Chapter 2 flashcards
Four main goals of psychology
Describe
* By simply describing thoughts and behavior, we can understand them better
Explain
* Conducting research helps explain why people think and behave the way they do
Predict
* Once we understand human behavior and thought, we can make predictions about how they will behave in the future
Change
* Psychology makes a difference in the world from treatment for mental health, changing habits, to educating children - it’s everywhere
Hindsight bias: I knew it all along phenomenon
Happens when, after an event has occurred, we believe we predicted it beforehand. This bias makes outcomes seem obvious and inevitable in retrospect, even if we had no way of knowing what would happen.
Example: After a sports game, you might think, “I knew our team would win!” even if you had doubts before the game.
Why It’s Misleading: It makes us overestimate our ability to predict events and blinds us to the reality of uncertainty.
Overconfidence: Too Much Faith in Our Own Judgment
Occurs when we overestimate the accuracy of our knowledge and judgments. It’s like wearing rose-colored glasses that make our abilities and decisions seem better than they actually are.
Example: You might be certain you aced an exam, only to find out you made several mistakes.
Why It’s Misleading: It leads to poor decision-making because we don’t seek out additional information or consider other viewpoints.
Confirmation Bias: Seeing Only What We Want to See
The tendency to search for, interpret, and remember information that confirms our preexisting beliefs or opinions. Instead of objectively evaluating all evidence, we give more weight to information that supports what we already think and discount evidence that contradicts our views.
Example: If you strongly support a political party, you’re more likely to favor news outlets and opinions that align with your views and ignore those that don’t, reinforcing your existing beliefs.
Why It’s Misleading: It skews our perception of reality, leads to poor decision-making, and can cause polarization by creating echo chambers where opposing viewpoints are disregarded.
What is a Theory?
A theory in psychology is like a roadmap that helps us organize our observations and make predictions about behavior. It’s a structured explanation based on principles that guide research and understanding.
The Importance of Hypotheses
A hypothesis is a specific, testable prediction derived from a theory. It’s crucial because it allows psychologists to conduct research that either supports or challenges the theory.
Example: “Teenagers who spend more than three hours a day on social media will report higher levels of anxiety and depression compared to those who spend less than one hour a day.”
Why It’s Important: It guides research by providing a clear statement to be tested.
Falsifiable: Can It Be Proven Wrong?
A falsifiable hypothesis is one that can be disproven through evidence.
Example: “All swans are white” is falsifiable because finding one black swan disproves it.
Why It’s Important: It ensures that hypotheses can be tested and potentially refuted.
Operational Definition: Making It Measurable
An operational definition specifies how a concept is measured or manipulated in a study.
Example: Defining “stress” as “cortisol levels in saliva.”
Why It’s Important: It allows for precise measurement and replication.
Replication: Testing Reliability
Replication is the process of repeating a study to see if the same results can be obtained.
Example: Conducting the same experiment on memory recall to verify findings.
Why It’s Important: It confirms the reliability and validity of research findings.
Peer Reviewers: The Gatekeepers of Quality
Peer reviewers are experts who evaluate the quality and validity of research before it gets published. They ensure that the research methods are sound and the conclusions are reliable.
Example: Before a study on new therapy techniques is published, peer reviewers critique the methods and findings.
Why It’s Important: It maintains the integrity and accuracy of scientific literature.
Non- experimental research methods
Research that observes and describes behavior without manipulation variables
Case study: In- depth analysis
A case study involves an in-depth investigation of a single individual or group.
Example: Studying a patient with a rare psychological disorder.
Why It’s Important: It provides detailed insights but may not be generalizable.
Correlational Study: Examining Relationships
A correlational study examines the relationship between two or more variables.
Example: Investigating the relationship between hours of sleep and academic performance.
Why It’s Important: Helps identify associations and make predictions but does not imply causation.
Meta- analysis
Statistical analysis of multiple studies on the same topic
Naturalistic observation
Observing behavior in its natural setting
Limitations of non- experimental research
Cannot establish cause and effect relationships
Advantages and disadvantages of a case study
Advantages:
* Provides detailed, rich qualitative data.
* Useful for studying rare or unique phenomena.
* Can generate new hypotheses for further research.
Disadvantages:
* Results may not be generalizable to the broader population.
* Can be time-consuming and resource-intensive.
* Subject to researcher bias in interpretation.
Correlation
Correlation helps predict
* I.E. low self esteem correlates (and therefore, predicts) depression
Does not imply cause and effect
* cannot say that low self esteem is the only cause for depression
Correlational study advantages and disadvantages
Advantages:
* Can study variables that cannot be manipulated experimentally.
* Can be conducted in natural settings.
* Can provide insights into potential causal relationships.
Disadvantages:
* Cannot establish cause-and-effect relationships. (Correlation does not equal causation)
* Subject to the third-variable problem (confounding variables).
Positive Correlation
Two sets of scores rise and fall together
Behave in the same way! Increase/ increase or decrease/ decrease
- I.E. less stress you drink less
- I.E. study less, scores go down
- I.E. study more, scores go up
Negative correlation
Relate inversely
* One goes up and the other goes down
- I.E. amount of exercise goes up, weight goes down
Correlation coefficient
- helps us figure out how closely two things vary together (correlate)
ex.
- how well do intelligence test scores predict achievement
- how well does low self esteem predict depression
How do we see correlation?
Scatterplots
- a graphed group (or cluster) of dots - shows patterns of correlation
- each dot represents the values of the 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
- perceived non-existent correlation
- a random coincidence
- when we notice
random coincidences,
we may forget that
they are random and
instead see them as
correlated, thus we
deceive ourselves by
seeing what is not
really there
Ex.
* rainy, cold
weather= catch a
cold
- when we notice
Meta-Analysis: Combining results
Meta-analysis statistically combines the results of multiple studies of the same topic.
Example: Analyzing numerous studies on the effectiveness of a specific therapy
Why it’s important: It increases statistical power and generalizab
Advantages of meta-analysis
- Increases statistical power by combining data from multiple studies
- Provides a comprehensive overview of research on a topic
- Can identify patterns and overall trends
Disadvantages of meta-analysis
- Dependent on the quality and consistency of included studies
- May be affected by publication bias (studies with significant results are more likely to be published)
- Complex and requires advanced statistical techniques
Naturalistic observation: Watching behavior
Naturalistic observation involves observing subjects in their natural environment without intervention
Example: Observing children’s play behavior in a park
Why it’s important: It provides authentic data on behavior in natural settings
Advantages of naturalistic observation
- Provides data on behavior in a natural setting
- Reduces the likelihood of participants altering their behavior due to being studied
- Useful for generating hypothesis about behavior
Disadvantages of naturalistic observation
- Lack of control over variables can make it hard to establish causation
- Observations can be influenced by observer bias
- Can be time-consuming and difficult to conduct
Survey: Gathering data
Surveys use questionnaires or interviews to collect data from a large number of people
Example: Asking high school students about their study habits
Why it’s important: It gathers a broad range of information quickly
Sampling techniques-
Population: The whole group
The population is the entire group that a researcher is interested in studying
Example: All high school students in the U.S.
Why it’s important: It defines the group to which the research findings will be generalized
Sampling techniques-
Sample: A subset of the population
A sample is a smaller group selected from the population to participate in the study
Example: 200 high school students from various schools
Why it’s important: It allows researchers to draw conclusions about the population
Sampling techniques-
Sampling Bias: Skewed selection
Sampling bias occurs when the sample is not representative of the population
Example: Surveying only honor students about study habits
Why it’s misleading: It leads to unrepresentative and unreliable results
Sampling techniques-
Random Sample: Equal Chance
A random sample ensures every member of the population has an equal chance of being selected
Example: Using a random number generator to select participants from a list
Why it’s important: It reduces bias and increases representativeness
Sampling techniques-
Convenience Sampling: Easy but Risky
Convenience sampling involves selecting participants who are readily available
Example: Surveying classmates because they are easy to reach
Why it’s misleading: It may not represent the broader population
Sampling techniques-
Representative sample: Miniature Population
A representative sample accurately reflects the demographics and characteristics of the population
Example: Ensuring the sample includes a mix of genders, ages, and socioeconomic statuses
Why it’s important: It enhances the generalizability of the study findings
Generalizability
The degree to which the results which of a study can be applied to different types of populations
Social desirability bias: Wanting to look good
Social desirability bias occurs when respondents give answers they think are socially acceptable rather than truthful
Example: Underreporting smoking habits in a health survey
Self-Report bias: Flawed Memories
Self-report bias occurs when participants provide inaccurate information about themselves
Example: Overestimating the number of hours studied per week
Experimenter bias: Influencing Outcomes
Experimenter bias happens when researchers’ expectations influence the outcome of a study
Example: A researcher unintentionally encouraging participants to respond in a certain way
Describing data- Measures of central tendency
- Mode (occurs the MOST)
- Mean (arithmetic AVERAGE)
- Median (MIDDLE score)
Range/Variance
- Distance from highest to lowest data
- smaller range = more
valid
- smaller range = more
Example: Basketball stats
A player scores
between 13 - 17
points a game
We can be more confident saying that player might score near 15 points in the next game; as opposed to if that players scoring varied between 5 to 25 points
Standard Deviation
- Still more useful is the standard deviation
- WHY? How much the data actually deviates (give you a clearer picture of how many fall near the mean)
Example: Average family income in a town-
Mean- This town makes 100,000 dollars on average
Range- People make anywhere from 10,000 to 1 million
Standard Deviation- More accurately tells us where the data is. If the SD is high then the data is all over the place; low means most people make around 100,000
Standard Deviation
= Square root ((sum of (deviations))/(Number of scores)
