Vocabulary: 

Hindsight bias: the tendency to believe, after learning an outcome, that one would have foreseen it. Also known as the “I-knew-it-all-along” phenomenon

Theory: an explanation using an integrated set of principles that organizes observations and predicts behaviors or events. 

Hypothesis: a testable prediction, often implied by a theory

Operational Definition: a carefully worded statement of the exact procedures (operations) used in a research study.

Replication: repeating the essence of a research study, usually with different participants in different situations, to see whether the basic finding can be reproduced. 

Case study: a descriptive technique in which one individual or group is studied in depth in the hope of revealing universal principles. 

Naturalistic Observation: a descriptive technique of observing and recording behavior in naturally occurring situations without trying to manipulate or control the situation. 

Survey: a descriptive technique for obtaining the self-reported attitudes or behaviors of a particular group, usually by questioning a representative, random sample of the group. 

Sampling bias: a flawed sampling process that produces an unrepresentative sample. 

Population: all those in a group being studied, from which samples may be drawn. 

Random sample: a sample that fairly represents a population because each member has an equal chance of inclusion. 

Correlation: a measure of the extent to which two factors vary together, and thus, how well either factor predicts the other. 

Correlation coefficient: a statistical index of the relationship between two things (from -1.00 to +1.00). 

Variables: anything that can vary and is feasible and ethical to measure

Scatterplots: a graphed cluster of dots, each of which represents the values of two variables. The slope of the points suggests the direction of the relationship between the two variables. The amount of scatter suggests the strength of the correlation (little scatter indicates high correlation). 

Illusory correlation: perceiving a relationship where none exists, or perceiving a stronger-than-actual relationship. 

Regression toward the mean: the tendency for extreme or unusual scores or events to fall back (regress) toward the average

Experiment: a research method in which an investigator manipulates one or more factors (independent variables) to observe the effect on some behavior or mental process (the dependent variable). By random assignment of participants, the experimenter aims to control other relevant factors. 

Experimental group: the group exposed to the treatment, that is, to one version of the independent variable. 

Control group: the group not exposed to the treatment in an experiment; contrasts with the experimental group and serves as a comparison for evaluating the effect of the treatment. 

Randomly assign: assigning participants to experimental and control groups by chance, thus minimizing the pre-existing differences between the different groups. 

Double-blind procedure: an experimental procedure in which both the research participants and the research staff are ignorant (blind) about whether the research participants have received the treatment or a placebo Commonly used in drug-evaluation studies. 

Placebo effect: experimental results caused by expectations alone; any effect on behavior caused by the administration of an inert substance or condition, which the recipient assumes is an active agent. 

Independent variable; the factor that is manipulated in an experiment; the variable whose effect is being studied. 

Confounding variables: a factor other than the factor being studied that might influence a study’s results. 

Dependent variable; in an experiment, the outcome that is measured; the variable that may change when the independent variable is manipulated. 

Validity: the extent to which a test or experiment measures or predicts what it is supposed to. 

Informed consent: giving potential participants enough information about a study to enable them to choose whether they wish to participate. 

Debrief: the post experimental explanation of a study, including its purpose and any deceptions, to its participants. 

Descriptive statistics: numerical data used to measure and describe characteristics of groups. Includes measures of central tendency and measures of variation. 

Histogram: a bar graph depicting a frequency distribution. 

Mode: the most frequently occurring score(s) in a distribution. 

Mean: the arithmetic average of a distribution, obtained by adding the scores and then dividing by the number of scores. 

Median: the middle score in a distribution; half the scores are above it and half are below it. 

Skewed distribution: a representation of scores that lack symmetry around their average value. 

Range: the difference between the highest and lowest scores in a distribution. 

Standard deviation: a computed measure of how much scores vary around the mean score. 

Normal curve: a symmetrical, bell-shaped curve that describes the distribution of many types of data; most scores fall near the mean (about 68% fall within one standard deviation of it) and fewer and fewer near the extremes. 

Inferential statistics: numerical data that allow one to generalize---to infer from sample data the probability of something being true of a population. 

Statistical significance: a statistical statement of how likely it is that an obtained result occurred by chance. 

THINKING CRITICALLY

• What is hindsight bias? Describe a time when you experienced this phenomena:

  • Hindsight bias is the tendency to believe, after already having learned an outcome of something, that no one would have ever foreseen it. This is also known as the “I knew it all along phenomenon”. This is an easy bias to demonstrate because you can give half of the members of a group some psychological finding and give the other half of the population an opposite result (because they can see how their perspective of the finding is true). 

• How can overconfidence affect thinking/behavior? 

  • Overconfidence can affect thinking/behavior because it can skew our results (because we tend to be more overconfident than we actually are correct most of the time). Overconfidence allows for us to feel that we are correct, when in reality we are more than likely incorrect about something. This may be bad for psychology purposes because someone may be overconfident (without critically thinking) and they could incorrectly diagnose someone (in the field of clinical psychology/psychiatry). 

• Why do we need to think critically in psychology? Give an example to explain. 

  • Critical thinking is important in psychology because it allows for us to be reasonable when researching in the field of psychology, and it allows for us to push away those “roadblocks” of critical thinking. For example, Actual evidence is needed when thinking critically in psychology because in order for a certain behavior to be studied properly, it needs a proper sample size as well as accurate data/results. A critical thinker is not just going to be overconfident that this social experiment/research they are doing is the way they think; they are going to instead test their hypothesis and stray away from gut-intuition. 

    • This method could be used for diagnosing cognitive disorders/treatments for certain cognitive disorders, mental health, etc. 

THEORIES v HYPOTHESES

What is the difference between theories and hypotheses? All hypotheses should begin: “It is predicted that…”

Why is it important to operationally define our variables? 

• Operationally define: a carefully worded statement of the exact procedures (operations) used in a research study. For example, human intelligence may be operationally defined as what intelligence test measures. 

  • It is important to operationally define our variables because others can replicate (or repeat) original observations using different participants, circumstances, and also materials. If they get similar results, confidence in the study’s credibility increases. 

    • Replication is confirmation. A lack of replication may allow us to revise our comprehension of a study. 

Descriptive Methods: Complete the following chart

CORRELATIONS

What does it mean to say two things correlate? When two things correlate, it means that the two factors vary together, and thus how well either factor predicts the other. Naturalistic observations and surveys often can show us that one trait or behavior will tend to coincide with the other. This can be measured with a statistical measure, known as the correlation coefficient. 

Correlations help us make: predictions on results that are either close to each other or vary. 

What does this statement mean?: “Correlation does not equal causation”

• Correlation in results does not directly relate to the cause of something that is unrelated; for example (as stated in the textbook), the length of marriage positively correlates with hair loss in men. Does this mean that marriage causes men to lose their hair or that men without hair are likely to be in a better marital standpoint with their spouse. 

What is an illusory correlation? An illusory correlation is the perception that a relationship exists, but none does at all, or perceiving a “stronger-than-actual” relationship between two scores. 

EXPERIMENTATION

How is an experiment different from a correlational method? Why is experimentation valuable?

• Experiment: research method where an investigator manipulates one or more factors (also known as independent variables) in order to observe the effect on some sort of behavior and/or mental processes, known as the dependent variable. By “random assignment of participants”, the experimenter aims to control other relevant factors. 

• Experimentation is valuable because it allows for accurate results with something to compare it to. It shows what happens when an independent variable is changed in the experiment so that you can test if it affects the results of the experiment (dependent variable). 

What is the difference between an experimental group and a control group? Why do we need both? 

• Experimental Group: the group exposed to the treatment (change) and is being exposed to the independent variable. 

• Control Group: group that is not exposed to the treatment. It contrasts with the experimental group and is used for comparison in evaluating the effect of the treatment. 

• Both an experimental and control group are necessary because they show what happens how some independent variable affects the dependent variable (experimental: independent variable; control: no change in the independent variable, “standard” for comparison in an experiment). 

Why do we use random assignment? How is it different from random selection? 

• Random assignment is used to minimize pre-existing differences between groups in an experiment. It effectively equalizes two groups (this can be done by flipping a coin). 

• Random selection does not prevent the pre-existing differences between a group of people in an experiment. 

Give an example of when to use a double blind procedure:

• Double-blind procedure: an experimental procedure in which both the research participants and the research staff are ignorant about whether the research participants have received the treatment of a placebo. 

  • This is commonly used in drug-evaluation studies; testing the effects of some kind of medication. This is so that neither the participants or the researchers are able to bias the results of the experiment. 

What is a placebo effect? Why is it important to be aware of this?

• Placebo effect: experimental results caused by expectations alone; any effect on behavior caused by the administration of an inert substance or condition, which the recipient assumes is an active agent. 

  • It is important to be aware of this because it can cause people to act differently, since they believe that they have had some kind of substance that makes them behave in a certain way which can skew the results of an experiment. “Thinking” that you are getting a treatment or some kind of change can relax your body and make your symptoms better (if you are given medication like this). 

Define:

• Independent variable: The factor that is being changed/manipulated in an experiment; the variable whose effect is being studied and determining the dependent variable. 

• Dependent variable: This is what is actually being measured (the outcome); this variable may change when the independent variable is being changed/altered. 

• Confounding variable: A factor that is anything other than the factor being studied that might influence a study’s results. 

Each of the following is a hypothesis of an experiment.  For each one list the Independent Variable (IV), Dependent Variable (DV), Experimental Group (EG), and Control Group (CG) and 2 possible confounding variables (CV).

“There will be a statistically significant difference in graduation rates of at-risk high-school seniors who participate in an intensive study program as opposed to at-risk high-school seniors who do not participate in the intensive study program.”

“After watching a videotaped reenactment of a bank robbery, people will recall more about the robbery while being questioned under hypnosis by a police officer as opposed to not being under hypnosis.”

“Caffeine will increase the maze running performance of older rats.”

DESCRIPTIVE STATISTICS

• Descriptive Statistics: numerical data used to measure and describe characteristics of groups. Includes measures of central tendency and measures of variation.

• Histogram: A bar graph depicting a frequency distribution. This is basically just a bar graph. 

Measures of Central Tendency

(average)

What does it mean to say something is skewed? When something is skewed, it lacks symmetry around the average value of the data. This means that the mean may be biased by a few extreme incomes as well. (This happens when the distribution is lopsided). When the data is skewed, these measures of central tendency (as listed above) do not summarize the data. 

Measures of Variation

(diversity of scores)

• Range: the difference between the highest and lowest scores in a distribution

• Standard Deviation:  computed measure of how much scores vary around the mean score

• Normal Curve: a symmetrical, bell-shaped curve that describes the distribution of many types of data; most scores fall near the mean (about 68% fall within one standard deviation of it) and fewer and fewer near the extremes (ends) of the data). 

INFERENTIAL STATISTICS

• Inferential statistics: numerical data that allow one to generalize---to infer from sample data the probability of something being true of a population. These help to know if results can be generalized to a much larger population.  

• Statistical significance: a statistical statement of how likely it is that a obtained result occurred purely by chance. When sample averages are reliable, and when the difference between them is relatively large, this means that the difference has statistical significance. In other words, the observed difference is probably not due to chance variation between the samples. 

Research Methods: The Need For Psychological Science

Cognitive Biases: “Hindsight is 20:20” 

1. Hindsight Bias

2. Overconfidence 

3. Random Patterns: odds are the same every single time, even when you think there is a pattern to something. 

Importance: Hindsight bias, overconfidence, and our tendency to perceive patterns in random events often lead us to overestimate our intuition. But, scientific inquiry can help us sift reality from illusion. 

Scientific Method: 

1. Identify a Problem or Question 

2. Formulate a hypothesis

3. Collect Data

4. Analyze Data

5. Draw conclusions

Where Does all of this research begin? 

Theories 

• Explain, Organize, Directs Research 

Hypothesis

• Specific, testable

• Can be confirmed or refute 

  • Falsifiability: the possibility that an idea, hypothesis, or theory can be disproven by observation or experiment. 

• “It is predicted that…” 

Null Hypothesis: A hypothesis that proves that something has happened by chance. 

The point of research is to reject the null hypothesis. 

Operational Definition

• Carefully worded (define what you are referencing) 

• Enables Replication! 

  • Why is it important to operationally define variables? 

    • Researchers like to use thing easier categorized (independent variables)

      • “Person variables”: age, gender, ethnicity

      • Objective variables: speed, distance, count (eg. #correct) 

      • Not everything is as easily defined 

Replication (repeat) 

• Different participants, situations, etc. 

• Theories are strengthened with replication. 

Something needs to be experimented multiple times and with all different age groups but also expand out of the small sample size you started with initially. 

Descriptive Methods: (“Non-experimental studies). 

Archival Research 

Case Study

Naturalistic Observation 

Survey Research 

• Wording effects

• Random sampling/selection 

  • Avoid sampling bias 

Pros and Cons to Descriptive Methods: 

Basic Research Terms: 

• Study: We prefer to use this as a term instead of experiment (when referring to research). 

• Participants: a.k.a subjects 

  • Population vs Sample

    • Random Sample: Good because you are not methodically picking people to participate in the study. 

• Variables: 

  • Independent: manipulated (at least 2 categories)

  • Dependent: measured

    • The behavior that “depends on” the independent variable. 

  • Confounding or Extraneous: 3rd variables, unmeasured (the stuff that we haven’t thought about when doing a study); dietary restrictions when surveying people for their favorite foods in the cafeteria, etc. 

    • Extraneous: anything that could create a change

    • Confounding: anything that does create a change. 

Correlations

Two things seem to be related to each other; predictive of each other. 

Correlational Research: How well does A predict B 

• Positive Correlation: as 1 variable increases, the other increases too

  • Not always “good” 

• Negative Correlation: as 1 variable increases, the other decreases too 

  • Not always “bad”  

Correlation does not equal causation!

Pearson’s r Correlation Coefficient 

• Strength of the correlation 

• -1.0 to +1.0 (Cannot Go Over 1)

• Whenever the score is, think of the negative or positive as not a scale-value, but a correlation (whether or not it’s negative or positive). As close as you get to either side of this is the strongest correlation.  

  • Negative correlation; negative number

  • Positive correlation; positive number.  

• Scatterplot 

Positive Correlation Means Same Directions

Negative correlation Means Opposite Directions 

Add Scale of correlation coefficient chart

Illusory Correlation: perception of a relationship where none exists. 

3rd Variable that has an influence

• a.k.a , extraneous variable or confounding variable 

Example: Length of Marriage and Hair Loss 

• 3rd Variable: Age

• Hair loss was probably going to happen anyway (you have to be a critical thinker and notice this perspective). 

Type I and Type II Factors 

Experimental Research 

• 2 or More Variables 

• Deliberately producing a change in one variable

• Observing the Effect

The ONLY way to demonstrate causation! (WITH CERTAINTY). 

Validity: the extent to which a test or experiment measures or predicts what it is supposed to. 

Illusory correlations are random events that we notice and falsely assume are related to one another. Regression toward the mean is the tendency for extreme or unusual scores to fall back toward their average. To discover cause-effect relationships, psychologists conduct experiments, manipulating one or more variables of interest and controlling other variables. Using random assignment, they can minimize confounding variables, such as pre-existing differences between the experimental group (exposed to the treatment) and the control group (given a placebo or different version of the treatment). The independent variable is the factor the experiment manipulates to study its effect; the dependent variable is the factor the experimenter measures to discover any changes occurring in response to the manipulation of the independent variable. Studies may use a double-blind procedure to avoid the placebo effect and researcher’s bias. An experiment has validity if it tests what it is supposed to test. 

Research Design and Ethics in Psychology

How To Know Which Design to Use: 

These different research design methods help us to determine how to set up experiments/studies the most effectively. They consider how much money and time are available, ethical issues, and other limitations, such as ethics and morals. 

Researchers design each study, measure target behaviors, interpret results, and learn more about the world of behavior and mental processes in addition to their findings.

Consider this: the experimenter intends the laboratory environment to be a simplified reality, which stimulates and controls important features of reality and everyday life. A laboratory experiment lets psychologists re-create psychological forces under controlled conditions.

• An experiment’s purpose is not to recreate the exact behaviors of everyday life but to test theoretical principles. It is the resulting principles---not the specific findings--- that illuminate everyday behaviors. 

When psychologists apply lab research, they apply theoretical principles that they have refined through many different experiments. (Principles of the visual system, developed from experiments in artificial settings (looking at red lights in the dark). 

It has been proven that investigations show that principles derived in the laboratory typically do generalize to the everyday world. 

Significance: Psychological science focuses on less specific behaviors than on revealing general principles that help explain many behaviors. Although psychological principles may help predict behaviors for groups of people, they more faintly predict behavior for an individual in any given situation. 

• For instance, knowing student’s ages may clue us to their average vocabulary level, but individual students’ word power will vary. 

Random assignment of participants is used to isolate the effects of an independent variable on a dependent variable. 

Studying and Protecting Animals (Ethics)

Psychologists will often study animals to learn more about people. Animal experiments have led to treatments for human diseases.

Is it right to place the well-being of humans above that of other animals? 

What safeguards should protect the well-being of animals in research? This question emerges for those who give human life top priority. 

The animal protection movement protests the use of animals in psychological, biological, and medical research. 

Some 98% supported government regulations protecting primates, dogs, and cats, and 74% supported regulations providing for the humane care of rats and mice. 

APA (American Psychological Association) guidelines: state researchers must provide “human care and healthful conditions” and that testing should “minimize discomfort”. The European Parliament also mandates standards for animal care and housing. 

Ohio Research Team (Regarding Dogs): Devised handling and petting methods to reduce stress and ease the dogs’ transitions to adoptive homes from animal shelters. This shows that animals have themselves benefited from psychological research. 

At its best, a psychology concerned for humans and sensitive to animals serves the welfare of both. 

Studying And Protecting Humans

Most psychological studies are free of such stresses as seen in movies/TV shows. Blinking lights, flashing words, and pleasant social interaction are much more common in psychological studies/experiments. Occasionally, researchers can temporarily stress or deceive people to a justifiable end, such as to understand and control violent behavior or study mood swings. Some experiments will not be effective if participants know everything beforehand. 

Ethics Codes of the APA and Britain’s BPS Urge Researchers to: 

1. Obtain potential participants’ informed consent (giving potential participants enough information about a study to enable them to choose whether they wish to participate) to take part. 

2. Protect participants from greater-than-usual harm and discomfort (physical and mental harm). 

3. Keep information about individual participants confidential

4. Fully debrief (the post experimental explanation of a study, including its purpose and any deceptions, to its participants) by explaining the research afterward, including any temporary deception. 

• Assurance that participation in research is completely voluntary. 

• Assent: giving consent to children under 18 to participate (the adult gives you consent). 

You may lose participants and you cannot force people to participate. 

Many university ethics committees also have guidelines that screen research proposals and safeguard human participants’ well-being. 

Values in Psychology

Values affect what we study, how we study it, and how we interpret results. Researchers’ values influence their choice of research topics. A science of behavior and mental processes can help us reach our goals, but it cannot decide what those goals should be. 

Psychology’s power is to enlighten. It speaks to many of our world’s great problems such as war, overpopulation, prejudice, family crises, and crime. It may not be able to address all of life’s great questions, but it speaks to some very important ones.  

Module 7 Review: 

• How would you know which research design to use? 

  • Psychological scientists design studies and choose research methods that will best provide meaningful results. 

  • Researchers generate testable questions, and then carefully consider the best design to use in studying those questions (experimental, correlational, case study, naturalistic observation, twin study, longitudinal, or cross-sectional) 

  • Next, psychologists measure the variables they are studying, and finally they interpret their results, keeping possible confounding variables in mind. 

• How can simplified laboratory conditions illuminate everyday life? 

  • Researchers intentionally create a controlled, artificial environment in the laboratory in order to test general theoretical principles. These general principles help explain everyday behaviors. 

• Why do psychologists study animals, and what ethical guidelines safeguard animal research subjects? 

  • Some psychologists are primarily interested in animal behavior; others want to better understand the physiological and psychological processes shared by humans and other species. 

  • Government agencies have established standards for animal care and housing. Professional associations and funding agencies also have guidelines for protecting animals’ well-being. 

• What ethical guidelines safeguard human research participants? 

  • The APA ethics code outlines standards for safeguarding human participants’ well-being, including obtaining their informed consent and debriefing them later. 

• How do values affect psychological science? 

  • Psychologists’ values influence their choice of research topics, their theories and observations, their labels for behavior, and their professional advice. 

  • Applications of psychology’s principles have been used mainly in the service of humanity. 

Statistical Reasoning

Basic Psychology Statistics 

Histograms: illustrate characteristics of the group under study. 

Sometimes, the graphs are scaled on the y-axis differently to emphasize significant differences. When zooming in, the different things used in the study show the significance in frames. 

Therefore, scaling is different! 

Measures of Central Tendency: Average Representation of Anything 

• Mode (occurs the most; number that occurs the most)

• Mean (arithmetic average) 

• Median: (middle score) 

Degrees of Freedom: used to account for human error 

• n-1 

(X with the line over it): mean 

Percentile Rank: the percentage of scores that are lower in a given score

• 99% means you scored higher than 99% of your peers

Skewed Distribution: a representation of scores that lack symmetry around their average value. 

Regress to the Mean: Are their results constant? Are we consistently performing at our average? 

Measures of Variation: 

• Range

• Standard Deviation

Standard Deviation: Average difference among each score. 

• Greater the deviation, the greater the variability. You are not always sure if you reduced the change. The chance is that it is indicative of what you are seeing. 

Example for class grades: Even if the average for something is better (mean), the variation being less means that they still did better. Smaller standard deviations are better and more indicative and less variability. 

Inferential Statistics

When is an Observed Difference Reliable? 

Inferential Statistics determine if results can be generalized to a larger population. 

Examples: 

• T-test

• ANOVA (Analysis of Variance)

  • Does caffeine (IV) influence running speed 

Making Inferences 

• Representative samples are better than biased samples

• Less variable observations are more reliable than those that are more variable

• More cases are better than fewer

Meta-Analysis: a statistical procedure for analyzing the results of multiple studies to draw conclusions. 

Statistical Significance 

• The averages are reliable

• The differences between averages is relatively large

• Implies the importance of the results 

Statistical Significance means the results likely did not occur by chance. 

• Remember the goal is to REJECT THE NULL HYPOTHESIS
  

We can only do this with statistical significance. 

P = % by chance

P <= 0.05 (95% chance) minimum to accept statistical significance!