Lect4 research methodology


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Lect4 research methodology

  1. 1. Biology 199 SS 2011-2012 Research Methodology
  2. 2. Research Design  represents the “plan of attack” of the researcher  in answering the research objectives  in obtaining all the relevant data in relation to objectives and hypothesis
  3. 3. The specific areas of concern in the choice of a research design are the following  selection and number of subjects  control and manipulation of relevant variables  establishment of criteria to evaluate outcomes  instrumentation  maximization of internal and external validity
  4. 4. Factors to consider  research objectives  feasibility  ethical considerations  economy and efficiency  internal and external validity.
  5. 5. Internal Validity  refers to extent to which investigator is able to control the different biases affecting the study and in the end, measures what he really intends to measure.  Did the experimental treatment really bring about a change in the dependent variable?  Did the independent variable make a significant difference?
  6. 6. External Validity  refers to the extent to which the investigator is able to generalize the results of his study. Are the results applicable to groups and environment outside of experimental setting?
  7. 7. DESIGN THE TOOLS FOR DATA COLLECTION  Experimentation  Questionnaire  Interview schedule and forms
  8. 8. DESIGN THE PLAN FOR DATA ANALYSIS  A number of researchers think about data analysis only after all data has been collected.  Consequences:  Some very important variables in study are either not measured at all or collected using a measurement scale which is inconsistent with desired mode of data analysis.  Objectives are too ambitious or non-measurable, given the nature of the data that were collected.
  9. 9. Dummy Table  skeleton tables drawn to help the investigator conceptualize how the data is going to be organized and presented after it has been collected.
  10. 10. COLLECTION OF DATA  Essential phase of the research process.  Researcher employs specialized tools, instruments and procedures depending upon the method designed for such activity.
  11. 11. DATA PROCESSING  Process the information gathered to prepare for and facilitate analysis and interpretation of data.  Editing of data collection forms and coding of responses are procedures usually done in this stage
  12. 12. DATA ANALYSIS AND INTERPRETATION  Involves quantification, description, and classification of data  Statistics play a major role.  Researcher must be familiar with basic statistical concepts and procedures and must know their limitations as well as the areas where they may be appropriately applied.
  13. 13. Selection of a research design depends mainly on the objectives of the study 1. To describe, compare – descriptive design 2. To test hypothesis – experimental designs
  14. 14. EXAMPLE General Objective: To explain why university students engage in vandalistic acts in schools?
  15. 15. Specific objectives 1. To determine the prevalence of students who admitted that they have committed some vandalistic acts at least once in their college life; PROPORTION/PERCENT 2. To describe the socio-demographic and psychographic profile of this group of students; DESCRIPTIVE STATS (frequencies, percent, mean (SD), median (range) 3. To identify the types of vandalistic acts committed by these students; FREQ, PERCENT
  16. 16. Specific objectives 4. To know the reasons why they committed such acts. FREQ, PERCENT 5. To analyze significant differences in motivations to commit vandalistic acts among students classified by sex and socio-economic status; INFERENTIAL STATISTICS (CHI-SQUARE since expected data is nominal level) 6. To determine significant associations among selected psychological traits and motivations to commit vandalistic acts.CORRELATION/REGRESSION
  17. 17. Criteria for selecting the most appropriate statistical test 1. if variable of interest (dependent variable, outcome factor) is continuous or discrete; 2. If level of measurement of the dependent variable is nominal, ordinal, ratio or interval; 3. if probability or non-probability sampling is used. This will indicate if your samples are independent or related; 4. if the underlying distribution of the dependent variable is normal or non-normal (skewed) 5. If variances of the samples are homoscedastic or equal. 6. depends also on number of groups being tested.
  18. 18. Parametric vs Non-parametric tests Based on the criteria, you have 2 choices of statistical methods: 1. Parametric tests - assume that data come from a normally distributed or approximately normally distributed population. It is more powerful but has more stringent requirements for use. 2. Non-parametric (or distribution-free) test make no assumptions about the probability distributions of the variables being assessed.
  19. 19. Use a parametric statistical tests if:  Variables are continuous ( i.e., interval or ratio level of measurement)  Samples have underlying normal distribution (is not a problem if you have large sample size) Compute for sample size to get minimum number of samples to be included in the study  Variances of the samples are equal (homoscedastic or homogenous) - (is not a problem if you have large sample size)
  20. 20. Use a non-parametric test if:  Variables are discrete or qualitative (i.e., nominal or ordinal level of measurement)  Samples have highly skewed distribution.
  21. 21. LEVEL OF MEASUREMENT Discrete Variables - Nominal Data (categories) e.g. SEX (male, female), CIVIL STATUS (single, married, living-in) separated, widowed) - Ordinal Data (ranks) Rank 1- most skillful basketball team Rank 2- next most skillful team Rank 3 – next, next most skill basketball team
  22. 22. LEVEL OF MEASUREMENT Continuous Variables -Interval Data – with no absolute zero; zero has meaning) e.g., IQ test, temperature -Ratio data – with absolute zero, zero means “none, nothing”. e.g., classroom tests. weight
  23. 23. TYPE OF STATISTICAL TEST Parametric Non-parametric Level of measurement NUMBER OF GROUPS Interval/Ratio Nominal Ordinal One group Z-test t-test One sample Chi- square test Binomial test Kolmogorov- Smirnov test Runs test Two groups (related samples) Paired t-test Walsh test (interval) McNemar test Wilcoxon Signed Rank test Two groups (independent samples) Independent Student t-test for equal /unequal variances Chi square test Fisher’s Exact test (if any cell has expected freq of <5) Mann-Whitney U test Kolmogorov Smirnov two sample test
  24. 24. TYPE OF STATISTICAL TEST Parametric Non-parametric Level of measurement NUMBER OF GROUPS Interval/Ratio Nominal Ordinal More than 2 groups (related samples) Repeated measures ANOVA Cochran test Friedman ANOVA More than 2 groups (independent samples) One-way ANOVA K-way ANOVA Chi square for independence Kruskal- Wallis ANOVA
  25. 25. CORRELATION Pearson product Moment correlation X & Y are ordinal Spearman Rho Tetrachoric correlation Kendall rank correlation Kendall tau Both variables, X & Y are nominal X & Y are continuous (interval/ratio) Phi Coefficient (only if dichotomous, 2 x 2 table) Contingency coefficient Cramers V Lambda
  26. 26. RESEARCH DESIGN TYPE OF RESEARCH RESEARCH DESIGN QUESTION USUALLY USED ---------------------------------------------------------------------- ---- Descriptive 1. Observational w/ one observation (Describe conditions) 2. Observational w/ multiple obs. 3. Ex Post Facto Differences 3. Ex Post Facto * (Is there a difference?) 4. Pre/Post (two obs. of DV) 5. Pre/Post w/Control Group (two obs. of DV) 6. Two-Group (one after treat. obs. of DV) 7. Three-Group (one after treat. obs. of DV) 8. Repeated Measures (two or more obs.) 9. Factorial (two or more IVs) 10. Co-variance (pre- observation as control) 11. ABA Time Series (single subject) 12. AB Time Series (single subject) Relationships
  27. 27. DATA ANALYSIS ----------- DESIGN STATISTICAL TEST --------------------------------------------------------- ---------- DIFFERENCES RESEARCH QUESTION 1. Basic two-group design 1. a. t-test - independent means (Interval or ratio data)* b. Mann- Whitney U test (Ordinal data) c. Chi-square (nominal data) 2. Pre-test and post-test 2. a. t-test - dependent design. (non- independent) means (Interval) b.
  28. 28. DATA ANALYSIS series analysis Subject (interval) 4. Covariance, or repeated 4. a. Repeated measures analysis measures design. of variance OR Analysis of co- variance (Interval) b. Friedman's AOV by ranks (Ordinal) c. Cochran's Q (Nominal) 5. Three or more groups 5. a. Analysis of variance design (Interval) b. Kruskal-Wallis AOV (Ordinal)
  29. 29. DATA ANALYSIS 6. One-group sample from a 6. a. One-group t-test (Interval) known population. b. Kolmogorov-Smirnov test for goodness-of-fit (Ordinal) c. Chi-square goodness-of-fit test (Nominal) RELATIONSHIPS RESEARCH QUESTION 7. Correlational study 7. a. Pearson product moment (Two or more variables correlation coefficient. and one group) (Interval) b. Spearman's rank order correlation, Kendall's Tau (Ordinal)