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PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Page 1 of 15 Bordens, K. S., & Abott, B. B. (1996). Research Design and Methods: A Process Approach (4-th Edition). CA: Mayfield Publishing Co. Color keyed to likelihood that material will be on the exams- (Highly, Possibly, Unlikely) 1. Explaining Behavior Exploring the Causes of Behavior Explaining Behavior Scientific Explanations Empirical Rational Testable Parsimonious General Tentative Rigorously Evaluated Commonsense vs. Scientific Explanations Belief-based vs. Scientific Explanations When Scientific Explanations Fail Failures Due to Faulty Inference Pseudoexplanations Methods of Inquiry Method of Authority Rational Method Scientific Method Observing a Phenomenon Formulating Tentative Explanations Further Observing & Experimenting Refining & Retesting Explanations Scientific Method at Work: Impact of the # of Bystanders on Helping Scientific Method as an Attitude Translating the Scientific Method into Practice: The Research Process Method vs. Technique Basic & Applied Research Basic Research Applied Research Overlap Between Basic & Applied Research The Steps of the Research Process Developing a Research Idea & Hypothesis Choosing a Research Design Choosing Subjects Deciding on What to Observe & Appropriate Measures Conducting Your Study Analyzing Your Results Reporting Your Results 1 3 4 4 5 5 5 5 5 5 5 5 8 9 9 11 13 13 14 15 15 16 16 17 17 19 19 19 19 20 20 20 21 21 21 23 23 23 23 23 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Page 2 of 15 Starting the Whole Process Over Again 24 2. Developing Ideas for Research 26 Sources of Research Ideas Unsystematic observation Systematic observation Theory The Need to Solve Practical Problems Developing Good Research Questions Asking Answerable Questions Asking the Right Questions Asking Important Questions Reviewing the Literature Sources of Research Information Primary vs. Secondary Sources Where to Find Research Books Scientific Journals Conventions & Professional Meetings Other Sources of Research Info Performing Library Research Basic Strategy Research Tools Using PsycLit Conducting a PsycLit Search Narrowing Your Search Note of Caution about Using PsycLit Other Computerized Databases Computer Searching the Card Catalog Computers & Literature Reviews: A Closing Note Using the Psychological Abstracts Citations Index Reading Research Reports Obtaining a Copy Reading the Research Report Reading the Literature Critically Evaluating the Introduction Evaluating the Method Section Evaluating the Results Section Evaluating the Discussion Section References Developing Hypotheses 3. Choosing a Research Design Functions of a Research Design Causal vs. Correlational Relationships Correlational Research 26 27 27 28 28 29 29 29 30 31 32 32 34 34 35 36 37 39 39 39 40 40 40 42 42 42 43 43 43 44 44 44 45 47 47 47 48 48 49 52 52 53 54 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Characteristics of Correlational Research Example of Correlational Research Assessing the Belsky & Rovine Study Causation & the Correlational Approach Third-Variable Problem Directionality Problem Why Use Correlational Research? Gathering Data in the Early Stages of Research Inability to Manipulate Variables Relating Naturally Occurring Variables Experimental Research Characteristics of Experimental Research Manipulation of IVs Control Over EVs Example of Experimental Research Assessing the Rauh et al. Experiment Strengths & Limitations of the Experimental Approach Experiments vs. Demonstrations Internal & External Validity Internal Validity Threats to Internal Validity Enhancing Internal Validity External Validity Threats to External Validity Internal vs. External Validity Research Settings Laboratory Setting Simulation: Re-creating the World in the Laboratory Why Simulate? Designing a Simulation Realism Field Setting Field Experiment Advantages & Disadvantages of the Field Experiment A Look Ahead 4. Making Systematic Observations Deciding What to Observe Choosing Specific Variables for Your Study Research Tradition Theory Availability of New Techniques Availability of Equipment Choosing Your Measures Reliability of a Measure Reliability of a Physical Measure Reliability of Population Estimates Page 3 of 15 54 55 55 55 56 57 57 57 58 59 59 59 60 61 62 62 63 63 65 65 66 67 67 68 68 69 70 70 70 70 71 72 72 73 73 76 76 77 77 77 77 78 78 79 79 79 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Reliability of Judgments or Ratings by Multiple Observers Reliability of Psychological Tests or Measures Accuracy of a Measure Validity of a Measure Acceptance as an Established Measure Scale of Measurement of a Measure Nominal Scales Ordinal Scales Interval & Ratio Scales Variables & Scales of Measurement Choosing a Scale of Measurement Information Yielded Statistical Tests Ecological Validity Adequacy of a DV Sensitivity of the DV Range Effects Tailoring Your Measures to Your Research Participants Types of DVs & How to Use Them Behavioral Measures Physiological Measures Self-Report Measures Choosing When to Observe Reactive Nature of Psychological Measurement Reactivity in Research with Human Participants Demand Characteristics Other Influences Role of the Experimenter Reactivity in Research with Animal Subjects Automating Your Experiments Detecting & Correcting Problems Conducting a Pilot Study Adding Manipulation Checks 5. Choosing & Using Subjects Using Subjects: General Considerations Populations & Samples Sampling & Generalization Is Random Sampling Always Necessary? Considerations When Using Human Participants Ethical Research Practice Nazi War Crimes & the Nuremberg Code APA Ethical Guidelines Government Regulations Ethical Guidelines, Your Research, & the IRB Acquiring Human Participants for Research Research Setting Page 4 of 15 79 79 81 82 83 84 84 84 84 85 86 86 86 87 88 88 89 90 91 91 92 93 94 95 95 96 97 98 100 101 102 103 103 106 107 107 109 111 111 111 112 113 116 116 119 119 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Laboratory Research Field Research The Needs of Your Research Institutional Policies & Ethical Guidelines Voluntary Participation & Validity Factors That Affect the Decision to Volunteer Participant-Related Characteristics Situational Factors Volunteerism & Internal Validity Volunteerism & External Validity Remedies for Volunteerism Research Using Deception Solutions to the Problem of Deception Role Playing Obtaining Prior Consent to be Deceived Debriefing Considerations When Using Animals as Subjects in Research Contributions of Research Using Animal Subjects Choosing Which Animal to Use Why Use Animals? How to Acquire Animals for Research Ethical Considerations Should the Research be Done? Generality of Animal Research Data The Animal Rights Movement Alternatives to Animals in Research 6. Using Nonexperimental Designs Conducting Observational Research Developing Behavioral Categories Quantifying Behavior in an Observational Study Frequency Method Duration Method Intervals Method Recording Single Events or Behavior Sequences Coping with Complexity Time Sampling Individual Sampling Event Sampling Recording Establishing the Reliability of Your Observations Percent Agreement Cohen's Kappa Pearson's Product-Moment Correlation Dealing with Data from Multiple Observers Sources of Bias in Observational Research Qualitative Approaches Page 5 of 15 119 120 120 121 121 121 121 122 123 124 125 126 128 128 129 130 132 132 132 133 133 134 137 138 138 142 145 145 145 146 146 146 147 147 147 148 148 148 148 150 151 151 153 154 155 155 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Naturalistic Observation Making Unobtrusive Observations Naturalistic Observation: Example Advantages & Disadvantages of Naturalistic Observation Ethnography Observing as a Participant or Nonparticipant Gaining Access to a Field Setting Gaining Entry into the Group Becoming Invisible Making Observations & Recording Data Analyzing Ethnographic Data Born to Be Wild: Example of Ethnography Evaluation of the Ethnography of the HDSC Sociometry Example of Sociometry The Case History Archival Research Content Analysis Defining Characteristics of Content Analysis Performing Content Analysis Limitations of Content Analysis Content Analysis: An Example 7. Using Survey Research Survey Research Designing Your Questionnaire Selecting the Questionnaire Format Types of Questionnaire Items Rating Scales Writing Questionnaire Items Assembling the Questionnaire Administering Your Questionnaire Mail Surveys Combating Nonresponse Bias Telephone Surveys Group Administration The Interview The Internet Assessing the Reliability of a Questionnaire Assessing Reliability by Repeated Testing Assessing Reliability with a Single Test Increasing Reliability Assessing the Validity of a Questionnaire Acquiring a Sample for Your Survey Representativeness Sampling Techniques Simple Random Sampling Page 6 of 15 156 156 157 157 159 159 160 161 161 161 162 162 164 164 165 166 166 167 167 168 169 169 173 174 175 176 176 178 180 183 184 185 185 187 189 189 191 191 192 193 194 194 195 195 196 197 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Page 7 of 15 Stratified Sampling Proportionate Sampling Systematic Sampling Cluster Sampling Random & nonrandom sampling revisited Sample Size 8. Using Between & Within Subjects Designs Types of Experimental Design Problem of Error Variance in Between-Subjects & Within-Subjects Designs Sources of Error Variance Handling Error Variance Reducing Error Variance Increasing the Effectiveness of Your IV Randomizing Error Variance across Groups Statistical Analysis Between-Subjects Designs Single-Factor Randomized Groups Designs Randomized Two-Group Design Randomized Multigroup Design Matched Groups Designs Logic of the Matched Groups Design Advantages & Disadvantages of the Matched Groups Design Matched Pairs Design Matched Multigroup Designs Within-Subjects Designs Advantages of the Within-Subjects Design Disadvantages of the Within-Subjects Design Sources of Carryover Dealing with Carryover Effects Counterbalancing Taking Steps to Minimize Carryover Making Treatment Order an IV When to Use a Within-Subjects Design Subject Variables Correlated with the DV Economizing on Subjects Assessing the Effects of Increasing Exposure on Behavior Within-subjects vs. Matched Groups Designs Types of Within-Subjects Designs Single-factor, 2-level Design Single-factor, Multilevel Designs Designs with 2 or more IVs Factorial Designs Main Effects Interactions Factorial Within-Subjects Designs Higher-order Factorial Designs 199 199 201 202 202 204 208 208 209 209 210 211 211 211 211 212 212 212 215 216 218 218 219 219 220 220 222 223 224 224 227 228 231 231 232 232 232 233 233 233 235 235 237 237 239 241 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Other Group-based Designs Designs with Two or More DVs Confounding & Experimental Design 9. Using Specialized Research Designs Combining Between-Subjects & Within-Subjects Designs Mixed Design Nested Design Nesting Tasks Nesting Groups of Subjects Combining Experimental & Correlational Designs Including a Covariate in Your Experimental Design Including a Quasi-IV in an Experiment Advantages of Including a Quasi-IV Disadvantages of Including a Quasi-IV Quasi-Experimental Designs Time-Series Designs Interrupted Time-Series Design Basic Data for Time-Series Studies Equivalent Time-Samples Design Advantages & Disadvantages of Quasi-Experiments Nonequivalent Control Group Design Pretest-Posttest Designs Developmental Designs Cross-Sectional Design Longitudinal Design Generation Effects in Longitudinal Designs Subject Mortality Multiple Observation Effects Advantages of the Longitudinal Design Cohort-Sequential Design 10.Using Single Subject Designs A Little History Baseline vs. Discrete Trials Designs Baseline Designs Features of the Baseline Design The Behavioral Baseline Stability Criterion Intrasubject Replication Intersubject Replication Rationale of the Baseline Design Dealing with Random Variability Handling Error Variance Assessing the Reliability of Findings Determining the Generality of Findings Implementing a Single-Subject Baseline Design Page 8 of 15 242 243 243 248 248 248 250 251 251 252 252 253 254 255 255 255 256 257 257 257 259 260 262 262 265 265 265 267 267 268 272 272 274 275 277 277 277 278 278 278 278 280 281 282 285 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Dealing with Problem Baselines Unsystematic Baseline Variability Drifting Baselines Unrecoverable Baselines Unequal Baselines Between Subjects Inappropriate Baseline Levels Types of Single-Subject Baseline Design Single-Factor Designs Multifactor Designs Multiple-Baseline Designs Observing Behavioral Dynamics Discrete Trials Designs Characteristics of Discrete Trials Designs Analysis of Data from Discrete Trials Designs Inferential Statistics & Single-Subject Designs Advantages & Disadvantages of the Single-Subject Approach 11.Describing Data Descriptive Statistics & Exploratory Data Analysis Organizing Your Data Organizing Your Data for Computer Entry Entering Your Data Grouped vs. Individual Data Grouped Data Individual Data Using Grouped & Individual Data Graphing Your Data Elements of a Graph Bar Graphs Line Graphs Shapes of Line Graphs Scatterplots Pie Charts The Importance of Graphing Data Showing Relationships Clearly Choosing Appropriate Statistics Frequency Distribution Displaying Distributions Histogram Stemplot Examining Your Distribution Descriptive Statistics: Measures of Center & Spread Measures of Center Mode Median Mean Choosing a Measure of Center Page 9 of 15 288 289 289 289 290 291 291 291 294 294 297 300 300 302 302 303 308 308 309 314 315 317 317 317 317 319 319 319 321 321 323 324 324 325 325 325 326 326 326 327 329 329 329 330 331 331 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Measures of Spread Range Interquartile Range Variance Standard Deviation Choosing a Measure of Spread Boxplots & the Five-Number Summary Measures of Association, Regression, & Related Topics Pearson Product-Moment Correlation Coefficient Factors That Affect the Pearson Correlation Coefficient Point-Biserial Correlation Factors That Affect the Point-Biserial Correlation Spearman Rank-Order Correlation phi Coefficient Linear Regression & Prediction Bivariate Regression Residuals & Errors in Prediction Coefficient of Determination Correlation Matrix Multivariate Correlational Techniques 12.Using Inferential Statistics Inferential Statistics: Basic Concepts Sampling Distribution Sampling Error Degrees of Freedom Parametric vs. Nonparametric Statistics The Logic Behind Inferential Statistics Statistical Errors Statistical Significance 1-Tailed vs. 2-Tailed Tests Parametric Statistics Assumptions Underlying a Parametric Statistic Inferential Statistics with 2 Samples t test t test for Independent Samples t test for Correlated Samples Contrasting 2 Groups: Example from the Literature z-test for the Difference Between 2 Proportions Beyond 2 Groups: Analysis of Variance (ANOVA) Partitioning Variation F ratio 1-Factor Between-Subjects ANOVA Interpreting Your F ratio Planned Comparisons Unplanned Comparisons Sample Size Page 10 of 15 333 334 334 334 334 335 335 337 337 338 340 340 341 341 341 341 343 344 344 345 349 349 349 350 350 351 351 354 355 356 357 358 358 358 358 359 359 361 361 361 362 362 363 364 364 365 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Unweighted-Means Analysis Weighted-Means Analysis 1-Factor Within-Subjects ANOVA Latin Square ANOVA Interpreting Your F ratio 2-Factor Between-Subjects ANOVA Main Effects & Interactions Sample Size ANOVA for a 2-Factor Between-Subjects Design: Example Interpreting the Results 2-Factor Within-Subjects ANOVA Mixed Designs Higher Order & Special Case ANOVAs Nonparametric Statistics Chi-Square Chi-Square for Contingency Tables Limitations of Chi-Square Mann-Whitney U Test Parametric Vs. Nonparametric Statistics Special Topics in Inferential Statistics Power of a Statistical Test Alpha Level Sample Size 1-Tailed vs. 2-Tailed Tests Effect Size Determining Power Statistical vs. Practical Significance The Meaning of the Level of Significance Data Transformations Alternatives to Inferential Statistics 13.Reporting Your Research Results APA Writing Style Writing an APA-Style Paper Getting Ready to Type Formatting a Page Heading Structure Title Page Title Author Name(s) & Affiliation(s) Running Head Abstract Formatting the Abstract Introduction Formatting the Introduction Method Section Subjects or Participants Page 11 of 15 365 365 365 367 367 367 367 369 369 369 370 371 371 372 372 372 372 374 375 375 375 375 376 376 376 376 377 378 378 380 384 384 385 385 387 387 387 388 388 389 389 390 390 392 392 393 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Apparatus or Materials Procedure Combining Sections Formatting the Method Section Results Section Formatting the Results Section Discussion Section Reference Section Other Optional Information Author Notes Footnotes Tables Figure Captions Figures Citing References in Your Report Using Numbers in the Text Avoiding Biased Language Expression, Organization, & Style Expressing your Ideas Clearly Grammatical Correctness Proper Word Choice Economy of Expression Organization Style Making It Work Avoiding Plagiarism & Lazy Writing Submitting a Paper for Publication Paper Presentations Oral Presentations Poster Sessions 14.Using Multivariate Design & Analysis Experimental & Correlational Multivariate Designs Correlational Multivariate Design Experimental Multivariate Design Multivariate Statistical Tests Advantages of the Experimental Multivariate Strategy Advantages of the Correlational Multivariate Strategy Causal Inference Assumptions & Requirements of Multivariate Statistics Linearity Outliers Identifying Outliers Dealing with Outliers Normality & Homoscedasticity Multicollinearity Error of Measurement Page 12 of 15 393 395 396 396 396 396 400 401 405 405 406 406 407 407 410 412 414 414 415 415 415 416 417 419 419 421 421 422 422 423 427 427 428 428 429 429 429 429 430 430 431 431 431 432 433 433 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Sample Size Multivariate Statistical Tests Factor Analysis Factor Loadings Rotation of Factors Principal-Components & Principal-Factors Analysis Example of Factor Analysis Partial & Part Correlations Partial Correlation Part Correlation Multiple Regression Multiple Regression Equation Types of Regression Analysis Example of Multiple Regression Multiple R & R-Square Regression Weights Interpretation of Regression Weights Discriminant Analysis Example of Discriminant Analysis Canonical Correlation Multivariate Analysis of Variance Example of MANOVA Using MANOVA for Within-Subjects Designs Loglinear Analysis Applications of Loglinear Analysis How Loglinear Analysis Works Path Analysis Causal Relationships Types of Variables & Causal Models Estimating the Degree of Causality Interpreting Path Analysis Multivariate Analysis: A Cautionary Note 15.Using Theory What is a Theory? Theory vs. Hypothesis Theory vs. Law Theory vs. Model Computer Modeling Mechanistic vs. Functional Explanations Types of Theory Quantitative vs. Qualitative Theory Quantitative Theory Qualitative Theory Level of Description Descriptive Theories Analogical Theories Page 13 of 15 434 435 435 435 436 437 438 439 439 441 441 441 441 442 443 443 444 445 447 448 448 449 451 452 453 453 454 455 457 457 457 459 463 463 465 465 465 466 468 469 469 469 470 470 470 471 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Page 14 of 15 Fundamental Theories Domain of a Theory Roles of Theory in Science Understanding Prediction Organizing & Interpreting Research Results Generating Research Characteristics of a Good Theory Ability to Account for Data Explanatory Relevance Testability Prediction of Novel Events Parsimony Developing Theories Step 1: Defining the Scope of Your Theory Step 2: Knowing the Literature Step 3: Formulating Your Theory Preparedness Using Analogy Using Introspection Step 4: Establishing Predictive Validity Step 5: Testing Your Theory Empirically Confirmation & Disconfirmation of Theories Confirmation of Theories Disconfirmation of Theories Strategies for Testing Theories Strong Inference Following a Confirmational Strategy Following a Disconfirmational Strategy Using Confirmational & Disconfirmational Strategies Together Theory-Driven vs. Data-Driven Research 16.Making Sense of Research Publication Practices Criteria for Acceptance of a Manuscript Statistical Significance Consistency with previous Knowledge Significance of the Contribution Editorial Policy Pernicious Problems of Peer Review Peer Review Problems with Peer Review Playing the Publication Game Fads in Research Fads vs. Trends in Research Why Fads Emerge & Die Reasons for Increased Popularity of a Research Area 472 474 475 475 475 476 476 478 478 478 478 479 479 480 480 480 481 481 482 482 484 485 485 485 486 487 488 488 489 490 491 497 497 498 498 499 501 502 503 503 503 505 507 507 507 508 PSY389 Dr. M. Plonsky – Bordens & Abbott Outline Page 15 of 15 Research Interests Fit the “Spirit of the Times” A Particular Theory Appears to Have Great Theoretical Power Appropriate Research Instruments & Methodology Prestigious, Widely Respected Researchers are Doing It There is Strong Financial Support An Area is a Unexplored Frontier Reasons for Decreased Popularity of a Research Area Feelings that the Important Aspects of the Problem are Solved Research Appears to Lead to an Empirical Dead End Research in the Area is shown to be Flawed Changes in Prevailing Ethical Standards Decreased Funding Dealing With Fads in Research Fraud & the Role of Values in the Research Process Fraud in Research What Constitutes Fraud in Research? Prevalence of Research Fraud Explanations for Research Fraud Dealing with Research Fraud Role of Values in Science How Values Influence What & How Scientists Study Interpreting Behavior Moving from what is to what ought to be Meta-analysis: A Tool for Comparing Results across Studies Step 1: Identifying Relevant Variables Step 2: Locating Relevant Research to Review Step 3: Doing the Meta-Analysis An Example of Meta-Analysis Drawbacks to Meta-Analysis Assessing the Quality of the Research Reviewed Combining & Comparing Studies Using Different Methods Practical Problems Do Results of Meta-Analysis Differ from Traditional Reviews? End of document 509 509 509 509 510 510 510 510 510 511 511 512 512 513 513 514 514 515 515 516 517 518 518 518 519 520 521 522 522 523 523 524 524
