Non-Experimental Quantitative Research DesignsDiscussion TopicsNon-Experimental DesignsSlide 4Descriptive DesignsSlide 6Relationship DesignsComparative DesignsSlide 9Correlational DesignsSlide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Slide 22Slide 23Causal Comparative DesignsSlide 25Slide 26Slide 27Using SurveysSlide 29Slide 30Slide 31Slide 32Slide 33Slide 34Slide 35Slide 36Slide 37Copyright © Allyn & Bacon 2008This multimedia product and its contents are protected under copyright law. The following are prohibited by law:any public performance or display, including transmission of any image over a network;preparation of any derivative work, including the extraction, in whole or part, of any images;any rental, lease, or lending of the program.Non-Experimental Quantitative Research DesignsChapter 8Copyright © Allyn & Bacon 2008Discussion TopicsNon-experimental research designs–Descriptive designs–Relationship designs–Causal-comparative and Ex Post Facto designsUsing surveys in quantitative researchCopyright © Allyn & Bacon 2008Non-Experimental DesignsResearch design - the plan and structure of research to provide a credible answer to a research questionPurpose of non-experimental designs–Describe current existing characteristics such as achievement, attitudes, relationships, etc.Copyright © Allyn & Bacon 2008Non-Experimental DesignsFour types of designs–Descriptive–RelationshipsComparativeCorrelational–Causal-comparative–Ex Post Facto–SurveyCopyright © Allyn & Bacon 2008Descriptive DesignsStudies that describe a phenomena–Statistical nature of the descriptionFrequencyPercentagesAveragesGraphsImportance of these designs in the early stages of the investigation of an areaCopyright © Allyn & Bacon 2008Descriptive DesignsCriteria for evaluating descriptive studies–Conclusions about relationships should not be drawn–Participants and instruments should be described completelyCopyright © Allyn & Bacon 2008Relationship DesignsTwo types of designs–Comparative–CorrelationalCopyright © Allyn & Bacon 2008Comparative DesignsThese designs investigate the relationship of one variable to another by examining differences on the dependent variable between two groups of participants–If math scores for males are significantly higher than those for females, a relationship exists between gender and math achievement–If the academic self-concept scores for ninth graders are significantly different than those for twelfth graders, a relationship exists between grade level and academic self-conceptCopyright © Allyn & Bacon 2008Comparative DesignsCriteria for evaluating these designs–Participants and instruments are described completely–Criteria for identifying the different groups is clearly stated–No inferences are made about causation–Graphs and images depict the results accuratelyCopyright © Allyn & Bacon 2008Correlational DesignsSimple correlation designs–Designs that examine the relationship between two variables –Two variables Predictor and criterionUse caution describing the variables as independent and dependent–ExamplesMath achievement and math attitudesTeacher effectiveness and teacher efficacyCopyright © Allyn & Bacon 2008Correlational DesignsSimple correlation designs–Cautions in interpreting correlationsA relationship between two variables (e.g., achievement and attitude) does not mean one causes the other (i.e., positive attitudes do not cause high levels of achievement)Possibility of low reliability of the instruments makes it difficult to identify relationshipsCopyright © Allyn & Bacon 2008Correlational DesignsSimple correlation designs–Cautions in interpreting correlationsLack of variability in scores (e.g., everyone scoring very, very low; everyone scoring very, very high; etc.) makes it difficult to identify relationshipsLarge sample sizes and/or using many variables can identify significant relationships for statistical reasons and not because the relationships really existCopyright © Allyn & Bacon 2008Correlational DesignsPrediction designs–Designs that examine the predictive nature of the relationships between variables–Two types of designsSimple predictionMultiple regressionCopyright © Allyn & Bacon 2008Correlational DesignsPrediction designs–Simple predictive studiesPerformance on one variable (i.e., the predictor) is used to predict performance on a second variable (i.e., the outcome or criterion)Examples–Scholastic Aptitude Test (SAT) scores are used to predict college freshmen grade point averages–Scores from a mathematical attitude scale are used to predict math achievement scoresImportance of the time interval between collecting the predictor and criterion variable dataCopyright © Allyn & Bacon 2008Correlational DesignsPrediction designs–Simple predictive studiesFactors influencing correlations–Possibility of low reliability of the instruments measuring the predictor and criterion variables makes it difficult to identify relationships–Length of time between the predictor and criterion variable data collection–Existence of many factors, not only the one being examined, that influence the criterion variableCopyright © Allyn & Bacon 2008Correlational DesignsMultiple regression–Studies that examine performance on several variables (i.e., predictor variables) to predict performance on a single outcome variable (i.e., criterion)–ExamplesScholastic Aptitude Test (SAT) scores, high school grade point average, and high school rank in class are used to predict college freshmen grade point averageMath attitude scale scores, academic self-esteem scale scores, and prior math grades are used to predict math achievement scoresCopyright © Allyn & Bacon 2008Correlational DesignsLogistic Regression–Another type of multiple regression analysis used to examine the relationship between the predictor variables and dependent variable.–The result is a prediction of whether the participant is a “case” or “non-case”Example: Overall GPA, gender, and special education classification used to determine if a student will pass or fail a standardized test.Copyright © Allyn & Bacon 2008Correlational DesignsMultiple regression–Issues of concernSample size of at least 10 subjects for each predictor variableRelationships among the