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Practical applications and analysis in Research Methodology

practical application in research, reviews of qualitative and mixed method studies, analysis, processing the data, data editing , data coding , classification of data, analysis of data, parametric test, non parametric test in
Research Methodology

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Practical applications and analysis in Research Methodology

  1. 1. H A F S A A R S H AD B B O F 17M006 RESEARCH METHODOLOGY
  2. 2. S U B MI TTE D T O : S I R A B D U L M A J ID DEPARTMENT OF BOTANY UNIVERSITY OF SARGODHA
  3. 3. • Practical Application ( Using computer software to facilitate mixed-methods data analysis • Reviews of qualitative and mixed-methods studies • Analysis • Processing the data • Data editing • Data coding • Classification of data • Analysis of data • Parametric test • Non parametric test CONTENTS
  4. 4. U S I N G C O M P U T E R S O F T W A R E T O F A C I L I T A T E M I X E D - M E T H O D S D A T A A N A L Y S I S PRACTICAL APPLICATIONS
  5. 5. COMPUTER APPLICATIONS: Using computer software to facilitate mixed-methods data analysis Some software programs are especially suited for mixed-methods data analysis; examples include EthnoNotes, MAXQDA, NVivo, and QDA Miner. Such software programs typically enable a researcher to: ■ Sift through and sort data that might be relevant to particular subproblems or research questions ■ Convert qualitative data into simple quantitative data ■ Create matrixes that summarize certain aspects of a data set ■ Integrate and compare results from quantitative data and coded qualitative data ■ Compare data obtained from various demographic groups
  6. 6. REVIEWS OF QUALITATIVE AND MIXED-METHODS STUDIES  When a researcher wants to consolidate the results of many quantitative studies related to a particular research problem, the researcher might conduct a meta-analysis—a statistical analysis of all of the studies’ individual statistical results—in order to discern general trends in the findings.  But when a researcher wants to synthesize the results of many previous qualitative or mixed-methods studies, a statistical meta-analysis obviously isn’t possible, at least not for most of the reported data.  A viable alternative for qualitative and mixed-methods studies is a systematic review, in which research reports, rather than individual people or other individual entities, are the objects of study (Harden & Thomas, 2010; Petticrew & Roberts, 2006).
  7. 7. REVIEWS OF QUALITATIVE AND MIXED-METHODS STUDIES The review is systematic in the sense that a researcher identifies and implements an explicit, rigorous method for selecting and analyzing the reports. For example, the researcher is likely to:  Conduct an extensive search for studies related to the research problem—for instance, by using certain keywords in appropriate online databases and then including all relevant juried qualitative and/or mixed-methods research reports (e.g., articles, dissertations, conference presentations) in the sample.  Evaluate the quality of each report (e.g., Were rigorous methods used to collect and analyze data? Are obvious researcher biases affecting conclusions?) and then possibly either exclude or give less credence to certain reports.  Code the contents of the reports for key ideas, concepts, themes, and/or theories.  Perform one or more meta-analyses of any statistical findings reported in the studies.
  8. 8. REVIEWS OF QUALITATIVE AND MIXED-METHODS STUDIES  Aside from taking steps such as these, there is no single “best” way to conduct a systematic review.  As is true for most qualitative and mixed-methods research, the specific strategies used depend on the nature of the research problem and the particular methodologies used in the individual studies included in the sample.  An example described by Harden and Thomas (2010) can illustrate what a systematic review might involve.  In response to a request from the United Kingdom’s Department of Health, these two researchers and several colleagues wanted to review current research regarding effective strategies for getting children to eat more healthfully
  9. 9. A SAMPLE DISSERTATION We conclude with an example of a mixed-methods study that used a two- phase, explanatory design. Laura Lara (who shortly thereafter became Laura Lara-Brady) conducted the study for her doctoral dissertation in educational psychology at the University of Northern Colorado (Lara, 2009). Her focus was on factors that might influence the college success of Latina/o students, especially those with Mexican American backgrounds.  Phase 1 of her study involved the administration of three questionnaires; hence, it had a descriptive, quantitative nature.  Phase 2 involved in-depth interviews with a small subsample of Phase 1 participants; it made use of methods common in qualitative phenomenological studies and content analysis.
  10. 10. • Lara expresses concern about “the relatively low numbers of Latina/os attending and graduating from higher education institutions” • She then draws on related research literature to identify four potentially important factors in Latina/o students’ college success, family, religion, other people’s support, and motivation • She ties these factors to theories of child development and ethnic identity. • She repeats the four research questions she first posed and then describes her methodology. In her dissertation
  11. 11. P R O C E S S I N G A N D A N A L Y S I S O F D A T A ANALYSIS
  12. 12. ANALYSIS  After the collection of data from primary or secondary sources, arrangement is done so that the same may be analyzed & interpreted with the help of statistical tools  Software packages used: o MS Excel o SPSS (Software Packages for Social Sciences) o Google Docs etc.
  13. 13. PROCESSING DATA Editing Field Editing Central Editing Coding Classification Tabulation Graphing
  14. 14. DATA EDITING Data editing is a process by which collected data is examined to detect any errors or omissions and further these are corrected as much as possible before proceeding further. Editing is of two types:  Field editing  Central editing
  15. 15. DATA EDITING Field editing This is a type of editing that relates to abbreviated or illegible written form of gathered data. Such editing is more effective when done on same day or the very next day after the interview. The investigator must not jump to conclusion while doing field editing. Central editing Such type of editing relates to the time when all data collection process has been completed. Here a single or common editor corrects the errors like entry in the wrong place, entry in wrong unit etc. As a rule all the wrong answers should be dropped from the final results.
  16. 16. BENEFITS OF DATA EDITING  The data obtained is complete in all respects.  It isaccurate in terms of information recorded and responses sought.  The response format is in the form that was instructed.  The data is structured in a manner that entering the information will not be a problem.
  17. 17. DATA CODING The process of identifying and denoting a numeral to the responses given by the respondent is called coding Sample record: Excel sheet for two-wheeler owners Unit Column 1 occupation Column 2 Vehicle Column 3 Km/day Column 4 Marital status Column 5 Family size Column 6 1 4 1 20 1 3 2 3 2 25 2 1 3 5 1 25 1 4 4 2 1 15 2 2 5 4 2 20 2 4 6 5 2 35 2 6 7 1 1 40 1 3 8 5 2 20 2 4
  18. 18. CLASSIFICATION OF DATA  Classification of the data implies that the collected raw data is categorized into common group having common feature.  Data having common characteristics are placed in a common group.  The entire data collected is categorized into various groups or classes, which convey a meaning to the researcher. Classification is done in two ways: • Classification according to attributes. • Classification according to the class intervals.
  19. 19. ACCORDING TO ATTRIBUTES  Here the data is classified on the basis of common characteristics that can be descriptive like literacy, sex, honesty, marital status etc.  Descriptive features are qualitative in nature and cannot be measured quantitatively but are kindly considered while making an analysis.
  20. 20. ON THIS BASIS OF INTERVAL  The numerical feature of data can be measured quantitatively and analyzed with the help of some statistical unit like the data relating to income, production, age, weight etc. come under this category.  This type of data is known as statistics of variables and the data is classified by way of intervals.
  21. 21. TABULATION OF DATA  Tabulation is an orderly arrangement of data in rows and columns.  Tabulation summarizes the raw data and displays data in form of some statistical tables. Types of tables o Vertical o horizontal
  22. 22. GRAPHING OF DATA Visual representation of data  Data are presented as absolute numbers or percentages  The most informative are simple and self-explanatory It may be: • Barchart • Pie chart • Line graph • Histogram • polygon/ ogive
  23. 23. BAR CHART In a bar chart, a bar shows each category, the length of which represents the amount, frequency or percentage of values falling into a category. 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 0% At home w ith family Travel to visit family Vacation Catching up on w ork Other How Do You Spend the Holidays? 7% 5% 5% 38% 45%
  24. 24. PIE CHART  The pie chart is a circle broken up into slices that represent categories.  The size of each slice of the pie varies according to the percentage in each category. How Do You Spend the Holiday's 45% 38% 5% 5% 7% At home with family Travel to visit family Vacation Catching up on work Other
  25. 25. HISTOGRAM A graph of a data in a frequency distribution is called histogram Histogram : DailyHighTemperature 7 6 5 4 3 2 1 0 5 15 25 35 45 55 More Frequency
  26. 26. POLYGON/ OGIVE A percentage polygon is formed by having the midpoint of each class represent the data in that class and then connecting the sequence of midpoints at their respective class percentages. Fre que nc y Polygon: Daily High Te m p e r a t u r e 7 6 5 4 3 2 1 0 5 15 25 35 45 55 More Frequenc
  27. 27. ANALYSIS OF DATA Analysis means computation of certain indices or measures along with searching for patterns of relationships that exists among the data groups.
  28. 28. ANALYSIS OF DATA Descriptive & CausalAnalysis Inferential or Statistical Analysis Uni-Variate Analysis Bivariate Analysis Multi Variate Analysis Estimation of Parameter Values Testing Hypothesis Point Estimate Interval Estimate Parametric Tests Non-Parametric Tests
  29. 29. DESCRIPTIVE ANALYSIS  The study of distribution of variables is termed as a descriptive analysis.  If we are studying one variable then it will be termed as a uni-variate analysis, in the case of two variables bi-variate analysis & multi- variate analysis in the case of three & more then three variables
  30. 30. UNI-VARIATE ANALYSIS Frequent tables Measure of central tendency Arithmetic mean Median Mode Diagram chart (pie, bar chart, histograms) Measure of dispersion Range Mean deviation Standard deviation Univariate analysis refers to the analysis of one variable at a time. The commonest approaches are as follows:
  31. 31. BIVARIATE ANALYSIS Bivariate analysis is concerned with the analysis of two variables at a time in order to uncover whether the two variables are related Main types:  Simple Correlation  Simple Regression  Two-Way ANOVA
  32. 32. MULTIVARIATE ANALYSIS Mutivariate analysis entails the simultaneous analysis of three or more variables Main Types Multiple Correlation Multiple Regression Multi- ANOVA
  33. 33. CASUAL ANALYSIS Causal analysis is concerned with the study of how one or more variables affect changes in another variables
  34. 34. INFERENTIAL ANALYSIS Inferential analysis is concerned with the testing the hypothesis and estimating the population values based on the sample values.
  35. 35. PARAMETRIC TEST These tests depends upon assumptions typically that the population(s) from which data are randomly sampled have a normal distribution. Types of parametric tests are:  t- test  z- test  F- test  x2- test
  36. 36. NON PARAMETRIC TEST  Do Not Involve Population Parameters Example: Probability Distributions, Independence  Data Measured on Any Scale (Ratio or Interval, Ordinal or Nominal)
  37. 37. THANK YOU

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practical application in research, reviews of qualitative and mixed method studies, analysis, processing the data, data editing , data coding , classification of data, analysis of data, parametric test, non parametric test in Research Methodology

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