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6-2McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights

Reserved.

Part TwoTHE DESIGN OF RESEARCH

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Chapter SixDESIGN STRATEGIES

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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

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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

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Methods of Data Collection

• Monitoring, which includes observational studies

• Interrogation/communication studies

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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

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Purpose of the Study

• Descriptive study tries to explain relationships among variables

• Causal study is how one variable produces changes in another

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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

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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

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The Research Environment

• Field conditions

• Laboratory conditions

• Simulations

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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

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Why do Exploratory Studies?

• Exploration is particularly useful when researchers lack a clear idea of the problems

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Data Collection Techniques

• Qualitative techniques

• Secondary data

• Focus groups

• Two-stage design

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Causation

• The essential element of causation is

– A “produces” B

or

– A “forces” B to occur

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Causal Study Relationships

• Symmetrical

• Reciprocal

• Asymmetrical

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Asymmetrical Relationships

• Stimulus-Response

• Property-Disposition

• Disposition-Behavior

• Property-Behavior

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Achieving the Ideal Experimental Design

• Control

– Random Assignment

– Matching

• Randomization

– Manipulation and control of variables

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Chapter SevenSAMPLING DESIGN

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Selection of Elements

• Population

• Population Element

• Sampling

• Census

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What is a Good Sample?

• Accurate: absence of bias

• Precise estimate: sampling error

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Types of Sampling Designs

• Probability

• Nonprobability

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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?

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Concepts to Help Understand Probability Sampling

• Standard error

• Confidence interval

• Central limit theorem

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Probability Sampling Designs

• Simple random sampling

• Systematic sampling

• Stratified sampling– Proportionate– Disproportionate

• Cluster sampling

• Double sampling

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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?

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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

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Nonprobability Sampling

• Convenience Sampling

• Purposive Sampling– Judgment Sampling– Quota Sampling

• Snowball Sampling

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Chapter EightMEASUREMENT

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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

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What is Measured?

• Objects: – Things of ordinary experience – Some things not concrete

• Properties: characteristics of objects

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Characteristics of Data

• Classification

• Order

• Distance (interval between numbers)

• Origin of number series

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Data Types

Order Interval OriginNominal none none none

Ordinal yes unequal none

Interval yes equal or none

unequal

Ratio yes equal zero

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Sources of Measurement Differences

• Respondent

• Situational factors

• Measurer or researcher

• Data collection instrument

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Validity

• Content Validity

• Criterion-Related Validity– Predictive– Concurrent

• Construct Validity

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Reliability

• Stability– Test-retest

Equivalence– Parallel forms

• Internal Consistency– Split-half– KR20– Cronbach’s alpha

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Practicality

• Economy

• Convenience

• Interpretability

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Chapter NineMEASUREMENT SCALES

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What is Scaling?

• Scaling is assigning numbers to indicants of the properties of objects

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Types of Response Scales

• Rating Scales

• Ranking Scales

• Categorization

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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

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Rating Scale Errors to Avoid

• Leniency– Negative Leniency– Positive Leniency

• Central Tendency

• Halo Effect

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Types of Ranking Scales

• Paired-comparison

• Forced Ranking

• Comparative

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Dimensions of a Scale

• Unidimensional

• Multidimensional

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Scale Design Techniques

• Arbitrary scaling

• Consensus scaling

• Item Analysis scaling

• Cumulative scaling

• Factor scaling