included in the sample, and • the researcher CANNOT estimate the error caused by not collecting data from all elements/members of the population.
- 30. Types of Non-Probability Sampling 1. Convenient Sampling 2. Judgment Sampling 3. Quota Sampling 4. Snowball Sampling
- 31. CONVENIENCE SAMPLING • Also known as opportunity or accidental or haphazard sampling. • It involves the sample being drawn from that part of the population which is close to hand. That is, readily available and convenient. • The researcher using such a sample cannot scientifically make generalizations about the total population because it would not be representative enough. • E.g if the researcher wants to conduct a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week. • This type of sampling is most useful for pilot testing.
- 32. Convenient….
- 33. • In this method the sample being drawn from that part of the population which is close to hand. • Convenience sampling is used in research where the researcher is interested in getting an inexpensive. As the name implies, the sample is selected because they are convenient. • Convenience sampling often leads to a biased study since it consists of only available people. • Convenience sampling has little statistical validity.
- 34. Advantages • No need for list of population. • Collect data quickly and economically. • Best method for exploratory research. • It does not require any statistical expertise. Disadvantages • It is highly biased, because of the researcher’s subjectivity, and so it does not yield a representative sample. • It is the least reliable sampling method. • The findings cannot be generalized.
- 35. Judgment Sampling • It also called as purposive sampling. Definition The researcher select the sample to fulfill a purpose; such as ensuring all members have a certain characteristic. No randomization. Example: The researcher want to be sure include members from Tamil Nadu, Kerala, Karnataka, Andhra in relatively equal numbers.
- 36. Advantages • Moderate cost. • Generally more appropriate than a convenience sample. • Sample guaranteed to meet a specific objectives. • Useful for certain types of forecasting. Disadvantages • Requires greater researcher effort. • Bias due to researcher’s beliefs may make sample unrepresentative. • Projected data beyond sample inappropriate.
- 37. QUOTA SAMPLING • The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. • Then judgment used to select the units from each segment based on a specified proportion. Example: The population is divided into cells on the basis of relevant control characteristics. A quota of sample units is established for each cell. 50 women, 50 men A sample is drawn for each cell until the quota is met. • For example interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection.
- 38. • For example, On basis of the quota in college year level, the researcher needs equal representation, with a sample size of 100. • He must select 25 -1st year students 25 -2nd year students 25 -3rd year and 25 -4th year students. • The bases of the quota are usually age, gender, education, race, religion and socioeconomic status.
- 39. Advantages • Moderate cost. • Very extensively use. • No need of population list. Disadvantages • Bias in researchers classification of units. • Errors can’t be estimated • It may not yield a precise representative sample. • Choose only accessible persons and accessible areas.
- 40. Snowball Sampling • This is the colourful name for the technique of building up a list or a sample of a special population. Definition Selecting participants by finding one or two participants and then asking them to refer to others. Selection of additional respondents is based on referrals from the initial respondents. (Building a sample through referrals) e.g: friends of friends • It is usually done when there is a very small population size.
- 41. Example 1: interviewing a homeless person and then asking him to introduce you to other homeless people you might interview. Example 2: if a researcher wants to study the problem faced by Indians through some source like Indian Embassy. Then he can ask each one of them to supply names of other Indians known to them, and continue this procedure until he gets an exhaustive list. • In this method the populations are not easily identified or accessed.
- 42. Advantages • Low cost. • Used in special situation. • Useful in locating members of rare populations. . • It is very useful in studying social groups and informal group in a formal organization. • It is useful for smaller populations for which no frames are readily available. Disadvantages • Highly bias because sample units not independent. • Projecting data beyond sample inappropriate. • It is difficult to apply this method when the population is large.
- 43. Sampling Errors • The errors which arise because of studying only a part of the total population are called sampling errors. • These may arise due to non-representativeness of the samples and inadequacy of sample size. • When several samples are drawn from a population, their results would not be identical. The degree of variations of sample results is measured by standard deviation (standard error). • As sample size increases the magnitude of the error decreases. • Sample size and sampling error are thus negatively correlated.
- 44. Non-Sampling Error These are errors which arise from sources other than sampling. It include errors of • Observation • measurement and • responses etc.,

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