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Research Tools and Techniques The Research Process: Step 6 (Sampling Design Part C) Lecture 27 Lecture Topics Covered Previously in the Last Lecture • Probability Sampling Techniques What we are going to Cover in this Lecture • Non-Probability Sampling Techniques • What Should be an Ideal Sample Size • Introduction to Data Analysis Process THE RESEARCH PROCESS (1). Observation The Broad Problem Area (3). (4). Theoretical Framework Problem Definition Variables Identification (5) (6). Scientific Research Design Generation of Hypothesis (2). Preliminary Data Gathering Interviews and Library Search (7). Data Collection and Analysis (8) Deduction (9). (10). (11). Report Writing Report Presentation Managerial Decision Making THE ELEMENTS OF RESEARCH DESIGN 1. Purpose of Study •Exploratory •Descriptive •Hypothesis Testing •Case Study 2. Type of Investigation Establishing: Causal Relationship or Co-relational 3. Extent of Researcher Interference •Minimal •Moderate •Excessive 4. Study Setting •Contrived •Non-Contrived 5. Measurement & Measures •Operational Definition • Scaling •Categorizing •Coding 10. Test Application Feel for Data Goodness of Data 6.Unit of Analysis (Population to be studied) Individuals Dyads Groups Organizations Machines etc. 8. Time Horizon 7. Sampling Design Probability Non-probability Sample Size (n) One-Shot (Cross-Sectional) or Longitudinal 9. Data Collection Methods Observation Interviews Questionnaire Physical Measurement Hypotheses Testing 4). Double Sampling: • A sub-sample of the primary sample is made to study phenomena in more detail. • Sample from a sample. • We selected 100 individuals to fill out the questionnaire and from these 100 individuals we selected 25 individuals for the interviews and further clarification. NON-PROBABILITY SAMPLING TECHNIQUES Elements of population do not have any probability for being chosen in a sample. Generalizability is low. I). Convenience Sampling: The members of population who are conveniently available are asked questions or interviewed i.e. whom so ever likes to answer questions at a marketing fair etc. II). Purposive Sampling: Data gathered from specific target groups. 1). Judgment Sampling: IT company heads in Islamabad, Women senior managers at Bank Al-Falah, Housewives surveyed in a locality. 2). Quota Sampling: Type of stratified random sampling done on convenience basis. 50 Senior Managers 100 Middle Managers 210 First Line Managers • From the group of managers who so ever is conveniently available is selected as a respondent to fill the questionnaire or to take interview from. • Non-Probability sampling techniques are ideal for data gathering at the preliminary stages of research. Choice Points in Sampling Design WHAT SHOULD BE AN IDEAL SAMPLE SIZE? The sample size depends on 1). The variability in the population 2). Precision and accuracy desired Precision refers to how close our estimate is to the true population. 3). Confidence level desired i.e. how certain we are that our estimates hold true for the population. 4). Type of sampling used 5). Sample size >30 and <500 is appropriate for most research 6). We can use the following formula Sx bar = S/under root n where Sx bar is the standard error or precision offered by the sample, S is the standard deviation of sample and n is the sample size. U = X bar + K Sx bar where U = Population Mean X bar = Sample Mean K = t-statistic for the level of confidence desired (Constant having values already known i.e. For 90% confidence level, the K value is 1.645 For 95% confidence level, the K value is 1.96 For 99% confidence level, the K value is 2.576) Krejcie and Morgan Table for Determining Sample Size Introduction to Data Analysis Process Data Analysis Process Interpretation of Results Data Collection Data Analysis Discussion Getting Data Ready for Analysis Feel for Data 1. Mean Editing Data 2. Median 1. Incompleteness /omissions 3. Mode 2. Inconsistencies 5. Frequency Distribution 3. Legibility 4. Coding Data 5. Categorizing 6. Creating a Data File 4. Variance Recommendations Hypotheses Testing Appropriate Statistical Manipulation (Inferential Statistics) Goodness of Data 1. Reliability 2. Validity Summary • Non-Probability Sampling Techniques • What Should be an Ideal Sample Size • Introduction to Data Analysis Process