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 Outline • Basic Concepts • Experimental Research Group Design Single-Subject Design E. Lin 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 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 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 Types of Research Descriptive (Describe population) e.g., • Case study • Developmental research • Normative research • Qualitative research • Correlational research (Portney & Watkins, 2000) Exploratory (Find relationships) Experimental (Cause and effect) e.g., • Experimental (randomized controlled) • Quasiexperimental • Sequential clinical trial • Single-subject designs E. Lin What is an experiment (實驗)? 1. 2. 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: 1. Obtain a representative sample 2. Establish equivalency between comparison groups E. Lin 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 Sampling Strategy Probability 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 Compensations for Lack of Random Sampling Homogeneous groups Matching Control Build in extraneous factors Blocking E. Lin Validity (效度) • Internal validity: 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 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 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 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 Level of Measurement Ratio With absolute zero Interval Equal intervals Ordinal Nominal Ranking Category E. Lin 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. a. b. Concurrent validity Predictive validity 4. Construct validity: the degree the test measures an abstract construct E. Lin 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 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 Three Basic Measurements In Descriptive Statistics 1. Central tendency: the average (“center”) score of a distribution; mean, median, mode 2. Variability: the dispersion of scores; range, standard deviation (SD) 3. Relative position: a score’s position within a distribution; percentile, z-score E. Lin 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 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 Hypothesis Testing: Type I and II errors (Real Situation ) Reject H0 H0 is true Type I error (a) H0 is false Correct decision (1-b) (Power of test) Accept H0 Correct decision (1-a) Type II error (b) (Decision) 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 Hypothesis Testing (Portney & Watkins, 2000) E. Lin 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 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 Group Design: One-Way Design for Independent Groups A1 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 A2 10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 A3 10 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 10 5 4 Score 3 2 1 A1 A2 A3 Teaching method Analysis Method: One-way ANOVA E. Lin Group Design: One-way Within-Subjects Design A1 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 A2 10 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 A3 10 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 10 5 4 Score 3 2 1 A1 A2 A3 Teaching method Analysis Method: One-way Repeated Measures ANOVA E. Lin 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 B1 B2 A2 S1 S2 S3 S4 S5 5 S11 S12 S13 S14 S15 5 S6 S7 S8 S9 S10 5 S16 S17 S18 S19 S20 5 E. Lin Main Effect Effect of Factor A (Teaching method) A1 B1 S1 S2 S3 S4 S5 A2 5 S11 S12 S13 S14 S15 5 5 B2 S6 S7 S8 S9 S10 5 S16 S17 S18 S19 S20 5 4 Score 3 2 1 A1 A2 Teaching method E. Lin Main Effect Effect of Factor B (Gender) A1 B1 S1 S2 S3 S4 S5 B2 S6 S7 S8 S9 S10 A2 5 5 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 5 5 6 5 Score 4 3 2 1 B1 B2 Gender E. Lin Interaction Effect Effect of Interaction between Factors A and B A1 B1 B2 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 A2 5 5 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 5 B1 (Female) B2 (Male) 5 7 6 5 Score 4 3 2 1 0 A1 (Oral) A2 (Visual) Teaching method E. Lin B1 (Female) B2 (Male) 7 6 5 Score 4 3 2 1 0 A1 (Oral) A2 (Visual) Teaching method No interaction Interaction E. Lin 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 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 Single-Subject Design: Structure Repeated measurement Two design phases: 1. Baseline phase: period prior to treatment 2. Intervention phase: Period during treatment. (Portney & Watkins, 2000) E. Lin 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 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 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 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 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 Withdrawal Design: A-B-A Design (Portney & Watkins, 2000) E. Lin Withdrawal Design: A-B-A-B Design (Portney & Watkins, 2000) E. Lin Multiple Treatment Design: A-B-C-B Design (Portney & Watkins, 2000) E. Lin Interactive Design: A-B-BC-B-BC Design (Portney & Watkins, 2000) E. Lin Alternating Treatment Design (Portney & Watkins, 2000) E. Lin Multiple Baseline Designs 1. Multiple baseline design across subjects 2. Multiple baseline design across conditions 3. Multiple baseline design across behaviours (Portney & Watkins, 2000) E. Lin Split-Half Method (Portney & Watkins, 2000) E. Lin Split-Half Method (continued) (Portney & Watkins, 2000) E. Lin Two Standard Deviation Method (Portney & Watkins, 2000) E. Lin 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