Research Methodology Part II

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The second of the three part series.

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Research Methodology Part II

  1. 1. RESEARCH METHODOLOGY PART II DR. ANWAR HASAN SIDDIQUI, senior resident, dep't of physiology, jnmc, amu, aligarh
  2. 2. Research Process I. Define Research Problem Review concepts and theories III. Formulate hypotheses IV. Design research(including sample design) V. Collect data (Execution) Review previous research finding VI. Analyse data (Test hypotheses) VII. Interpret and report II. Review the literature
  3. 3. Research Design “A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.” Research Methods in Social Sciences, 1962, p. 50 • It constitutes he blueprint for the collection, measurement and analysis of data. • An outline of what the researcher will do from writing the hypothesis and its operational implications to the final analysis of data.
  4. 4. Research Design What will be the sample design? What periods of time will the study include? What techniques of data collection will be used? How will the data be analysed? What is the study about? Why is the study being made? Where will the study be carried out? Where can the required data be found?
  5. 5. Research Design Important concepts relating to research design: 1. Dependent and independent variables: • A concept which can take on different quantitative values is called a variable. As such the concepts like weight, height are all examples of variables. • Phenomena which can take on quantitatively different values even in decimal points are called ‘continuous variables’. • If it can only be expressed in integer values, they are non-continuous variables or in statistical language ‘discrete variables’. • If one variable depends upon or is a consequence of the other variable, it is termed as a dependent variable, and the variable that is antecedent to the dependent variable is termed as an independent variable. • For instance, if we say that height depends upon age, then height is a dependent variable and age is an independent variable.
  6. 6. Research Design 2. Extraneous variable: • Independent variables that are not related to the purpose of the study, but may affect the dependent variable are termed as extraneous variables or confounding variables. • Whatever effect is noticed on dependent variable as a result of extraneous variable(s) is technically described as an ‘experimental error’. • A study must always be so designed that the effect upon the dependent variable is attributed entirely to the independent variable(s), and not to some extraneous variable or variables.
  7. 7. Research Design 3. Control: • One important characteristic of a good research design is to minimise the influence or effect of extraneous variable(s). • The technical term ‘control’ is used when we design the study minimising the effects of extraneous independent variables. • In experimental researches, the term ‘control’ is used to refer to restrain experimental conditions. 4. Experimental and control groups: • In an experimental hypothesis-testing research when a group is exposed to usual conditions, it is termed a ‘control group’, but when the group is exposed to some novel or special condition, it is termed an ‘experimental group’ 5. Treatments: • The different conditions under which experimental and control groups are put are usually referred to as ‘treatments’.
  8. 8. Statistics in Research • Mean: – Mean, also known as arithmetic average, is the most common measure of central tendency – Defined as the value which we get by dividing the total of the values of various given items in a series by the total number of items. – where X = The symbol we use for mean (pronounced as X bar) ∑ = Symbol for summation Xi = Value of the ith item X, i = 1, 2, …, n n = total number of items
  9. 9. Statistics in Research • Median: – Median is the value of the middle item of series when it is arranged in ascending or descending order of magnitude. It divides the series into two halves; in one half all items are less than median, whereas in the other half all items have values higher than median. – If the values of the items arranged in the ascending order are: 60, 74, 80, 90, 95, 100,110 then the value of the 4th item viz., 90 is the value of median.
  10. 10. Statistics in Research • Mode: – Mode is the most commonly or frequently occurring value in a series. – The mode in a distribution is that item around which there is maximum concentration. – In general, mode is the size of the item which has the maximum frequency. – Mode is particularly useful in the study of popular sizes. – For example, a manufacturer of shoes is usually interested in finding out the size most in demand so that he may manufacture a larger quantity of that size.
  11. 11. Statistics in Research • Standard deviation: – is most widely used measure of dispersion of a series – Commonly denoted by the symbol ‘ σ ’ (pronounced as sigma). – Standard deviation is defined as the square-root of the average of squares of deviations, when such deviations for the values of individual items in a series are obtained from the arithmetic average. It is worked out as under:
  12. 12. Statistics in Research • Example to calculate SD. – The pulse rate of 10 student in a class are as follows 80,90,96,80,94,72,84,92,82,90.calculate SD? Mean = X = 860/10 =86 ∑(Xi – X) = 520 S.D= √ S.D= √(520/10)= 7.21 Xi Xi - X (Xi – X)2 80 80-86= -6 36 90 90-86= 4 16 96 96-86= 10 100 80 80-86= -6 36 94 94-86= 8 64 72 72-86=-14 196 84 84-86= -2 4 92 92-86=6 36 82 82-86=-4 16 90 90-86= 4 16 Total = 860 520
  13. 13. Sampling • Population (Universe)- An aggregate of units of observation either animate or inanimate about which certain information is required. • Eg. When recording the pulse rate of boys in the college , all boys in the college constitute the population or universe. • Sample – It’s a portion or part of the universe selected for the study in such a manner that the inference drawn can be applied to the whole universe.
  14. 14. Sampling Techniques • The methods of sampling can be divided on the basis of the element of probability associated with the sampling technique. • Probability means chances available to members of the population for getting selected in the sample. Accordingly, the methods of sampling are classified into two broad types:  Probability Sampling  Non Probability Sampling
  15. 15. Sampling Techniques Simple Random Sampling Accidental Sampling Systematic Sampling Convenience Sampling Stratified Sampling Judgment Sampling Cluster Sampling Purposive Sampling Quota Sampling
  16. 16. Sampling Techniques • Non Probability Methods – The probability of any particular member being chosen for the sample is unknown. – In case of non-probability sampling, units in the population do not have an equal chance or opportunity of being selected in the sample. The non-probability method believes in selecting the sample by choice and not by chance. – This is an unscientific and less accurate method of sampling, hence it is only occasionally used in research activities
  17. 17. Sampling Techniques • Probability Sampling Method – Probability Sampling is also known as Random Sampling – Probability means chance – Therefore element of the population has known chance or opportunity of being selected in the sample – It is the only systematic and objective method of sampling that provides equal chance to every element of the population in getting selected in the sample – The results of probability sampling more accurate and reliable – It helps in the formulation of a true representative sample by eliminating human biases
  18. 18. Sampling Techniques • Simple Random Sampling: – This sampling procedure gives every unit in the universe an equal chance or opportunity of being selected. – This method of sampling can be applied when the parameter to be estimated is homogeneously distributed in the population – A crude method of which is by drawing a lot. – A good method of simple random sampling involves the use of published tables called tables of Random Numbers. – Now a days computer generated random number can also be used for the selection
  19. 19. Sampling Techniques • Example : To select a random sample of 25 student from a class of 75 students. – In this case all the 75 student in the class are arranged in some order say alphabetical order of their names or by their roll numbers. – From the random number table any arbitrary row or column is selected and 25 numbers ranging from 1-75 are chosen. – The students corresponding to the chosen number constitute a sample.
  20. 20. Sampling Techniques A random number table
  21. 21. Sampling Techniques • Systematic sampling – In this type of sampling the first unit of the sample is selected at random and the subsequent unit are selected in a systematic way. Example: A sample of 50 students are required from 600 students of a school. • 1st population is divided by the required sample • 600/50= 12 • Now a random number between 1-12 is obtained (suppose 4) • Then our first sample will be student number 4 • Rest will be obtained by adding 12 to each number • 4, 4+12(16), 16+12(28), 40+12 (52) and so on……
  22. 22. Sampling Techniques • Systematic sampling is useful for studying hospital cases. • If it is proposed to study a sample of 20 cases of a disease and if the mean annual admission for that disease are 100 then every fifth case who seeks admission to the hospital is included in the sample • It is called as quasi-random sampling. • It is called quasi because it is in between probability and non-probability sampling.
  23. 23. Sampling Techniques • 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. – 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’) – We select items from each stratum to constitute a sample. – Example: If it is known that the prevalence of a certain disease is different in different age group then to estimate the prevalence rate of the disease stratified sample is taken from each of the age group of the population
  24. 24. Sampling Techniques • Cluster Sampling: – It is a sampling technique where the entire population is divided into groups, or clusters, and a random sample of these clusters are selected. – All observations in the selected clusters are included in the sample – Example: Suppose researcher wants to study the learning habits of the college students from Mumbai. He may select the sample as: • First prepare a list of all colleges in Mumbai city • Then, select a sample of colleges on random basis. Suppose there are 200 colleges in Mumbai, then he may select 20 colleges by random method. • 3)From the 20 sampled colleges, prepare a list of all students. From these lists select the required number of say 1000 students on random basis
  25. 25. Determination of Sample size • When conducting investigation to obtain information on quantitative data, the sample size is calculated by the formula: n= (tα 2 × σ2)/e2 where n =desired sample size σ = standard deviation of the obserbvation e= permissable error in the estimation of mean tα = is the value of ‘t’ statistics at α level of significance
  26. 26. Determination of Sample size • A ‘t’ table Example: In a community survey to estimate the haemoglobin level of antenatal mothers, it is assumed from pilot studies, that the mean Hb% level is about 12 gm% with a standard deviation of 1.5 gm% then the sample size required to estimate the Hb.level with a permissible error of 0.5gm% is??? Answer: Standard Deviation σ = 1.5 gm Permissible error e= o.5gm tα is taken as 1.96 as it is conventional to use 5% significance level n= (tα 2 × σ2)/e2 ={(1.96)2 × (1.5)2}/(0.5)2 = 36
  27. 27. Determination of Sample size • Sample size in inferential or experimental study is given by: Where N= number of patients required in each group K = constant which is a function of α and β (see Table) µ1 = mean of first population µ2= mean of second population
  28. 28. Determination of Sample size • A clinical trial tests the preventive effect upon neonatal hypocalcemia of giving Supplement A to pregnant women. Women are randomised and given either placebo or Supplement A. – Measure: serum calcium level of baby one week postnatally – Analysis: Comparisons of difference between two groups of babies using an independent samples t-test at 5% significance (α = 0.05) – Serum calcium in babies of untreated women 9.0 mg/100 ml, standard deviation (s) 1.8mg/100ml – Study should detect clinically relevant increase in serum calcium of 0.5 mg/100ml, 80 per cent of the time ( β= 0.2) • Answer: • In summary: m = Mean serum calcium level = 9.0 mg/100ml s Standard Deviation = 1.8mg/100ml d = difference in means m1 - m2 = 0.5mg/100ml a = 0.05 b = 0.2 K= 7.9
  29. 29. Determination of Sample size • The number of patients required in each group is given by • N= 2 × 7.9 × (1.8/0.5)2 = 205
  30. 30. To be continued……………………
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