Unit 0 Research Methods in Psychology 📄

Experimental Methodologies

Control Groups: The group in an experiment that does not receive the treatment.
Example: In a drug study, the control group may receive a sugar pill.
Experimental Groups: The group that receives the treatment or intervention.
Example: Participants receiving the actual medication in the drug study.
Independent Variable (IV): The variable that is manipulated by the researcher.
Example: The amount of a drug given in a study.
Dependent Variable (DV): The outcome that is measured in the experiment.
Example: The level of pain reported by participants after treatment.
Placebo Group: A group that receives a placebo to compare against the experimental group.
Example: Participants who believe they are receiving treatment but receive no active ingredients.
Operational Definitions: How variables are specifically measured in a study.
Example: Defining "happiness" as a score on a standardized happiness questionnaire.
Confounding Variables: External factors that may affect the DV, leading to inaccurate results.
Example: If studying the effect of sleep on test scores, not controlling for study time can be confounding.
Hypothesis: A testable prediction about the relationship between variables.
Example: "Increased study time will improve test scores."
Falsifiable: A characteristic of a hypothesis that allows it to be proven wrong.
Example: "All swans are white" can be falsified by finding a black swan.
Peer Review: The process of having other experts evaluate research before publication.
Example: Submitting a study to a journal where experts critique the methods and results.
Institutional Review: Ethical guidelines that protect participants in research studies.
Example: Ensuring informed consent and confidentiality before conducting a study.
Experimenter Bias: When a researcher’s expectations influence the outcome of a study.
Example: A researcher unconsciously gives more attention to participants they believe will perform better.
Single/Double Blind Procedures:
- Single Blind: Participants do not know if they are in the control or experimental group.
- Double Blind: Neither participants nor researchers know who is receiving the treatment, reducing bias.
Sampling of a Population: The method of selecting participants for a study.
- Representative: A sample that accurately reflects the population.
- Convenience Sample: Selecting participants who are easy to reach; may lead to bias.
- Random Assignment: Randomly assigning participants to control or experimental groups to ensure equality.
Confederates: Individuals in an experiment who are part of the research team but act like participants.
Example: An actor posing as a participant in a study to manipulate social interaction.
Hindsight Bias: The tendency to see events as being predictable after they have occurred.
Example: After an election, saying you knew the outcome all along.
Replication: Repeating a study to see if the results are consistent.
Example: Conducting the same drug trial in different locations to verify findings.
Generalizability: The extent to which findings from a study can apply to the broader population.
Example: If a study on college students applies to all young adults.

Non-Experimental Methodologies

Survey Methodology: Techniques for collecting data through questionnaires or interviews.
Example: Using an online survey to gather opinions on a new product.
Longitudinal Studies: Research conducted over a long period to observe changes over time.
Example: Following a group of children from childhood to adulthood.
Cross-Sectional Studies: Observing different subjects at one point in time to compare various groups.
Example: Surveying different age groups about their technology use in a single year.
Demand Characteristics: Cues that influence participants to respond in a way they think the researcher desires.
Example: Participants might guess the study's purpose and change their behavior accordingly.
Social Desirability Bias: The tendency to answer questions in a manner that will be viewed favorably by others.
Example: Individuals may underreport unhealthy behaviors in surveys.
Framing/Wording Effects: The way questions are phrased can influence responses.
Example: Asking if people support "environmental protection" vs. "taxes for environmental regulation."
Representative Sample: A sample that accurately reflects the characteristics of the population.
Example: Randomly selecting participants from different demographics to represent a city.
Meta-Analysis: A statistical analysis that combines the results of multiple studies.
Example: Analyzing various studies on a specific treatment to determine overall effectiveness.
Likert Scale: A rating scale used to measure attitudes or opinions, typically ranging from "strongly agree" to "strongly disagree."
Example: A survey asks participants to rate their satisfaction on a 5-point scale.
Structured Interviews: A research method where questions are predetermined and asked in the same order.
Example: Conducting interviews with job applicants using a set list of questions.
Self-Report Bias: When participants provide inaccurate or misleading information about themselves.
Example: Overestimating hours spent exercising in a self-report survey.
Naturalistic Observation: Observing behavior in its natural environment without interference.
Example: Watching children play in a park to study social interactions.
Case Studies: In-depth exploration of a single individual or group to gain detailed insights.
Example: A detailed report on a patient with a rare psychological disorder.
Correlational Research: Examining the relationship between two variables without manipulation.
Illusory Correlations: Perceived relationships that do not exist.
Example: Believing there’s a link between full moons and increased crime rates without evidence.
Directionality Problem: Uncertainty about which variable influences the other.
Example: Does stress cause sleep problems, or do sleep problems cause stress?
Third Variable Problem: A third factor may influence both variables, creating a false correlation.
Example: Ice cream sales and drowning incidents may both increase in summer due to warm weather.
Informed Consent: Participants must be fully aware of the study's purpose and procedures before agreeing to participate.
Example: A researcher provides a consent form detailing the study and its risks.
Informed Assent: Obtaining agreement from participants who may not be able to fully understand the study (e.g., minors).
Example: A child is given a simplified explanation of a study and agrees to participate.
Confidentiality: Ensuring that participants' data and identities are kept secret.
Example: Research findings are reported without disclosing any personal information.
Deception: Misleading participants about certain aspects of the study, used only when necessary and justified.
Example: Participants might be told a study is about memory when it’s actually about social influence.
Debriefing: Informing participants about the study's true purpose and any deception used after its completion.
Example: After a study, researchers explain the reasons for any deception and answer questions.
Protection from Harm: Ensuring that participants are not placed at risk of physical or psychological harm.
Example: Screening participants for mental health issues before enrolling them in a potentially stressful study.

Statistics in Psychology

Descriptive Statistics: These summarize and describe the features of a dataset.
Example: Calculating the average score of a class on a test.
Inferential Statistics: These allow researchers to draw conclusions and make predictions about a population based on a sample.
Example: Using a sample of voters to predict the outcome of an election.
Statistical Significance: A measure that indicates whether the results of a study are likely due to chance. Typically, a p-value of less than 0.05 is considered significant.
Example: Finding that a new therapy results in lower anxiety scores with a p-value of 0.03 means it's statistically significant.
Probabilities and the Gambler's Fallacy: The gambler's fallacy is the mistaken belief that past events can affect the probabilities of future independent events.
Example: Believing that a coin flip is "due" to land on heads after several tails in a row.
Correlation: A statistical measure that indicates the extent to which two variables fluctuate together.
Example: A positive correlation between hours studied and test scores means more study hours are associated with higher scores.
Causation: Indicates that one variable directly affects another.
Example: A study shows that increasing exercise leads to weight loss.
Positive Correlation: Both variables increase or decrease together.
Example: Height and weight often show a positive correlation.
Negative Correlation: One variable increases while the other decreases.
Example: More time spent on social media might correlate with lower grades.
Zero Correlation: No relationship between the variables.
Example: Shoe size and intelligence typically show zero correlation.
Correlation Coefficients: A numerical value ranging from -1 to +1 that indicates the strength and direction of a correlation.
Example: A correlation coefficient of +0.8 indicates a strong positive relationship.
Scatterplots: Graphs that depict the relationship between two variables, showing data points on a Cartesian plane.
Example: A scatterplot showing test scores vs. hours studied.
Frequency Distributions: A summary of how often each value occurs in a dataset.
Example: A table showing the number of students with each grade on a test.
The Normal Curve: A bell-shaped curve representing the distribution of many types of data, where most values cluster around the mean.
Example: Heights of individuals often form a normal distribution.
Positive Skew: A distribution where the tail extends to the right, indicating that most data points are clustered on the left.
Example: Income distribution often has a positive skew.
Negative Skew: A distribution where the tail extends to the left, indicating that most data points are clustered on the right.
Example: A test where most students scored high but a few scored very low might show a negative skew.
Percentiles: Values below which a certain percentage of data falls.
Example: A score in the 90th percentile means you scored better than 90% of participants.
Variation and the Standard Deviation: Measures of how spread out the numbers in a dataset are.
Example: A low standard deviation means the scores are close to the mean, while a high standard deviation indicates a wide range of scores.
Mean: The average score, calculated by adding all values and dividing by the number of values.
Example: The mean score of a test is calculated as (sum of all scores) / (number of scores).
Median: The middle score in a dataset when arranged in order.
Example: In the set {1, 3, 3, 6, 7, 8, 9}, the median is 6.
Mode: The most frequently occurring score in a dataset.
Example: In the set {1, 2, 2, 3, 4}, the mode is 2.
Bimodal Distribution: A distribution with two modes or peaks.
Example: A dataset where two different groups yield the highest frequencies at two different points.
Regression to the Mean: The tendency for extreme values to return to the average on subsequent measurements.
Example: An exceptionally high test score may be followed by a score closer to the average.
Range: The difference between the highest and lowest values in a dataset.
Example: In the scores {70, 80, 90}, the range is 90 - 70 = 20.