Jan 17 Research Methods in Psychology Anxiety → Physiological measure → behavioural measure → Self-reported measure - Research method is important to know if you want to conduct reliable studies - Study says your anxiety can make you better at your job How did they measure anxiety? - Self-report? Valid, reliable scale? Clinical levels of anxiety? Three big categories of Research Methods 1. Descriptive methods - Often concerned with a single variable of interest 2. Correlational methods - Examine associations between two or more variables 3. Experimental methods - Examine cause-and-effect relationships between two or more variables Descriptive methods - Involves systematic observation and classification behaviour Includes: - Surveys - Focus groups - Case studies - Observational research E.g., survey of study strategies - Sample a group of students - Does not give us detailed description Case studies - What can we gain from this - We don’t know if what works for one person will work for others - Provide insights into situations that we can’t get otherwise Types of Observation 1. Naturalistic Observation - Passive observation; when a researcher is out there making observations in real-world scenarios without altering with people we are observing 2. Participant observation - Active observation; the researcher is actively involved in the situation. Immersed themselves 3. Laboratory Observation - Systematic observations are made within a laboratory setting (rather than the real world) Strengths of Descriptive Approaches - Case studies and observational research can provide important insights and stimulate further research to test specific hypotheses Surveys allow us to gather large amounts of information quickly and easily Focus groups and interviews can provide rich, detailed information that may be lacking from a survey Potential Problems with descriptive methods - Reactivity (e.g., the Hawthorne effect) - If individuals know they are being watched, it might change their behaviour - Demand characteristics - Observer/experimenter bias - Can affect observation making, must have a strict coding scheme for your study - Self-report bias - Social desirability bias - The “better than average” effect Summary - In psychology, methods are often used in combination with other methodological approaches - May lead to claims about the frequency of prevalence of a behaviour - May add rich, qualitative information to a research program that would otherwise be missing this type of detail Correlational Methods - Relationship between two variables Single group of participants, at least two measures We are NOT manipulating any of the variables Example: Laptop multitasking - School-unrelated laptop use during class time has been associated with lower academic satisfaction, lower end-of-semester GPAs, and lower course performance relative to classmates Correlational studies tell us about relationships between variables - No Relationship (0) - Positive relationship (+) → variables move in the same direction - One variable increases the other increases - Negative relationship (-) → variables move in the inverse direction - One variable increases the other decreases - How strong is the relationship? Correlational studies do NOT tell us whether one variable causes changes in another variable - Why not? - Directionality problem - Which variable is causing the changes - Third-variable problem - A third variable we have not measured that is causing these changes - Correlation does not equal causation Why bother with correlational methods? - We can’t always manipulate a variable we are interested in (due to feasibility or ethical concerns) Summary - Correlational studies are an important component of psychological research as they allow us to examine hypotheses about the relationships between variables - However, they do not allow us to make cause-and-effect claims, as tempting as they may be Experimental Methods - Independent variable: A variable that is manipulated in order to see its impact on the dependent variable Dependent Variable: Measured Experiments - Involve manipulating an independent variable in order to determine its impact on a dependent variable (which we measure) - Are tightly controlled (typically take place in the laboratory) - Participants are randomly assigned to study conditions Example Karpicke & Bauernschmidt (2011) Independent variable: the type of studying the participants engaged in 1. Study once 2. Recall once 3. Repeated massed 4. Repeated spaced Dependent variable: tested one week later The importance of Control Confound: Anything that may unintentionally systematically vary along with the independent variable - Is there anything else that might be different between experimental conditions? - Confounds limit our ability to make causal claims Random sample: each member of the population you are interested in has an equal chance of being chosen to participate Why are “Double-Blind” experiments ideal - Recall: Observer/experimenter bias & Demand characteristics - In a double-blind experiment, both the participants and the experimenters who interact with them are unaware of which condition the participant is in Participants: Samples & Populations - Population: the group that you want to be able to generalize your findings to - Sample: the group of individuals from this population who are part of your study - Random samples vs. convenience samples - Random from a population where everyone has an equal chance to be a participant Convenience from a population you already know
