Psychological Research Strategies Module 2

Why is Research Important? Gives us a reliable, systematic way to consider our questions Helps us to draw logical, supported conclusions * Not all will conduct research, but all have to evaluate its relevance

Research Methods Example research topic: Does listening to music through headphones affect studying? What does common sense say?

Observation Benefit: simplest scientific technique Problem: bias Two Types of bias: Researcher bias – observation may be influenced by what they want/expect to discover Participant bias – may respond a certain way because: know they’re being observed believe they know what the researcher wants

What do you see?

Ways to reduce bias: Researcher – finding ways to rely less on observers’ opinion Participant – using naturalistic observation – naturally occurring settings

Case Studies one person is studied in depth in the hope of revealing universal principles Problems: bias can’t always apply results of one study to other situations Sometimes necessary for ethical reasons

Correlational Study Reveals the extent to which two variables are related to each other Examples: Is there a relationship between… diet and health? training techniques and success in sports? studying and better grades?

Uses of Correlational Studies does not prove that a cause-and-effect relationship exists only tell you only that variables are related, not why they are related Useful for making predictions – the stronger the correlation, the more accurate your prediction will be

Correlation Positive Correlation – both variables increase or decrease together Examples: height & weight, studying effectiveness & frequency of wearing headphones Perfect Positive CorrelationModerate Positive Correlation

Correlation Negative Correlation – one variable increases while the other decreases Example: hours spent watching TV & GPA, studying effectiveness & frequency of wearing headphones Perfect Negative CorrelationModerate Negative Correlation

Surveys Goal: discover the self-reported attitudes or behaviors of a sample of people using questionnaires or interviews Benefits: efficient, can reach large crowds Problems: bias in wording, social desirability

Surveys For results to be relevant to the population: Random sample – every member of the population has an equal chance of being selected Adequate number of participants – determined by mathematical formulas; larger samples = more relevant results

Longitudinal and Cross-Sectional Studies both study how individuals change during life Longitudinal studies – follow the same group of individuals for many years Benefits: abundant data Problems: expensive, difficult to conduct = rare Cross-sectional studies – compare people of different ages at one time Benefits: more efficient Problems: changes in time can affect results

Experiments only cause-and-effect Purpose: only method that allows you to establish a cause-and-effect relationship

Hypotheses and Operational Definitions 1 st step: form a hypothesis – testable prediction of the experiment’s outcome operational definitions of the variables – putting them in a more specific form so they can be precisely measured

Operational Definitions Example Which of these example hypotheses has provided operational definitions? A) “Headphones influence concentration in class” B) “Students assigned to wear headphones in class will have higher average grades at the end of the quarter than students banned from wearing headphones.” * WS w/ partner

Independent and Dependent Variables Two variables of a hypothesis: 1. Independent variable (IV): the variable that should cause something to happen 2. Dependent variable (DV): the variable that should show the effect (or outcome) of the IV

IV & DV Example “Students assigned to wear headphones in class will have higher average grades at the end of the quarter than students banned from wearing headphones.” What are the independent and dependent variables? IV = presence or absence of headphones DV = participants' average end-of-quarter grades

Groups & Random Assignment Most experiments have two groups: Experimental group – exposed to the IV (headphones) Control group – not exposed to the IV (no headphones) Critical feature: people placed in groups by random assignment – assigning by chance

Confounding Variables Variables other than the IV that could produce a change in the DV Examples of individual differences for our example: Confounding variables can be controlled for by random assignment – can assume that factors will balance out evenly across both groups amount of sleep personal problems health quality of teachers

Control for Other Confounding Variables: Environmental Differences Other types of confounding variables: Environmental differences Expectation effects Control for environmental differences by making sure both environments are the same (temperature, lighting, noise conditions)

Control for Other Confounding Variables: Expectation Effects Control for expectation effects by: Blind procedure - participants don’t know hypothesis until after data is collected Double-blind procedure – the people collecting the data & participants don’t know the expected outcome Placebo – nonactive substance or condition that is given instead of a drug or active agent (all drug studies)

Data Analysis Run the experiment & collect the data, then analyze the numbers using statistics Statistically significant – the possibility that the difference between groups would occur by chance alone is not more than 5%

Replication Results must be replicable (can be reliably repeated) to prove a cause-and-effect relationship between the IV & DV

Ethics: Human Research 4 Ethical Principles Guiding Human Research: 1. Informed consent – about nature of research & risks 2. Right to be protected from harm and discomfort 3. Right to confidentiality – can’t release data about individual participants 4. Right to debriefing – full explanation of research when involvement is done

Ethics: Animal Research Must have clean housing, adequate ventilation, and appropriate food, be well-cared for