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Quantitative Research Design.pptx
- 1. Dr. Alok Kumar Gaurav Assistant Professor Department of Public Administration QUANTITATIVE RESEARCH DESIGN
- 2. Quantitative Research Design relates to the design of a research project which uses quantitative research methods. The design varies depending on the method used, which could be telephone interviews, face-to-face interviews, online surveys, or surveys by post for instance. Other methodologies include SMS / Test Message surveys, or physical counts.
- 3. Quantitative Research Design aimed at discovering how many people think, act or feel in a specific way. Quantitative projects involve large sample sizes, concentrating on the quantity of responses, as opposed to gaining the more focused or emotional insight that is the aim of qualitative research. The standard format in quantitative research design is for each respondent to be asked the same questions, which ensures that the entire data sample can be analysed fairly. The data is supplied in a numerical format, and can be analysed in a quantifiable way using statistical methods. Surveys can, however, be tailored to branch off if the respondent answers in a certain way - for instance people who are satisfied or dissatisfied with a service may be asked different questions subsequently.
- 4. Quantitative research design tends to favour closed-ended questions . Providing respondents with a set list of answers, they will not normally be able to give lengthy open-ended responses. This design ensures that the process of quantitative research is far more efficient than it would be if qualitative-style open ended questions were employed. It is more efficient because it is then not necessary to carry out the time-consuming process of coding vast quantities of open-ended responses. Quantitative research design does often allow the inclusion of an Other category in the list of possible responses to questions, This allows those respondents who do not fit directly into the main categories to still get their precise responses recorded and used in the analysis of the research project results.
- 5. QUANTITATIVE Quantitative research determine the relationship between one thing (an independent variable) and another (a dependent or outcome variable) in a population. Quantitative research designs descriptive (subjects usually measured once) experimental (subjects measured before and after a treatment).
- 7. A Descriptive Design seeks to describe the current status of a variable or phenomenon. The researcher does not begin with a hypothesis, but typically develops one after the data is collected. Data collection is mostly observational in nature. A Correlational Design explores the relationship between variables using statistical analyses. it does not look for cause and effect and therefore, is also mostly observational in terms of data collection. A Quasi- Experimental Design (often referred to as Causal-Comparative) seeks to establish a cause-effect relationship between two or more variables. The researcher does not assign groups and does not manipulate the independent variable. Control groups are identified and exposed to the variable. Results are compared with results from groups not exposed to the variable. Experimental Designs, often called true experimentation, use the scientific method to establish cause- effect relationship among a group of variables in a research study. Researchers make an effort to control for all variables except the one being manipulated (the independent variable). The effects of the independent variable on the dependent variable are collected and analyzed for a relationship.
- 9. DATA ANALYSIS FOR QUANTITATIVE RESEARCH Descriptive Statistics Inferential Statistics
- 10. Descriptive
- 12. Quantitative Data Obtained when the variable being studied is measured along a scale that indicates how much of the variable present. Reported in terms of scores. Higher scores indicate that more of the variable Ex: The amount of money spent on sports equipment by various schools Categorical Data Simply indicate the total number of objects, individuals or events a particular category. Ex: The representation of each ethnic group in a school.
- 13. Raw Scores Initial score obtained Difficult to interpret and it has a little meaning. Derived Scores Obtained by taking raw scores and converting them into more useful scores. Age and Grade-level Equivalents Tell us of what age or grade an individual score is typical. Percentile Ranks Refers to percentage of individuals scoring at or below a given raw score Standard Scores Provide an other means of indicating how one individual compares to other individuals in a group
- 14. Frequency Polygons Skewed Polygons Histograms and Stem-Leaf Plots The Normal Curve Averages Spreads Standard Scores and the Normal Curve Correlation
- 15. When the data are simply listed in no apparent order, it is difficult to tell. We must put the information into some sort of order. Frequency distribution list the scores in rank order from high to low (Table 10.2) Grouped frequency distribution information grouped into intervals and quite informative (Table 10.3) Frequency polygon present the data in graph (graphical display). 64, 27, 61, 56, 52, 51, 34, 17, 27, 17, 24, 64, 31, 29, 31, 29, 29, 31, 31, 59, 56, 31, 27, 17 Listed below are the scores of a group of students on mid semester biology test.
- 17. Skewed Positively Skewed Polygon The tail of the distribution trails off to the right, in the direction of the higher scores values. Negatively Skewed Polygon The longer tail of the distribution goes off to the left, in the direction of the lower scores values.
- 18. & Histogram is a bar graph used to display quantitative data at the interval or ratio level of measurement. Arranged from left to right on the horizontal axis. At the intersection of the two axis is always zero. Steam-leaf plot is a display that organizes a set of data to show both in shape and distribution. Each data value is split into stem and leaf. The leaf usually the last digit of the number and the other digits to the left is stem. Ex: 149 leaf 9 stem 14
- 19. The The smooth curve not just connecting the series of dots, but rather showing a generalized distribution of scores that is not limited to one specific set of data. This smooth curves are known as distribution curves. When a distribution curve is normal, the large majority of the scores concentrated in the middle and the scores decrease in frequency far away from the middle. It is based on a precise mathematical equation. Useful for researchers.
- 20. Mod Median Is the point below and above which 50 percent of the scores in distribution fall (midpoint). 7, 6, 5, 4, 3, 2, 1 Mean It is determined by adding up all the scores and then dividing this sum by the total number of scores. Is the most frequent score in a distribution 25, 20, 19, 17, 16, 16, 16, 14, 14, 11,10, 9, 9 70, 74, 82, 86, 88, 90 Median is 84 the point halfway between the two middlemost scores. 52, 68, 74, 86, 95, 105 The mean score is 80.
- 21. Is the extent to which a distribution is stretched or squeezed. The two distributions differ in what statisticians call variability. Inter Quartile Range Overall Range Standard Deviation The most useful index of variability. A single number that represents the spread of a distribution. A B
- 23. Standard Scor&es T scores z scores A raw score that is exactly on the mean corresponds to a z score of zero. A raw score that is exactly one SD above the mean equals a z score of +1, while below the mean equals a z score of -1. Ex: Mean = 50, SD = 2 48 50 52 z score +2 z score +2
- 24. z scores Of course z scores are not always exactly one or two standard deviation away from the mean.
- 25. Convert the raw scores below the mean from negative to positive. One way to eliminate negative z scores and convert them to T scores. T scores = (z scores x 10) + mean
- 26. When two sets of data are strongly linked together we say they have a High Correlation. The word Correlation is made of Co- (meaning "together"), and Relation. Correlation is Positive when the values increase together, and. Correlation is Negative when one value decreases as the other increases. scatterplots A pictorial representation of the relationship between two quantitative variables.
- 27. INFERENTIAL STATISTICS Inferential statistics allow researchers to make inferences about a population based on findings (data they obtain) from a sample.
- 28. Relate to data that are flexible and do not follow a normal distribution. Make no assumptions. INFERENTIAL STATISTICS PARAMETRIC NON-PARAMETRIC Make assumptions about the parameters (defining properties) of the population distribution(s) from which one's data are drawn, T-test ANOVA Pearson Correlation ANCOVA Chi-Square Friedman Mann-Whitney U test Spearman Correlation
- 29. PARAMETRIC INFERENTIAL STATISTICS T-test is used to determine whether there is a significant difference between the means of two groups. Analysis of variance (ANOVA) is used to check if the means of two or more groups are significantly different from each other. Pearson's Correlation Coefficient is a measure of the strength of the association between the two variables.
- 30. NON-PARAMETRIC INFERENTIAL STATISTICS Chi square test is any statistical hypothesis test where the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. The Mann-Whitney U test is used to compare differences between two independent groups when the dependent variable is either ordinal or continuous, but not normally distributed. The Friedman test is used to test for differences between groups when the dependent variable being measured is ordinal. The Spearman is often used to evaluate relationships involving ordinal variables.