U0 Psych In-Class Notes

Correlation: expresses a relationship between two variables

• No relationship 

  • Random, unrelated points (flat line)

  • Correlation does NOT equal causation

  • As more ice cream is eaten more people are murdered. Does ice cream cause murder, or murder causes people to eat ice cream?

• Positive Correlation

  • The variables go in the same direction. (Positive line)

• Negative Correlation

  • The variables go in opposite directions (Negative line)
    

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-1 0 +1

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  

• Greater the absolute value, the stronger the correlation coefficient

Illusory Correlation

• The perception of a relationship where none exists

• Example: more babies are born during a full moon

Experiment

• Can explain cause and effect when properly conducted

• Manipulate one or more factors in a controlled environment

Survey method

• Most common type of study in psychology

• Measures correlation

• Cheap and fast

• Need a good random sample

• Low-response rates

Correlation does NOT = causation: Experiments show causation

Experimental Method

• Shows that cause = effect

• Laboratory vs Field Experiments

• Theory: an explanation using an integrated set of principles that organizes and predicts observations. 

• Hypothesis: A testable prediction

  • If …, then … statements are helpful because they make sure you include the IV and DV

    • IV is what you change 

      • What's being manipulated in the experiment

      • If there is a drug in the experiment, the drug is usually the IV

      • Brings about change

    • DV is the measured thing being changed by the independent variable

      • Would be the effect of the drug

  • Expresses a relationship between two variables

  • A variable is anything that can that can vary among participants in a study

• Operational Definition (be careful to remember and do this)

  • Explain what you mean in your hypothesis

  • How will the variables be 

  • How you operationalize the variables will tell us if the study is valid and reliable

1. Defining the variable

2. How the variables will be measured in “real life” terms

• Description 

  • Population: All the cases in a group from which samples may be drawn for a study

  • Sample: a fraction group from all of the population

    • Have to get a random sample to represent the bigger population

    • Identify the population you want to study

    • The sample must be representative of the population you want to study

  • Random Assignment

    • Once you have a random sample, randomly assig

• Experimentation

  • Experimental Group

    • Condition of an experiment that exposes participants to the treatment, that is, to one version of the independent variable

  • Control group

    • Condition of an experiment that contrasts with the experimental treatment

• Beware of confounding variables

  • The object of an experiment is to prove that A causes B

  • A confounding variable is anything that could cause a change in B that is not A

  • If I wanted to prove smoking causes heart issues confounding variables are

    • Lifestyle

    • Genetics

  • Experimenter Bias (Another confounding variable)

    • If I spend more time watching one group because I know which one is being tested I will have bias

    • Non conscious act

    • To avoid this don't tell the researcher or the participant: Double-blind Procedure

• Hawthorne Effect

  • Even the control group may experience changes

  • Knowing can cause the experiment to change

• Placebo effect

  • Just knowing there is a treatment can have a therapeutic effect

• Order effect

  • How treatments are presented can change the outcome

Full metal jacket

The hotzone

Harvard Bias test

APA Ethical GUidelines for Research

• IRB - Internal Review Board

• Both for humans and animals

Animal Research: 

• Clear purpose

• Treated in a humane way

• Acquire animals legally

• Least amount of suffering possible

Human Research:

• No Coercion (must be voluntary)

• Informed consent

• Anonymity (needs written permission)

• No significant risk

• Must debrief

  • Deception Can be used if you debrief

• Right to withdraw

Statistics:

• Recording 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

  • Frequency polygons or histograms

• Central Tendency

  • Mean

  • Median

  • Mode

  • Range

  • normal distribution

  • Memorize numbers ^^^^

  • One starting deviation from the mean is 68%^

  • standard deviation

    • Measures averages and difference between each score and the mean of the data set of a measure of how much scores vary around the mean

    • Higher the variance or SD the more spread out the distribution is

  • statistical significance

    • A statistical statement of how likely it is that an obtained result occurred by chance

  • Watch for extreme scores or outliers

    • Outliers skew distributions

    • 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)

  • Z score  WHAT IS Z SCORE

  • Inferential statistics

    • The purpose is to discover whether the finding can be applied to the larger population from which the sample was collected. (forming conclusions

    • P-value equal to or less than .05 for statistical significance

    • This means 5% likely the results are due to chance

Things to remember:

• Random Sampling vs Random Assignment: Random sampling is about selecting a representative sample from a population, while random assignment is about placing those sample members into different groups within an experiment

• Effect size vs correlation coefficient: Effect size and correlation coefficients are related measures that quantify the strength of relationships between variables, but they differ in their specific applications and interpretations. Effect size generally refers to the magnitude of an effect (e.g., a difference between groups), while a correlation coefficient, like Pearson's r, specifically measures the strength and direction of a linear relationship between two variables.

• Correlation coefficient vs correlation: Correlation refers to the statistical relationship between two or more variables, indicating how much they change together. A correlation coefficient is a specific numerical value that quantifies the strength and direction of that relationship, typically represented by "r".