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研究設計 Research Design Emily Lin, PhD. (林永芬)

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Presentation on theme: "研究設計 Research Design Emily Lin, PhD. (林永芬)"— Presentation transcript:

1 研究設計 Research Design Emily Lin, PhD. (林永芬)
Department of Communication Disorders University of Canterbury Christchurch, New Zealand Taiwan Academy of Physical Medicine and Rehabilitation Conference: Current Intervention for Children with Developmental Delay Taoyuan, Taiwan December 2, 2006

2 Outline Basic Concepts Experimental Research Group Design
Single-Subject Design E. Lin

3 What is Research? “Research is the process of investigating
scientific questions.” To satisfy the need to: 1. explain events 2. solve practical problems 3. demonstrate certain effects legal, social, professional, and scientific considerations (Hegde, 2003) E. Lin

4 Research Process Phase I: Identify the research question
define the research problem review literature; provide theoretical framework identify target population identify variables state research rationale clarify objectives state specific purposes or hypotheses Phase II: Design the study (i.e., design protocol, select a sample) Phase III: Methods (i.e., collect data, reduce data) Phase IV: Data Analysis (i.e., analyze data, provide interpretation) Phase V: Communication (i.e., report findings, suggest future studies) (Portney & Watkins, 2000) E. Lin

5 Purpose of Research To determine the relationship between variables*
*Some basic terms: 1. constant (常數) 2. variable (變數): -independent variable -dependent variable -extraneous variable E. Lin

6 Types of Research e.g., e.g., Case study Experimental Developmental
Descriptive Exploratory Experimental (Describe population) (Find relationships) (Cause and effect) e.g., Case study Developmental research Normative Qualitative Correlational e.g., Experimental (randomized controlled) Quasi- experimental Sequential clinical trial Single-subject designs (Portney & Watkins, 2000) E. Lin

7 What is an experiment (實驗)?
Manipulation of variables: Independent variables are manipulated through: -administration of treatment, or -deliberate operation imposing predetermined experimental conditions combined with classification (Quasi-experiment) Random selection/assignment: Obtain a representative sample Establish equivalency between comparison groups E. Lin

8 Selecting a Study Sample
Target population (reference population) Accessible population (experimental population) Subject selection Participants (Study sample) Non-participants Group assignment Experimental group Control group (comparison groups) (Portney & Watkins, 2000) E. Lin

9 Sampling Strategy Probability sampling: Nonprobability sampling:
Simple random sampling Systematic sampling Stratified random sampling Disproportional sampling Cluster sampling Nonprobability sampling: Convenience sampling Quota sampling Purposive sampling Snowball sampling (Portney & Watkins, 2000) E. Lin

10 Compensations for Lack of Random Sampling
Homogeneous groups Matching Control Build in extraneous factors Blocking E. Lin

11 Validity (效度) Internal validity: External validity: generalization
the degree a cause-and-effect inference can be made based on the observed relationship between the variables “Measure what is claimed to be measured” External validity: generalization “Generalize to other situations” E. Lin

12 Threats to Internal Validity
Potential for confounding factors to interfere with the relationship between the independent and dependent variables: e.g., History Maturation Mortality/attrition Testing or test-practice effects Statistical regression Differential selection of subjects Instrumentation (Schiavetti & Metz, 1997) E. Lin

13 Threats to External Validity
Factors limiting “the degree to which internally valid results can be generalized”: e.g. Subject selection Reactive or interactive effects of pretesting Reactive arrangement (Hawthorne effect) Multiple-treatment interference (Schiavetti & Metz, 1997) E. Lin

14 Measurement Purpose: to provide a mechanism for achieving a degree of precision in the understanding of the characteristics of the object of interest Key elements: Construct Rules Evaluation of a measurement: 1. validity (效度): measuring what was intended 2. reliability (信度): yielding consistent results E. Lin

15 Level of Measurement With absolute zero Equal intervals Ranking
Category E. Lin

16 Test Validity 1. Face validity: appears to test what is supposed to test. 2. Content validity: consists of items that adequately sample the content that defines the variable being measured. 3. Criterion-related validity: yields outcomes that can be used as a substitute measure for an established gold standard criterion test. Concurrent validity Predictive validity 4. Construct validity: the degree the test measures an abstract construct E. Lin

17 Threats to Test Validity
Length effect: e.g., fatigue, learning Enabling behaviors required of the test taker The representativeness of the norm Bias Reliability: e.g., test-retest, inter-judge E. Lin

18 Reliability 1. Test-retest reliability:
Repeat the whole test or a portion of test Conduct a parallel test Split-half method (internal consistency) 2. Inter and intra-judge reliability: Total reliability Trial-by-trial (point-by-point) reliability Occurrence reliability Nonoccurrence reliability E. Lin

19 Three Basic Measurements In Descriptive Statistics
Central tendency: the average (“center”) score of a distribution; mean, median, mode Variability: the dispersion of scores; range, standard deviation (SD) 3. Relative position: a score’s position within a distribution; percentile, z-score E. Lin

20 Inferential Statistics
Sample Population Known Unknown *Difference between subjective inference and statistical inference: Statistical inference requires objective criteria to make decisions. Inferential statistics: Decision-making process To estimate population characteristics from sample data Assumptions made about how well the sample represents the larger population. The assumptions are based on two concepts of statistical reasoning: Probability Sampling error E. Lin

21 Hypothesis Testing Null hypothesis (statistical hypothesis; H0):
the group difference is due to sampling error Alternative hypothesis (research/scientific hypothesis; H1): the research hypothesis is correct The purpose of posing a research hypothesis: usually with the intention to reject the null hypothesis E. Lin

22 Hypothesis Testing: Type I and II errors
(Real Situation ) H0 is true H0 is false Reject H0 Type I error (a) Correct decision (1-b) (Power of test) (Decision) Accept H0 Correct decision (1-a) Type II error (b) a : significance level Power (1-b): the probability that a test will produce a significant difference at a given significance level Type I error: The error that results when null hypothesis is falsely rejected. Type II error: The error that results when null hypothesis is falsely accepted. E. Lin

23 Hypothesis Testing (Portney & Watkins, 2000) E. Lin

24 Group Design One-shot case study One-group pretest-posttest design
Static-group comparisons Pretest-posttest control group design Posttest-only control group design Solomon four-group design Multigroup pretest-posttest design Multigroup posttest-only design E. Lin

25 Group Design -continued
Factorial designs Single-group time-series design Multiple-group time-series design One-group single-treatment counter-balanced design Crossover design Correlational analysis E. Lin

26 Group Design: One-Way Design for Independent Groups
10 S11 10 S21 10 S2 S3 S4 S5 S6 S7 S8 S9 S10 S12 S13 S14 S15 S16 S17 S18 S19 S20 S22 S23 S24 S25 S26 S27 S28 S29 S30 Analysis Method: One-way ANOVA E. Lin

27 Group Design: One-way Within-Subjects Design
10 10 10 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 Analysis Method: One-way Repeated Measures ANOVA E. Lin

28 Two-way Factorial Design
Factors: -Factor A (teaching method): 2 levels (oral vs. visual) -Factor B (gender): 2 levels (female vs. male) This is a two-by-two (2X2) design. A1 A2 S1 S2 S3 S4 S5 5 S11 S12 S13 S14 S15 5 B1 S6 S7 S8 S9 S10 5 S16 S17 S18 S19 S20 5 B2 E. Lin

29 Effect of Factor A (Teaching method)
Main Effect Effect of Factor A (Teaching method) A1 A2 5 5 S1 S2 S3 S4 S5 S11 S12 S13 S14 S15 B1 5 5 S6 S7 S8 S9 S10 S16 S17 S18 S19 S20 B2 E. Lin

30 Effect of Factor B (Gender)
Main Effect Effect of Factor B (Gender) A1 A2 5 5 S1 S2 S3 S4 S5 S11 S12 S13 S14 S15 B1 5 5 S6 S7 S8 S9 S10 S16 S17 S18 S19 S20 B2 E. Lin

31 Effect of Interaction between Factors A and B
Interaction Effect Effect of Interaction between Factors A and B A1 A2 5 5 S1 S2 S3 S4 S5 S11 S12 S13 S14 S15 B1 5 5 S6 S7 S8 S9 S10 S16 S17 S18 S19 S20 B2 E. Lin

32 No interaction Interaction
E. Lin

33 Single-Subject Design
Synonym: Single-case design Single-system design Time series experimentation Definition: an experimental design involving the systematic collection of repeated measurements of a behavioural response over time, usually at frequent and regular intervals E. Lin

34 Single-Subject Design: Length of Phases
Two choices: Equal phase lengths: preset a short period time to minimize maturation, motivational changes over prolonged periods Unequal phase lengths: extend baseline or intervention phases until stability is achieved E. Lin

35 Single-Subject Design: Structure
Repeated measurement Two design phases: Baseline phase: period prior to treatment Intervention phase: Period during treatment. (Portney & Watkins, 2000) E. Lin

36 Single-Subject Design: Assumption
Baseline data reflect the ongoing effects of background variables, such as daily activities, other treatments, and personal characteristics, on the target behaviours. Therefore, when treatment is initiated, changes from baseline to the intervention phase should be attributable to intervention. (Portney & Watkins, 2000) E. Lin

37 Single-Subject Design: Length of Phases
Why not 1 or 2 sessions only? Stability: a minimum of 3 to 4 data points in each phase (the greater the number of data points, the more obvious trends will become) Why not 100 sessions? Efficiency: also to avoid maturation, history, and other confounding factors. (Portney & Watkins, 2000) E. Lin

38 Single-Subject Design: Measuring the Target Behaviour
Frequency: the number of occurrence of a certain behaviour within a fixed time interval or a fixed number of trials Duration: how long the target behaviour lasts Magnitude: some form of instrumentation that provides a quantitative score E. Lin

39 Single-Subject Design
Why use it? Practical: fewer subjects are required. Emphasis on individual performance: allowing for differentiation between subjects who respond favourably to treatment from those who are not affected by treatment. E. Lin

40 Types of Single-Subject Design
Withdrawal design A-B-A design A-B-A-B design Multiple treatment designs A-B-C-B design Interactive design: A-B-BC-B-BC Alternating treatment design Multiple baseline designs E. Lin

41 Withdrawal Design: A-B-A Design
(Portney & Watkins, 2000) E. Lin

42 Withdrawal Design: A-B-A-B Design
(Portney & Watkins, 2000) E. Lin

43 Multiple Treatment Design:
A-B-C-B Design (Portney & Watkins, 2000) E. Lin

44 Interactive Design: A-B-BC-B-BC Design (Portney & Watkins, 2000)
E. Lin

45 Alternating Treatment Design
(Portney & Watkins, 2000) E. Lin

46 Multiple Baseline Designs
Multiple baseline design across subjects Multiple baseline design across conditions Multiple baseline design across behaviours (Portney & Watkins, 2000) E. Lin

47 Split-Half Method (Portney & Watkins, 2000) E. Lin

48 Split-Half Method (continued)
(Portney & Watkins, 2000) E. Lin

49 Two Standard Deviation Method
(Portney & Watkins, 2000) E. Lin

50 References Hegde, M. N. (2003). Clinical Research in Communicative Disorders: Principles and Strategies (3rd Edition). Austin, TX: Pro-ed. Jadad, A. (1998). Randomised Controlled Trials. London: BMJ Books. Portney, L. G. & Watkins, M. P. (2000). Foundations of Clinical Research: Application to Practice (2nd Edition). Upper Saddle River, NJ: Prentice Hall Health. Schiavetti N., & Metz, D. (2002). Evaluating Research in Communicative Disorders (4th Edition). Sydney: Allyn & Bacon. E. Lin

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