Questions that Social Psychologists wanted to explore: ► What impact does attractiveness have on perception? ► How do we explain peoples’ behavior? Famous Experiment Famous Experiment Famous Experiment ► What are the reasons for attitudes or prejudices? ► Why do we conform? ► Why are some people altruistic?
► Describe a phenomenon ► Make predictions about the phenomenon ► Control variables ► Explain phenomenon with some degree of confidence 4 Main Goals of Psychological Research:
In order to conduct good psychological research one must: Use the scientific method. ► Have a clear operational definition. Allows for replication. ► Have informed consent. ► Follow APA guidelines. APA guidelinesAPA guidelines
Various Types of Research Methods Video Video ► Descriptive Research: Scientists use naturalistic observations, case studies, and surveys t o describe and predict behavior and mental processes.
Various Types of Research Methods ► Correlational Research: predicts naturally occurring relationships.
Various Types of Research Methods ► Experimental Research: Scientists use experiments to control variables and to establish Cause and Effect Relationships (in which one variable can be shown to have caused a change in another.)
Descriptive Research ► Naturalistic Observation: Seeing subjects in natural environment Best way to gather descriptive data. Problem- when people know that they are being watched, they act differently. (Hawthorn effect) Test 1 Test 1 Test 1 Video 1 Video 1 Video 1 Video 2 Video 2 Video 2 ► Case Studies: Intense examination of a phenomenon. Useful when something is new and complex. Problem- limited to what researchers consider important.
Surveys ► Researchers use questionnaires and interviews to gather information about behavior, beliefs, and opinions. ► Gives a broad view of a large group. ► Validity depends on representativeness and how questions are worded. ► Problem- people are reluctant to say what they really believe, say what is desired. Descriptive Research
Correlation: Correlation: ► Used to say that two traits or behaviors accompany one another and the Correlation Coefficient measures the relationship between the two. ► Correlation does not mean Causation!!! ► Types of correlation: Positive- two variables same direction Negative- two variables opposite direction
Correlation Coefficient ► A number that measures the strength of a relationship. ► Range is from -1 to +1 ► The relationship gets weaker the closer you get to zero. Which is a stronger correlation? ► -.13 or +.38 ► -.72 or +.59 ► -.91 or +.04
Experiments: ► Establish a Cause and Effect relationship. ► Allow researchers to control variables. By manipulating variables, researchers view effects. Types of Variables: ► Independent- manipulated factor. ► Dependent- factor that is observed and measured. (depends on the application of the independent variable) ► Confounding a.k.a.Random Variables- uncontrolled factors in research design.
Experimental Method: ► 1. Observe some aspect of the universe. ► 2. Invent a tentative description, called a hypothesis, that is consistent with what you have observed. ► 3. Use the hypothesis to make predictions. ► 4. Test those predictions by experiments or further observations and modify the hypothesis in the light of your results. ► 5. Repeat steps 3 and 4 until there are no discrepancies between theory and experiment and/or observation.
How do I pick the people to study? ► Pick a sample of people from a population so that you can generalize your findings. Regardless of research design) Population Random Sample Random Assignment Control Group OR Experimental Group Larger samples yield more reliable results (External Validity)
So now, what do we do with all the data collected??????? ► We use STATISTICS! Yay…..
Important stuff to know about data… ► In order for data to be considered worthy or good, sound data it must be… Reliable- stable and consistent Valid- it is reporting what it set out to report
Statistical Analysis of Research Results: Descriptive or Inferential? ► Statistics are methods for demystifying and making meaning from data. ► Numbers have little meaning unless it is organized. An organized list allows us to see clusters or patterns in data. ► Graphing allows us to see some level of meaning from numbers. (Pie Charts, Line Graphs, Frequency Polygon
Descriptive Statistics: ► Used to describe and present data. ► They can tell us the difference between two or more groups. ► Basic categories of DS are 1. Central Tendency 2. Variability 3. Correlation
Central Tendencies Central Tendencies ► A way to describe the typical or average score distribution. ► Mean- average, can be skewed. ► Median- best indicator of central tendency, middle score. ► Mode- Most frequently occurring score.
Variability: ► Indicates how much spread there is in a distribution. ► Range- difference between lowest and highest score. ► Variance- how different scores are from each other. ► Standard Deviation- most common measure of variability. (how far each value is from the mean.)
Let’s practice with Central Tendencies ► What options does Mars Co have when filling bags of M&Ms in regard to color? ► FIXED…..RANDOM…..You decide!
Choose a sample from the population….. Propose a hypothesis by recording a % on worksheet. (f = frequency) Calculate %. Observe and record actual colors in your sample. (how closely do they match?) ► Will you alter your hypothesis? Get into groups of 5 people. Record data. ► Will you alter your hypothesis? Get into 2 big groups. Record data. ► Will you alter your hypothesis? Get into 1 group. Record data. ► Will you alter your hypothesis?
So what… ► ….does this tell us about sampling? ► …does this tell us about randomness? ► …does this tell us about the relationship between research and statistics?
Correlation: ► Used to describe the relationship between two variables. (co- relation) ► Measured in numbers from -1.0 to +1.0. ► -1.0 indicates negative relationship ► 0 indicates no relationship ► +1.0 indicates positive relationship ► Scatter plots are used for visual representation.
Inferential Statistics: ► Provides the researcher with a measure of confidence that the difference between groups is large enough to be important (or small enough to be insignificant.) In other words, IS provide a measure of how likely it was that results came about by chance. In summary it answers the question- was the difference mostly due to the Independent Variable rather than chance factors.
Statistically Significant? (Tests of Statistical Significance.) ► t-test: establishes if results were by chance or because of IV. ► ANOVA: compares means of 2 or more groups