Bài giảng Chapter 3 Introduction to Quantitative Research

Chapter 3 Introduction to Quantitative ResearchQuantity is the unit of analysisAmountsFrequenciesDegreesValuesIntensityUses statistics for greater precision and objectivityBased on the deductive model1Model for Conceptualizing Quantitative ResearchOverall purpose or objectiveResearch literatureResearch questions and hypothesesSelecting appropriate methodsValidity and reliability of the data2Creating the Foundation for Quantitative ResearchConcept Abstract thinking to distinguish it from other elementsConstruct Theoretical definition of a concept; must be observable or measurable; linked to other conceptsVariable Presented in research questions and hypothesesOperationalization Specifically how the variable is observed or measured3Research Hypotheses for Quantitative Research Educated guess or presumption based on literatureStates the nature of the relationship between two or more variablesPredicts the research outcomeResearch study designed to test the relationship described in the hypothesis4Quantitative Research HypothesesDirectional hypothesisPrecise statement indicating the nature and direction of the relationship/difference between variablesNondirectional hypothesisStates only that relationship/difference will occur5Assessing HypothesesSimply stated?Single sentence?At least two variables?Variables clearly stated?Is the relationship/difference precisely stated?Testable?6Null HypothesesImplicit complementary statement to the research hypothesisStates no relationship/difference exists between variablesStatistical test performed on the nullAssumed to be true until support for the research hypothesis is demonstrated7Research Traditions in the Use of HypothesesHypotheses are always tentativeResearch hypothesis, not the null hypothesis, is the focus of the research and presented in the research report8Research Questions in Quantitative ResearchPreferred when little is known about a communication phenomenonUsed when previous studies report conflicting resultsUsed to describe communication phenomena9Types of VariablesVariable Element that is identified in the hypothesis or research questionProperty or characteristic of people or things that varies in quality or magnitude Must have two or more levelsMust be identified as independent or dependent10Independent VariablesManipulation or variation of this variable is the cause of change in other variablesTechnically, independent variable is the term reserved for experimental studiesAlso called antecedent variable, experimental variable, treatment variable, causal variable, predictor variable11Dependent VariablesThe variable of primary interestResearch question/hypothesis describes, explains, or predicts changes in itThe variable that is influenced or changed by the independent variableIn non-experimental research, also called criterion variable, outcome variable12Relationship Between Independent and Dependent VariablesCannot specify independent variables without specifying dependent variablesNumber of independent and dependent variables depends on the nature and complexity of the studyThe number and type of variables dictates which statistical test will be used13Intervening and Confounding VariablesIntervening variableExplains or provides a link between IV and DVRelationship between the IV and DV can only be explained when the intervening variable is presentConfounding variableConfuses or obscures the effect of independent on dependentMakes it difficult to isolate the effects of the independent variable 14Operationalizing VariablesAll variables need an operationalizationMultiple operationalizations exist for most variablesSpecifies the way in which variable is observed or measuredPractical and useful?Justified argument?Coincides with the conceptual definition?15Making the Case for Quantitative Research AdvantagesTradition and history implies rigorNumbers and statistics allows precise and exact comparisonsGeneralization of findingsLimitationsCannot capture complexity of communication over timeDifficult to apply outside of controlled environments16Issues of Reliability and ValidityReliability = consistency in procedures and in reactions of participantsValidity = truth - Does it measure what it intended to measure?When reliability and validity are achieved, data are free from systematic errors17Threats to Reliability and ValidityIf measuring device cannot make fine distinctionsIf measuring device cannot capture people/things that differWhen attempting to measure something irrelevant or unknown to respondentCan measuring device really capture the phenomenon?18Other Sources of VariationVariation must represent true differencesOther sources of variationFactors not measuredPersonal factorsDifferences in situational factorsDifferences in research administrationNumber of items measuredUnclear measuring deviceMechanical or procedural issuesStatistical processing of data19