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6-1. 6-2 McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved. Part Two THE DESIGN OF RESEARCH.

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Presentation on theme: "6-1. 6-2 McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved. Part Two THE DESIGN OF RESEARCH."— Presentation transcript:

1 6-1

2 6-2 McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved. Part Two THE DESIGN OF RESEARCH


4 6-4 What is Research Design? A plan for selecting the sources and types of information used to answer research questions A framework for specifying the relationships among the study variables A blueprint that outlines each procedure from the hypothesis to the analysis

5 6-5 Classifications of Designs Exploratory study is usually to develop hypotheses or questions for further research Formal study is to test the hypotheses or answer the research questions posed

6 6-6 Methods of Data Collection Monitoring, which includes observational studies Interrogation/communication studies

7 6-7 Power to Produce Effects In an experiment, the researcher attempts to control and/or manipulate the variables in the study In an ex post facto design, the researcher has no control over the variables; they can only report what has happened

8 6-8 Purpose of the Study Descriptive study tries to explain relationships among variables Causal study is how one variable produces changes in another

9 6-9 The Time Dimension Cross-sectional studies are carried out once and represent a snapshot of one point in time Longitudinal studies are repeated over an extended period

10 6-10 The Topical Scope Statistical studies attempt to capture a population’s characteristics by making inferences from a sample’s characteristics Case studies place more emphasis on a full contextual analysis of fewer events or conditions and their interrelations

11 6-11 The Research Environment Field conditions Laboratory conditions Simulations

12 6-12 A Participant’s Perceptions Usefulness of a design may be reduced when people in the study perceive that research is being conducted Participants’ perceptions influence the outcomes of the research

13 6-13 Why do Exploratory Studies? Exploration is particularly useful when researchers lack a clear idea of the problems

14 6-14 Data Collection Techniques Qualitative techniques Secondary data Focus groups Two-stage design

15 6-15 Causation The essential element of causation is –A “produces” B or –A “forces” B to occur

16 6-16 Causal Study Relationships Symmetrical Reciprocal Asymmetrical

17 6-17 Asymmetrical Relationships Stimulus-Response Property-Disposition Disposition-Behavior Property-Behavior

18 6-18 Achieving the Ideal Experimental Design Control –Random Assignment –Matching Randomization –Manipulation and control of variables

19 6-19 Chapter Seven SAMPLING DESIGN

20 6-20 Selection of Elements Population Population Element Sampling Census

21 6-21 What is a Good Sample? Accurate: absence of bias Precise estimate: sampling error

22 6-22 Types of Sampling Designs Probability Nonprobability

23 6-23 Steps in Sampling Design What is the relevant population? What are the parameters of interest? What is the sampling frame? What is the type of sample? What size sample is needed? How much will it cost?

24 6-24 Concepts to Help Understand Probability Sampling Standard error Confidence interval Central limit theorem

25 6-25 Probability Sampling Designs Simple random sampling Systematic sampling Stratified sampling –Proportionate –Disproportionate Cluster sampling Double sampling

26 6-26 Designing Cluster Samples How homogeneous are the clusters? Shall we seek equal or unequal clusters? How large a cluster shall we take? Shall we use a single-stage or multistage cluster? How large a sample is needed?

27 6-27 Nonprobability Sampling Reasons to use Procedure satisfactorily meets the sampling objectives Lower Cost Limited Time Not as much human error as selecting a completely random sample Total list population not available

28 6-28 Nonprobability Sampling Convenience Sampling Purposive Sampling –Judgment Sampling –Quota Sampling Snowball Sampling

29 6-29 Chapter Eight MEASUREMENT

30 6-30 Measurement Selecting observable empirical events Using numbers or symbols to represent aspects of the events Applying a mapping rule to connect the observation to the symbol

31 6-31 What is Measured? Objects: –Things of ordinary experience –Some things not concrete Properties: characteristics of objects

32 6-32 Characteristics of Data Classification Order Distance (interval between numbers) Origin of number series

33 6-33 Data Types OrderIntervalOrigin Nominalnonenonenone Ordinalyesunequalnone Intervalyesequal ornone unequal Ratioyesequalzero

34 6-34 Sources of Measurement Differences Respondent Situational factors Measurer or researcher Data collection instrument

35 6-35 Validity Content Validity Criterion-Related Validity –Predictive –Concurrent Construct Validity

36 6-36 Reliability Stability –Test-retest Equivalence –Parallel forms Internal Consistency –Split-half –KR20 –Cronbach’s alpha

37 6-37 Practicality Economy Convenience Interpretability

38 6-38 Chapter Nine MEASUREMENT SCALES

39 6-39 What is Scaling? Scaling is assigning numbers to indicants of the properties of objects

40 6-40 Types of Response Scales Rating Scales Ranking Scales Categorization

41 6-41 Types of Rating Scales Simple category Multiple choice, single response Multiple choice, multiple response Likert scale Semantic differential Numerical Multiple rating Fixed sum Stapel Graphic rating

42 6-42 Rating Scale Errors to Avoid Leniency –Negative Leniency –Positive Leniency Central Tendency Halo Effect

43 6-43 Types of Ranking Scales Paired-comparison Forced Ranking Comparative

44 6-44 Dimensions of a Scale Unidimensional Multidimensional

45 6-45 Scale Design Techniques Arbitrary scaling Consensus scaling Item Analysis scaling Cumulative scaling Factor scaling

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