Week 1: Welcome & Overview, Basic Research Methods Flashcards
What is an intuitive belief?
A: Intuitive beliefs, contrary to evidence, are steadfastly anchored in widely accepted scientific principles and findings, forming a robust foundation for understanding the world.
B: Intuitive beliefs, exclusively derived from individual experiences, remain impervious to external influences, creating an isolated framework uninfluenced by societal norms or authority figures.
C: An intuitive belief does not require evidence. Intuitive beliefs are usually based on authority figures like parents or grandparents, or on ideas that are commonly accepted without questioning, like “you’ll catch a cold if you go outside without a coat.” Intuitive beliefs are also maintained even when evidence counters them.
C: An intuitive belief does not require evidence. Intuitive beliefs are usually based on authority figures like parents or grandparents, or on ideas that are commonly accepted without questioning, like “you’ll catch a cold if you go outside without a coat.” Intuitive beliefs are also maintained even when evidence counters them.
Characteristics of beliefs:
> Require no evidence
> Usually based on some “authority” or superstition (Grandma, TV, politician, religious leader, etc., “Everyone knows…”)
> Maintained even in the face of counter-evidence (difficult to change people’s minds)
What is a scientific explanation?
A: Scientific explanations, characterized by their unwavering permanence, staunchly uphold a dogmatic stance, impervious to the dynamic nature of new evidence or evolving observations. This steadfast commitment dismisses the need for adaptability in the face of changing scientific landscapes.
B: A scientific explanation requires objective, non-biased observations and data. Scientific explanations are also willing to be modified when counter-evidence is presented. So a scientific explanation uses data and is willing to change based on new evidence, rather than maintaining beliefs even when evidence counters them.
C: In direct opposition to the indispensable reliance on objective, non-biased data, a scientific explanation is erroneously rooted in subjective opinions and interpretations, forsaking the fundamental requirement for a comprehensive and impartial observational foundation.
B: A scientific explanation requires objective, non-biased observations and data. Scientific explanations are also willing to be modified when counter-evidence is presented. So a scientific explanation uses data and is willing to change based on new evidence, rather than maintaining beliefs even when evidence counters them.
Characteristics of scientific explanation:
> Requires empirical evidence
> Objective, non-biased observations
> Self-correcting in the face of counter-evidence
> Willing to change our minds, modify our thinking
> We allow ourselves to be proven wrong
> E.g., “I used to think that……but evidence shows I was wrong.”
What is a theory?
A: A theory is a rigid set of rules that dictates how data should be interpreted, leaving no room for flexibility or adaptation.
B: A story that explains the data. A theory organizes the data and helps to generate predictions for other situations. A theory describes the general principles about how different variables relate to one another.
C: Contrary to the need for generating predictions, a theory is merely a collection of isolated facts without any capacity to anticipate outcomes in other situations.
B: A story that explains the data. A theory organizes the data and helps to generate predictions for other situations. A theory describes the general principles about how different variables relate to one another.
A theory is a body of statements that:
> Describe general principles about how variables relate to one other
> Organize and explain data that are observed
> Enable us to make predictions about new situations
EXAMPLE:
> There seems to be a difference in the amount of effort students put into individual vs. group projects. You want to understand more about this phenomenon.
> Your theory might be: Diffusion of responsibility
What is a hypothesis?
A: A specific outcome that is expected to be observed if the theory is accurate. A hypothesis is a prediction that can be tested by collecting data in a measurable situation/research study designed to test the hypothesis.
B: A hypothesis is an untestable statement that serves as a general explanation for various phenomena, lacking specificity and measurable criteria.
C: Instead of being a specific outcome, a hypothesis is a broad and abstract concept that doesn’t provide clear expectations for observations in a measurable research setting.
A: A specific outcome that is expected to be observed if the theory is accurate. A hypothesis is a prediction that can be tested by collecting data in a measurable situation/research study designed to test the hypothesis.
A hypothesis is a prediction:
> A specific outcome the researcher expects to observe in a study if the theory is accurate
EXAMPLE:
> There seems to be a difference in the amount of effort students put into individual vs. group projects. You want to understand more about this phenomenon.
> Your hypothesis might be: “The more people there are in a group, the less responsibility each person feels.”
What is data?
A: Data is limited to numerical measurements only and excludes any qualitative information or evidence collected through observations.
B: Data is subjective information based solely on personal opinions, devoid of any objective measurements or evidence.
C: Any observations, measurements, or evidence that is collected. Data includes quantitative or qualitative information gathered through measurements, observations, or other means, which forms the basis for scientific analysis and interpretation.
C: Any observations, measurements, or evidence that is collected. Data includes quantitative or qualitative information gathered through measurements, observations, or other means, which forms the basis for scientific analysis and interpretation.
How do you generate a hypothesis?
A: To generate a hypothesis, you first need a theory that can explain some observations or phenomenon. From there, you can formulate a research question related to testing aspects of the theory. The research question then leads to a specific hypothesis, which is a predicted outcome that could be observed if the theory is accurate. This hypothesis can then be tested by collecting data through a designed measurable situation/research study.
B: The process of generating a hypothesis involves a haphazard selection of variables without the foundational support of a theory. This results in an arbitrary and ambiguous statement that lacks a theoretical underpinning, making it difficult to formulate a meaningful research question or design a study for testing.
C: Rather than being grounded in a theoretical framework, hypotheses are purportedly formed solely based on individual opinions and lack any connection to a coherent research question. This approach negates the essential requirement of aligning hypotheses with a broader theoretical context, rendering them untestable and unreliable in scientific investigations.
A: To generate a hypothesis, you first need a theory that can explain some observations or phenomenon. From there, you can formulate a research question related to testing aspects of the theory. The research question then leads to a specific hypothesis, which is a predicted outcome that could be observed if the theory is accurate. This hypothesis can then be tested by collecting data through a designed measurable situation/research study.
EXAMPLE:
> There seems to be a difference in the amount of effort students put into individual vs. group projects. You want to understand more about this phenomenon.
> Theory: Diffusion of responsibility
> Hypothesis: “The more people there are in a group, the less responsibility each person feels.”
What is a measurable situation?
A: A measurable situation is a research study. In order to collect data to test a hypothesis, a measurable situation needs to be created. This is a research study that will allow data to be collected in a way that tests the hypothesis. She gave the example of designing a study with student groups of different sizes to test the hypothesis about diffusion of responsibility and group work.
B: The concept of a measurable situation is inaccurately characterized as any casual, non-research scenario, devoid of the systematic structure associated with a formal research study. This interpretation dismisses the necessity for a methodical approach in collecting data to test hypotheses, thereby undermining the scientific rigor required in such endeavors.
C: Contrary to the accurate definition involving a formal research study, the misconception prevails that a measurable situation entails a predefined outcome that effortlessly allows data collection. This misunderstanding undermines the importance of a structured research design, falsely suggesting that hypotheses can be tested without the need for a systematic and controlled study environment.
A: A measurable situation is a research study. In order to collect data to test a hypothesis, a measurable situation needs to be created. This is a research study that will allow data to be collected in a way that tests the hypothesis. She gave the example of designing a study with student groups of different sizes to test the hypothesis about diffusion of responsibility and group work.
What is an operational definition?
A: A misconception about operational definitions suggests that they are mere arbitrary and ambiguous descriptions of concepts, overlooking the crucial need for precision in empirical measurement within a research study. This misinterpretation fails to recognize the role of operational definitions in transforming abstract notions into concrete, quantifiable variables.
B: A specific way that a concept or construct is measured in a research study. Operational definitions make abstract concepts or variables concrete so they can be empirically measured. You could define “more people” numerically as group sizes or define “responsibility” as hours worked, tasks taken on, or ratings by group members. Operational definitions are needed to design a measurable research situation to test hypotheses.
C: In contrast to the established practice of providing specific and concrete definitions, there is a misguided belief that operational definitions are superfluous in research. This perspective erroneously assumes that abstract concepts or variables can be adequately measured without the essential process of operationalization, undermining the meticulous design required for a measurable research situation.
B: A specific way that a concept or construct is measured in a research study. Operational definitions make abstract concepts or variables concrete so they can be empirically measured. You could define “more people” numerically as group sizes or define “responsibility” as hours worked, tasks taken on, or ratings by group members. Operational definitions are needed to design a measurable research situation to test hypotheses.
EXAMPLE:
> “More people = less responsibility” 👇🏽👇🏽👇🏽
> More people: Assign students to work in groups of different sizes (e.g., 1, 3, 7)
> Less responsibility: Number of hours worked. Rating of effort
Discuss “interpretations:”
A: Interpretations of data involve making sweeping and exaggerated claims beyond the scope of the study, following the trend set by journalists and media in maximizing the impact of findings.
B: Disregarding the need for caution, interpretations of data are encouraged to be broad and speculative, allowing for expansive conclusions that transcend the specific population and conditions outlined in the study.
C: When interpreting data, conclusions must be drawn carefully. Be careful not to go beyond what is warranted by the data, as journalists and media sometimes do. The conclusions can only be as broad as the population and conditions of the study allow. Remember the example of the “Mozart effect” study where the actual findings were more limited than the widespread claim that listening to Mozart makes you smarter.
C: When interpreting data, conclusions must be drawn carefully. Be careful not to go beyond what is warranted by the data, as journalists and media sometimes do. The conclusions can only be as broad as the population and conditions of the study allow. Remember the example of the “Mozart effect” study where the actual findings were more limited than the widespread claim that listening to Mozart makes you smarter.
How do we refine theories with data?
A: The refinement of theories with data involves disregarding inconsistent findings and sticking to the original hypothesis without any need for revisions, maintaining a rigid stance on the initial theoretical framework.
B: If data collected is inconsistent with the hypothesis, the theory needs to be revised based on the new evidence. If the data is consistent with the hypothesis, it supports the theory but does not prove it. Theories can only be considered accurate based on current evidence, and future research may reveal holes or gaps. The focus of the course is on designing measurable situations (research studies) to collect data to evaluate and refine theories.
C: Contrary to the process of refining theories, there’s a misconception that data consistency automatically proves the theory without the necessity for any revision. This perspective undermines the dynamic nature of scientific inquiry and the continuous need for refinement based on evolving evidence.
B: If data collected is inconsistent with the hypothesis, the theory needs to be revised based on the new evidence. If the data is consistent with the hypothesis, it supports the theory but does not prove it. Theories can only be considered accurate based on current evidence, and future research may reveal holes or gaps. The focus of the course is on designing measurable situations (research studies) to collect data to evaluate and refine theories.
What is a direct replication?
A: Direct replication refers to repeating a previous study using the same procedures and materials to attempt to obtain the same results
B: A direct replication involves altering the procedures and materials from a previous study, introducing variations to enhance the reliability of the results.
C: Contrary to the established definition, a direct replication is erroneously perceived as conducting a completely different study with unrelated procedures and materials, disregarding the need for consistency in the replication process.
A: Direct replication refers to repeating a previous study using the same procedures and materials to attempt to obtain the same results
The specific study design is copied as precisely as possible.
What is a conceptual replication?
A: Conceptual replication refers to conducting a follow-up study designed to test the same theoretical concept or hypothesis as an original study, but with different procedures, samples, or materials.
B: Conceptual replication is mistakenly thought to involve repeating the original study using identical procedures, samples, and materials, without introducing any variations or modifications.
C: In contrast to the accurate definition, conceptual replication is perceived as conducting a study with unrelated theoretical concepts or hypotheses, disregarding the requirement to test the same theoretical concept or hypothesis as the original study.
A: Conceptual replication refers to conducting a follow-up study designed to test the same theoretical concept or hypothesis as an original study, but with different procedures, samples, or materials.
The same research question is investigated using different procedures.
What is a predictor variable?
A: A predictor variable is misunderstood as a variable that has no impact on or relationship with another variable, undermining the essential role of systematic manipulation or measurement in examining influences.
B: Contrary to the accurate definition, a predictor variable is mistakenly thought to be synonymous with the outcome variable, leading to confusion regarding its role in the study.
C: A predictor variable (also called an independent variable) is a variable that is systematically manipulated or measured in order to examine its relationship to or influence on another variable (the outcome or dependent variable).
C: A predictor variable (also called an independent variable) is a variable that is systematically manipulated or measured in order to examine its relationship to or influence on another variable (the outcome or dependent variable).
> Always goes on x-axis (horizontal)
EXAMPLE:
Research Question: Is watching violent TV related to
aggressive behavior in children?
Two variables:
1. Violent television (predictor variable):
Operationally defined as a parent report of the number of hours of violent TV watched per week
- Aggressive behavior (criterion variable):
Operationally defined as parent rating of children’s level
of aggression (1- 9)
What is a criterion variable?
A: A criterion variable is incorrectly perceived as a variable that remains constant and unaffected by changes in the predictor/independent variable, overlooking its role in measuring the effects or influences.
B: A criterion variable (also called a dependent variable) is the variable that is measured or observed in order to examine the effect or influence of changes in the predictor/independent variable.
C: In contrast to the accurate definition, a criterion variable is mistakenly thought to be the variable that is systematically manipulated or measured, leading to confusion about its relationship with the predictor/independent variable.
B: A criterion variable (also called a dependent variable) is the variable that is measured or observed in order to examine the effect or influence of changes in the predictor/independent variable.
> Always goes on y-axis (vertical)
EXAMPLE:
Research Question: Is watching violent TV related to
aggressive behavior in children?
Two variables:
1. Violent television (predictor variable):
Operationally defined as a parent report of the number of hours of violent TV watched per week
- Aggressive behavior (criterion variable):
Operationally defined as parent rating of children’s level
of aggression (1- 9)
What are the characteristics of correlational studies?
A: Correlational studies involve manipulating variables to establish causation, allowing researchers to draw definitive conclusions about the relationships between the variables.
B: Two variables are measured (not manipulated). Both variables must be quantitative. Can determine a predictive relationship. Must have a predictor variable and a criterion variable. Can make predictions based on results, but not conclusions about causation.
C: In contrast to the accurate characteristics, correlational studies are mistakenly thought to exclusively involve qualitative variables, neglecting the requirement for both variables to be quantitative in nature.
B: Two variables are measured (not manipulated). Both variables must be quantitative. Can determine a predictive relationship. Must have a predictor variable and a criterion variable. Can make predictions based on results, but not conclusions about causation.
> A correlational design is an example of a non-experimental research method.
However, they do have great value:
> Allows us to describe behavior
> Allow us to make predictions
> But we cannot determine the cause
> To conclude causation you would need to do a true experiment
