Download
recap research says n.
Skip this Video
Loading SlideShow in 5 Seconds..
RECAP RESEARCH SAYS PowerPoint Presentation
Download Presentation
RECAP RESEARCH SAYS

RECAP RESEARCH SAYS

0 Views Download Presentation
Download Presentation

RECAP RESEARCH SAYS

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. RECAPRESEARCH SAYS

  2. LESSON 4: Importance of Quantitative Research and Research Variables

  3. Pre-Activity1. Determine the type of Quantitative Research suited in the following and explain: a. Behavior of Students towards studying b. Perception of people about “Kulam” c. Performance of STEM students in Bio 1 d. Modular Approach in studying math e. Effectiveness of MTAP in developing math skills

  4. Some Central Issues in Quantitative Research • Description versus Explanation (notes follow Punch, 2015) • Goal of scientific inquiry is not just to describe phenomena, but to explain them and to make predictions based on the patterns that are uncovered and their putative causes or at least their antecedents

  5. The purpose of description is to give an account of the phenomenon in order to improve understanding and reduce complexity by extracting the features necessary to achieve understanding. The “what” • The purpose of explanation is to locate the causal underpinnings of events; to create a “story” in which the reasons for things or the enabling or antecedent conditions which give rise to them are laid out. The “why” and/or the “how”.

  6. Description versus Explanation, continued • How does explanation relate to theory? Theory is a systematic, sometimes axiomatic effort to explain a phenomenon or group of phenomena. Both descriptive and explanatory studies have a role in the development of theory. • Theory verification vs. theory generation • Theory verification: from theory we derive hypotheses which are then tested. More likely to be quantitative • Theory generation: theory derived from the data we have collected and described, using a defined strategy such as grounded theory. More likely to be qualitative

  7. Description versus Explanation • Presumably if we know what happened, why it happened, and how it happened, we will be able to • Predict under what circumstances it might happen again and • Possibly prevent/encourage it from happening again by removing/encouraging motivating or enabling conditions. • Although generally speaking purely descriptive studies have less appeal to journal editors, descriptive research is important • When the phenomena of interest are new and unexplored, or • When the researcher is attempting to isolate causal factors for testing with confirmatory methods • Description is of course the essential work of ethnography

  8. Tight versus Loose Structure • How much structure is applied to the research ahead of time, with respect to • Research questions • Can range from explicitly stated hypotheses to research questions to completely exploratory work with no formulated research questions and only a loose conceptual framework, if any, for guidance

  9. Research design • Can range from experimental designs with planned comparisons among conditions and control of potentially confounding variables, to quasi-experimental design to case studies and ethnographies • Data • Can range from ratio level measurement where the obtained data is coded into pre-established categories based on valid, reliable, established measures to inductive categorization based on categories which emerge from the researcher’s immersion in the data

  10. Tight versus Loose Structure, continued • Typically a quantitative study will introduce the most explicit forms of these three sorts of structure (research questions, research design, data) , at the beginning or very early in the research process

  11. Uses of Quantitative Research • It doesn’t make much sense to talk about one approach as being superior to the other. • Both have their uses and can complement each other and lead to further interesting questions when the methods yield conflicting findings. • The important thing is to decide what circumstances might make application of quantitative methods appropriate.

  12. Quantitative methods are appropriate when measurement can offer a useful description • We can say that someone is 6 feet tall, or we can say that she is “the tallest in the group”, or that she is “one of the tall ones” (interval, ordinal, nominal), or we could say that she is an “Amazon”, or someone who “towers over this researcher” It depends on what we want to do with the information. Sometimes we want to use very precise metrics, and sometimes we want to make comparisons, and sometimes we want to provide a subjective impression of an object of study in reference to ourselves.

  13. Is Measurement Relevant, Useful, Possible? • Is measurement relevant and possible? • One of the questions we have to ask is at what level it might be possible to measure something. • Suppose we have a group of ten cat owners. What measures would we use to sort them out by how much they love their cats? • A. Amount spent on vet bills? Time per day spent petting, grooming? • B. Could we order them from 1 to 5, where number 4 loves her cat ten times as much as number 1? • Could we merely classify them into very high, high, average, low and very low love? Would a narrative description gained from interviews and/or observations be more useful?

  14. Are there Generalizations to be Made and or Hypotheses to be Tested? • Quantitative methods are appropriate when there are statistical generalizations to be made/hypotheses to be tested • One of the reasons for using statistics is that we want to be able to generalize about the attitudes, beliefs, and behaviors of people on the basis of a set of observations that will probably be limited by various constraints including financial ones. • Am I able to describe precisely the population to which I want to generalize? • Do I have confidence that I can draw a sample that is large enough and representative enough to stand in for the population?

  15. IMPORTANCE OF QUANTITATIVE RESEARCH • More reliable and objective • Can use statistics to generalise a finding • Often reduces and restructures a complex problem to a limited number of variables • Looks at relationships between variables and can establish cause and effect in highly controlled circumstances • Tests theories or hypotheses • Assumes sample is representative of the population • Subjectivity of researcher in methodology is recognized less • Less detailed than qualitative data and may miss a desired response from the participant

  16. Common Terms in Quantitative Research • Phenomenon: the object of study; the behaviors, beliefs, attitudes, characteristics, and their purported inter-relationships that we seek to describe, explain, and/or predict (e.g. How competent people are at using computers) • Variable: an observable property or characteristic that can be measured (operationalized) and is expected to vary across cases or observational instances, such as what people report about their ability to use computers, or, what computer skills people can demonstrate, etc. • Measurement: a plan for operationalizing the variable, such as using a particular scale or observational technique (e.g. Durndell and Haag self-report measure of computer self-efficacy) • Data: the obtained values which the researcher ascribes to individual measured instances of the variable (the numbers researchers assign to the answers study subjects give to items on the self-efficacy scale)

  17. Terms in Quantitative Research, continued • Population: the totality of cases which constitute the sphere within which the phenomenon is to be observed. Could be people in general, people in the US, college students, children under 12, could also be states, cities, animal shelters, department stores, countries, etc. • Sample: some portion of the population which is believed to be representative • Descriptive statistics: statistically derived values that represent the central tendencies and variability with a body of data • Sampling statistics: values used to make inferences about the characteristics of the population from which they were drawn, including the variation of the sample characteristics from corresponding population parameters

  18. Variables– Characteristics or property whereby the members of the group set differ from one another. • A. Independent Variables – “X variable or Response Variable” • - Characteristics or condition that is introduced, removed or manipulated to cause a change in the dependent variable that is to be observed or measured. • - Predictor of Input • - assumed to be causal (logic, time order, ascribed vs. achieved characteristics)

  19. B. Dependent Variable – “Y variable or Outcome Variable” - Characteristics or condition that is observed and measured to find out how the independent variable affects it. - (Outcome or attitude variable) - Dependent variable: assumed to be an effect or result of one or more other variables

  20. Z Variable – “Moderator Variable” • - Extraneous variable • - Secondary independent variable that maybe included or mostly not and measured in the study to determine whether it affects, modify or alter the relation between the independent and dependent variable.

  21. APPLICATION :Identify the variables in the following titles and tell whether such variable is independent or dependent:1. Relationship between age and academic performance2. Correlation between leadership skills and educational qualifications3. The effects of light to the growth of mongo seeds.4. Instructional media in relation to scores in quizzes5. Effects of attitude to grades in Math6. Socio-economic status in relation to attendance in classroom7. Teacher’s competence and passing rate of students