Presentation on theme: "It’s actually way more exciting than it sounds!!! It’s actually way more exciting than it sounds!!! Research Methods & Statistics."— Presentation transcript:
It’s actually way more exciting than it sounds!!! It’s actually way more exciting than it sounds!!! Research Methods & Statistics
Why do we have to learn this research stuff? Psychology is first and foremost a science… …So it is based in research.
APPLIED VS. BASIC RESEARCH Applied Research has clear, practical applications. YOU CAN USE IT!!! Basic Research explores questions that you may be curious about, but not intended to be immediately used. Studying how kissing changes when you get older is interesting…but that’s about it. Research on therapies for drug addicts has a clear purpose.
Hypothesis & Theory Hypothesis: a testable prediction about what you expect to happen – Prediction that expresses a relationship between two variables – A variable is anything that can vary among participants in a study – EX: participating in class leads to better test grades than not participating Theory: an explanation that is testable and backed by evidence – Ex: Freud’s theory of psychosexual dev.
Hindsight Bias Befo re we dive into how to do research, you should be aware of three hurdles that tend to skew our logic. The tendency to believe, after learning the outcome, that you knew it all along. After Amy Winehouse died my brother said he always knew she was going to die young. Did he really? Monday Morning Quarterback!
Overconfidence We tend to think we know more than we do. 82% of U.S. drivers consider themselves to be in the top 30% of their group in terms of safety. 81% of new business owners felt they had an excellent chance of their businesses succeeding. When asked about the success of their peers, the answer was only 39%.
3 Types of Research Psychologists conduct 3 main types of research: 1. Experimental-an investigation seeking to understand relations of cause and effect 2. Descriptive-drawing conclusions from observation & studies Case studies Surveys Naturalistic observations 3. Correlational-assessing the degree of association between 2 or more variables that occur naturally EX: Relationship b/w violent media & aggression in kids
CASE STUDIES One person is studied in depth in hopes of revealing conclusions about other similar people/cases Strength: you get a full, detailed picture of one person (tons of info) Weakness: the findings are not always GENERALIZABLE to the entire population – Individual could be not typical of the larger population
Cross-Sectional & Longitudinal Studies Cross-sectional Study: people of different ages are compared with each other Longitudinal Study: the same people are restudies & retested over a long period of time We’ll cover these more in development unit…
SURVEYS Technique for learning self-reported attitudes or behaviors of a particular group by questioning a random sample of the group Wording is important – Ex: “helping the poor” vs. “welfare” – Ex: 27% approved of “government censorship” of sex & violence in media but 66% approved of “more restrictions on what is shown on tv” Strengths: cheap & easy way to collect data Weaknesses: – Not always worded well – People are not always truthful in responses – Very low response rate (maybe 1 in 100)
Random Sample You want to know whether the amount of time spent of homework affects test scores Identify the population you want to study – Ex: all juniors at La Serna GET A RANDOM SAMPLE – Represents the larger population b/c everyone has an equal chance of getting chosen (not volunteer=not representative) – Ex: political polls 1500 people across country randomly
NATURALISTIC OBSERVATION Observing & recording behavior in naturally occurring situations – cannot intervene in any way Ex: watching children interact on a playground Strengths: easy source of collecting data Weaknesses: – Cannot know what people are thinking or feeling b/c you are only observing the behavior – Describes behavior but doesn’t explain
EXPERIMENTS! The only type of research that can show CAUSE & EFFECT!!! – Operational Definitions – Independent Variable – Dependent Variable – Confounding Variables – Control Group – Experimental Group – Random Assignment – Double Blind – Placebo Effect
Operational Definitions Explain what you mean in your hypothesis. How will the variables be measured in “real life” terms. How you operationalize the variables will tell us if the study is valid and reliable. Let’s say your hypothesis is that chocolate causes violent behavior. What do you mean by chocolate? What do you mean by violent behavior?
INDEPENDENT VARIABLE Whatever is being manipulated in the experiment. Hopefully the independent variable brings about change. *If there is a drug in an experiment, the drug is almost always the independent variable.
Dependent Variable Whatever is being measured in the experiment. It is dependent on the independent variable. The dependent variable would be the effect of the drug.
Beware of Confounding Variables!!! If I wanted to prove that smoking causes heart issues, what are some confounding variables? The object of an experiment is to prove that A causes B. A confounding variable is anything that could cause change in B, that is not A. Lifestyle and family history may also effect the heart.
Random Assignment Once you have a random sample, randomly assigning them into two groups helps control for confounding variables. Experimental Group v. Control Group. Done to assure that each group has minimal differences
Control vs. Experimental Group Experimental Group – The group that is given the independent variable Control Group: the group that is NOT given the independent variable – Serves as comparison to experimental group
Single & Double Blind Procedure Want to avoid experimenter bias Single Blind: the subjects don’t know if they are in the control of experimental group Double Blind: neither the subjects nor the researcher know who is in the 2 groups – Requires 3 rd party has to participate to obtain data Placebo – An object/procedure given to control group so they think they are experimental group – Does not contain any of the tested material (IV)
Hawthorne Effect But even the control group may experience changes. Just the fact that you know you are in an experiment can cause change. Whether the lights were brighter or dimmer, production went up in the Hawthorne electric plant.
Research Ethics 1. Obtain informed consent of participants 2. Protect them from harm & discomfort 3. Treat information about participants confidentially 4. Fully debrief participants: explain the research afterward
Correlation Assesses the degree of association between two or more variables that occur naturally together – Ex: Poor school performance may be related to lack of sleep – Ex: education level may be related to political affiliation or income… – Surveys are a good method to provide data for correlations CORRELATION IS NOT CAUSATION! – Other factors could influence
Correlational Method Correlation expresses a relationship between two variable. Does not show causation. As more ice cream is eaten, more people are murdered. Does ice cream cause murder, or murder cause people to eat ice cream ?
More Correlation Stuff… Correlation Coefficient: statistical measure of the relationship between to things (+1 to -1) – The closer to +1 OR -1 the STRONGER the relationship – The closer to 0 the WEAKER the relationship Scatterplot: graph that represents the values of 2 variables – The direction of dots shows positive or negative correlation Which is a stronger correlation? -.13 or +.38 -.72 or +.59 -.91 or +.04
Types of Correlation Positive Correlation The variables go in the SAME direction. Negative Correlation The variables go in opposite directions. Studying and grades hopefully has a positive correlation. Heroin use and grades probably has a negative correlation.
Positive Correlation b/c both variables are increasing
Negative Correlation b/c one variable in increasing and one veriable is decreasing
No Relationship (not going in either direction)
Statistics Recording the results from our studies. Must use a common language so we all know what we are talking about.
Descriptive Statistics Just describes sets of data. You might create a frequency distribution.
Central Tendency Mean, Median and Mode. Watch out for extreme scores or outliers. $25,000-Pam $25,000- Kevin $25,000- Angela $100,000- Andy $100,000- Dwight $200,000- Jim $300,000- Michael Let’s look at the salaries of the employees at Dunder Mifflen Paper in Scranton: The median salary looks good at $100,000. The mean salary also looks good at about $110,000. But the mode salary is only $25,000. Maybe not the best place to work.
Normal Distribution In a normal distribution, the mean, median and mode are all the same.
Distributions Outliers skew distributions Mean!!! If group has one high score, the curve has a positive skew (contains more low scores) If a group has a low outlier, the curve has a negative skew (contains more high scores)
Other measures of variability Range: distance from highest to lowest scores. Standard Deviation: the variance of scores around the mean. The higher the variance or SD, the more spread out the distribution is. Kobe and Dwight Howard may both score 30 ppg (same mean). But their SDs are very different.
Inferential Statistics The purpose is to discover whether the finding can be applied to the larger population from which the sample was collected P-value=.05 for statistical significance 5% likely the results are due to chance
Illusory Correlation When you think that a relationship exists when really there is none Ex: you think you do better on tests when you wear your lucky sweatshirt
Reliable & Valid We want all of our research to be reliable & valid Reliability (reliable): replicate (repeat) the study & get the same results Validity (valid): research measures what it is supposed to