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Research Methodology For reader assistance, have an introductory paragraph in which attention is given to the organization of the section in relation to.

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Presentation on theme: "Research Methodology For reader assistance, have an introductory paragraph in which attention is given to the organization of the section in relation to."— Presentation transcript:

1 Research Methodology For reader assistance, have an introductory paragraph in which attention is given to the organization of the section in relation to the problem and/or hypotheses. The main purpose of this section is to ensure the reader can readily determine how each question/hypothesis/sub-problem is to be handled. Can a reasonable literate person conduct the study as intended based on the described procedures and methods of analysis?

2 Research Design provides a framework for the collection and analysis of data.  the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.  constitutes the blueprint for the collection, measurement, and analysis of data.  the plan, structure, and strategy of investigation conceived so as to obtain answers to research questions.

3 Essentials of good research design 1. It is a plan that specifies the sources and types of information relevant to the research question. 2. It is a strategy or blueprint specifying which approach will be used for gathering and analyzing the data. 3. It includes both time and cost budgets.

4 Common research designs used by graduate students 1. Evaluation Research Design  Appraises quality or effectiveness of given products, materials, procedures, programs, and the like.  Uses list of criteria and judgments made against criteria are stated verbally and supported by statistics.

5 2. Descriptive Research Design  Describes phenomena associated with or characteristics of a subject population.  Topics can include people, places, situations, conditions, procedures, interactions, and undertakings about which a researcher wishes to know more.

6 3. Correlational Research Design  Identifies variables that relate to each other.  Make predictions of one variable trait from the other.  Examines the possible existence of causation.

7 4. Experimental Research Design Convincingly demonstrates cause-effect relationships. Manipulates or makes changes in an independent variable, possibly producing corresponding changes in the dependent variable. 5. Quasi-Experimental Research Design Shows cause and effect but less convincingly than experimental research design because the participants involved have not been selected at random

8 6. Causal-Comparative Research Design or Ex Post Facto Research Design Suggests cause and effect but even less convincingly because the independent variable is fixed and cannot be, or is not, manipulated due to one of these three reasons: i. the independent variable is fixed (gender) ii. manipulation of the independent variable would be unethical iii. manipulating the independent variable would be impractical

9 The cause-effect linkage is made logically as the research process proceeds as follows: i. It focuses on the effect. ii. It asks what might be causing that effect. iii. It attempts to identify and substantiate a plausible connection between the effect and its cause.

10  Identifies a criterion group, which is composed of people who have been observed, judged, or who describe themselves as possessing a certain characteristic that differentiates them from others and examines the possible causes for these differences.

11 Sampling Design 1. Probability Sampling a category of sample selection procedures in which one can state the probability (likelihood) of each member of the population being selected for the sample and in which there is a constant probability of selection for each member of the population.

12 i. Random Sampling (simple random sampling)  done in such a way that each individual in the total population has an equal chance of being selected. ii. Stratified Sampling used when researchers want to ensure that subgroups within the population are represented proportionally or equally in the sample

13 iii. Cluster Sampling involves the random selection of groups that already exist iv. Systematic Sampling  done when all members of the population are named on a master list and the sample is drawn directly from that list.

14 2. Non-probability Sampling a sampling procedure in which the probability of inclusion for each member of the population cannot be specified. i. Convenience Sampling uses groups of participants that simply happen to be available

15 ii. Judgmental Sampling (Purposive Sampling) used to select certain segments of the population for study where the researcher uses his or her judgment as to which segments should be included more appropriate for qualitative research where making generalizations to the entire population is not the focus

16 iii. Snowball (network or chain) Sampling begins with the identification of a few initial participants, then the researcher asks these participants to recommend other individuals who meet certain criteria and who might be interested in participating in the research study  useful for getting started when you are unable to identify another means of finding the participants you want.

17 iv. Quota Sampling done when an investigator wants to do research applicable to the population but for whatever reason is not able to draw a sample from the population the investigator intentionally constructs a sample that seems to have the same characteristics as the population

18 Size of Samples A minimum sample size depends on the type of research study being conducted. Correlational research – not less than 30  Experimental research – not less than 15 per group and at least 5 groups per different research treatment Descriptive research – 10 to 20% of the population  for large population (5,000 or more), a sample size of 400 will provide adequate representation.

19 Sample accuracy is more important than sample size as improperly selected sample, though large in number, can lead to invalid conclusions. Statistical significance becomes less likely as the sample size becomes smaller.


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