# Planning the analysis and interpretation of resseaech data

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### Planning the analysis and interpretation of resseaech data

1. 1. PLANNING THE ANALYSIS AND INTERPRETATION OF RESEARCH DATA English 4a A203 July 22, 2013
2. 2. o The design of a study does not only consist of the procedures a researcher will employ in the gathering of data but also includes the researcher’s plan on how collected data will be analyzed. o It deals with the procedures in analyzing both qualitative and quantitative data, as well as the guidelines in choosing the appropriate statistical techniques for analyzing quantitative research data.
3. 3. TYPES OF DATA ANALYZED IN RESEARCH  Qualitative Data- mostly verbal. Ex. Gender, socio economic status, religious preference  Quantitative Data- mostly numerical Ex. Scores on achievement tests, numbers of hours of study, or weight of a subject. hahahaQualitative DataQuantitative Data Overview.docx Through analysis, a researcher can do the following things; 1. Describe the data clearly; 2. Identify what is typical or atypical among the data; 3. Answer research questions or test hypothesis.
4. 4.  Qualitative are analyzed logico-inductively; 1. Observations are made of behaviors, situations, interactions, objects and environments. 2. Topics are identified from the observations and are scrutinized to discover patterns and categories. 3. Conclusions are deduced from what is observed and are stated verbally to answer research questions.
5. 5.  Quantitative Data are analyzed mathematically and the results are expressed in statistical terminology. 1. Depict what is typical and atypical among the data; 2. Show degrees of difference or relationship between two or more variables; and 3. Determine the likelihood that findings are real for the population as opposed to having occurred by chance in the sample.
6. 6. METHODS OF ANALYZING QUALITATIVE DATA
7. 7. METHODS OF ANALYZING QUALITATIVE DATA o Researcher has to present in greater details the nature or characteristics of the phenomenon or situation being described. o Data analysis may take any of the following forms: a) establishing categories or typologies and determining the sequence of events or patterns of behaviour.
8. 8. METHODS OF ANALYZING QUALITATIVE DATA  Historical Analysis- can be utilized when the researcher is after explaining events or phenomenon in the past so as to understand the present. - generalization in historical analysis are arrived at, based on the pattern of events that the researcher is able to discover. hahahaExamples of Historical Analysis.docx
9. 9. METHODS OF ANALYZING QUALITATIVE DATA  Inductive Analysis- this method of analyzing qualitative data follows the pattern of thinking and reasoning that starts from specific to universal. - the process starts from particular observations and ends up with generalization based on this specific observations.
10. 10. METHODS OF ANALYZING QUALITATIVE DATA  Deductive Analysis- exactly opposite of the inductive method of analysis. - the researcher has to begin with a general statement about a phenomenon, situation or object and ends up by providing details, particulars or specific facts to support the said general statement.
11. 11. METHODS OF ANALYZING QUALITATIVE DATA  Content Analysis- is appropriate when the researcher is concerned about explaining the status of some at a particular time or its development over a period of time using available documents. - is also called documentary analysis. - sources of data for this method are records, reports, printed forms, letters, autobiographies, diaries, books, periodicals, films, cartoons etc. hahahaHow to Do Content Analysis.docx
12. 12. METHODS IN ANALYZING QUANTITATIVE DATA
13. 13. METHODS IN ANALYZING QUANTITATIVE DATA o Quantitative analysis is employed when the data to be analyzed are numerical or information which was assigned numerical values to facilitate counting, summarization, comparison and generalization. - (Ardales, 1992) o This type of analysis relies heavily on statistical techniques. o hahahaIn.docx
14. 14. METHODS IN ANALYZING QUANTITATIVE DATA o Through statistics; researchers can- 1. Summarize data and reveal what is typical and atypical within a group; 2. Show similarities and differences among groups with the use of tests off differences; 3. Identify that is inherent in the selection of samples; 4. Test for significance of findings; and 5. Make other inferences about the population.
15. 15. ANALYTIC PROCEDURES FOR QUANTITATIVE DATA
16. 16. ANALYTIC PROCEDURES FOR QUANTITATIVE DATA  Five Types of Analysis  Descriptive Analysis- the researcher is only after describing the characteristics of the subjects under study. Data are usually analyzed to; o Identify the general characteristics of a group , with the use of descriptive statistics such as frequency, percentage, mean, median mode and o Determine the differences in the group or how members of a group vary with reference to a given variable or factor being studied with the use of standard deviation and coefficient of variation. hahahaProducing Descriptive Summary Data Using SPSS.docx
17. 17. ANALYTIC PROCEDURES FOR QUANTITATIVE DATA  Univariate Analysis- is utilized when the researcher wants to analyze one variable or factor at a time, such as levels of commitment or job performance. - relies heavily on the use of the following summary statistics: measures of central tendency; and measures of variability. hahahaFor example.docx
18. 18. ANALYTIC PROCEDURES FOR QUANTITATIVE DATA Measures of Central Tendency- to co0mmunicate where scores or observations center in the distribution. 1. Mean- is computed by dividing the sum of the values by he number of cases. 2. Median- the middlemost value in an array, such that 505 are below it and 50% are above it. 3. Mode- is the category or value with the greatest frequency of cases. It is the only acceptable indicator of the most typical case for data which are nominal or categorical.
19. 19. ANALYTIC PROCEDURES FOR QUANTITATIVE DATA Measures of Variability- reflect the amount of variation in the score of distribution. 1. Minimum and Maximum Values- the minimum value indicates how far the spread toward the lower direction and the maximum value shows the extent of spread toward the upper direction from the average. 2. Range- is the simply the distance and difference between the maximum and minimum values, showing the total spread between extremes. 3. Standard Deviation- measure of deviation that spread away from the mean. 4. Quartile Deviation- is appropriate measure of variability to employ when the median is the average used in describing a given distribution.
20. 20. ANALYTIC PROCEDURES FOR QUANTITATIVE DATA  Bivariate Analysis- this type analysis is used when the researcher is interested in probing into the relationship of two variables at a time.  Multivariate Analysis- is utilized when there are researched questions which cannot be responded using bivariate analysis. - multiple regression analysis and multiple classification analysis. hahahaExamples of multivariate regression.docx
21. 21. ANALYTIC PROCEDURES FOR QUANTITATIVE DATA  Comparative Analysis- when research participants have to be compared on the basis of certain variables being studied. Example: a researcher who is after looking into the differences in the work attitudes of the rank-and-file and managerial employees of one company, can used the aforementioned analytic procedure. hahaha1.jpg
22. 22. CHOOSING THE APPROPRIATE STATISTICAL TESTS AND TECHNIQUES
23. 23. CHOOSING THE APPROPRIATE STATISTICAL TESTS AND TECHNIQUES  Type of Research Questions- “Three types of research questions usually posed by an investigator: descriptive, relationship, and difference.” - Kumar. 1998  Nature of Raw Data- Diekhoff (1992) categorized data into three types, namely: categorical or nominal, ordinal, and metric. - if nominal or ordinal, non-parametric tests are appropriate to use; if metric, parametric tests are deemed feasible to apply.
24. 24. CHOOSING THE APPROPRIATE STATISTICAL TESTS AND TECHNIQUES 3 Types of Data  Nominal Data- data or the number of individuals or items falling under a particular category or group. Ex. When researcher records the number of respondents according to gender or civil status.  Ordinal Data- are data about rank or order.
25. 25. CHOOSING THE APPROPRIATE STATISTICAL TESTS AND TECHNIQUES Example of Likert Scale
26. 26. CHOOSING THE APPROPRIATE STATISTICAL TESTS AND TECHNIQUES  Metric Data- data which can be subjected to mathematical computations. That is can be added , subtracted, multiplied and divided. Ex. Age, temperature reading, monetary transaction and heights.  Hypothesis to be Tested- if the researcher is probing into the association of two or more characteristics of variables, he has to employ correlational statistics and tests for determining the significance of the computed correlation coefficient. - Downing & Clark. 1997
27. 27. CHOOSING THE APPROPRIATE STATISTICAL TESTS AND TECHNIQUES  Assumptions About the Nature of the Population- used either parametric tests or non-parametric tests of significance.
28. 28. SOME USEFUL PARAMETRIC AND NON- PARAMETRIC TECHNIQUES
29. 29. SOME USEFUL PARAMETRIC AND NON- PARAMETRIC TECHNIQUES - “Most research in the academe is done in one or two ways, either two or more groups are compared or variables within one group are related.” - Frankel & Wallen. 1994 - Some of the most commonly used measures of relationship and differences, as well as their uses are presented below to guide in preparing the statistical design of your research proposal.
30. 30. TESTS OF REALTIONSHIP OR ASSOCIATION  Pearson-Product Moment Correlation (R)- is calculated to show linear relationships between two variables.  Spearman Ranks (rho)- used when ranks are available for each of the two variables being related.  Coefficient of Concordance (W)- usually applied when the researcher wants to determine whether agreements exist among the rankings of three or more groups of respondents on a particular variable under study.
31. 31. TESTS OF REALTIONSHIP OR ASSOCIATION  Chi-sauare Test (X2)- used as an inferential statistics for nominal or categorical data. When employed as test relationship, it is called test independence. When used as a test difference, it is considered a test of homogeneity.  Cramer’s V Statistics- used for assessing the strength of the association between two variables which were found to be significantly related through the chi0square test of independence .
32. 32. TESTS OF REALTIONSHIP OR ASSOCIATION  Point Biserial Correlation- used as a measure of relationship between two variable, where ne is continous and the other is dichotomous.  Phi Coefficient- another measure of relationship appropriate when two variables correlated are both dichotomous.  Coefficient of Determination and Alienation- these two measures have to be computed when a significant R is obtained.
33. 33. TESTS OF REALTIONSHIP OR ASSOCIATION  Partial Correlation- is a correlational method involving two or more variables.  Multiple Correlation- is a measure of relationship appropriate when one dependent variable is related to two or more independent or predictor variables.
34. 34. TESTS OF DIFFERENCE  T test for Independent Samples- a parametric test that is used in determining whether the mean value of a variable in one group of subjects is different from the mean value on the same variable with the same group of subjects.  Fmax Hartley Test- is used in comparing the standard deviations or variances of two or more groups of research subjects on a variable being studied.  Mann-Whitney U Test- is the non-parametric counterpart of the independent T test.
35. 35. TESTS OF REALTIONSHIP OR ASSOCIATION  Sign Test- can also be used in determining the significance of differences between two sets of data from correlated samples.  Median Test- is a sign test for two independent samples, in contrast to two correlated samples.  Critical Ratio or Z test- often used in determining the significance of differences between two give percentages or proportions, when they are not correlated.
36. 36. TESTS OF REALTIONSHIP OR ASSOCIATION  T test for Correlated Samples- is used when two groups that have been matched are being compared as in a pretest-posttest design to see if any bserved mean gain is significant.  Sandler’s A Test- is the non-parametric analog of the T test for correlated samples.  Wilcoxon Rank Sum Test- is another non- parametric alternative to difference of means for correlated samples.
37. 37. TESTS OF REALTIONSHIP OR ASSOCIATION  Kolmoorov-Smirnov Test- this test fulfills the function of the chi-square test in testing the goodness-of-fit and the Wilcoxon Rank Sum Test in determining whether the random samples are from the same population.  Analysis of Variance (ANOVA)- used when the researcher wants to find out if there are significant differences between the means of two or more groups on a variable under study.  Kruskall-Wallis H Test- this test looks for the significance of differences among three or more groups on a variable under study.
38. 38. TESTS OF REALTIONSHIP OR ASSOCIATION  Friedman Analysis of Variance (Fr)- is the non-parametric analog of two-way ANOVA.  Analysis of Covariance (ANCOVA)- a statistical technIque for equating groups in one or more variables when testing for statistical significance.  Multivatiate Analysis Of Variance (MANOVA)- is an extension of ANOVA, which incorporates two or more dependent variables in the same analysis.