Final Study Guide Flashcards
Correlation vs Causation
- definitions
- examples
- Correlation: Correlation can indicate a potential relationship between variables. It does not establish a cause-and-effect relationship
- Causation: Cause-and-effect relationship between variables
-ex: if a study finds a correlation between ice cream sales and crime rates, it doesn’t mean that eating ice cream causes crime or vice versa. A third factor, such as hot weather, could be driving both trends
Reliability vs Validity
- definitions
- examples
- Reliability refers to the consistency of a measure. A reliable measure will produce similar results across multiple trials or observers
- Accuracy of a measure – whether it is truly measuring what it intends to measure
examples: scale always shows 150 pounds = reliability. Scale always gives correct weight of person = validity
Types of reliability
Types of Reliability:
- Test-retest reliability assesses the consistency of scores over time
-Inter-rater reliability measures the degree of agreement between different raters or observers
-Intra-rater reliability assesses the consistency of ratings by the same rater across multiple trials or occasions.
Types of Validity:
Types of Validity:
- Construct validity: assesses whether a measure accurately represents the underlying construct or concept it is intended to measure.
-Internal validity: refers to the extent to which a study can establish a cause-and-effect relationship between variables, minimizing the influence of confounding variables.
- External validity: refers to the extent to which the findings of a study can be generalized to other populations or settings.
Types of Research Designs: Observational Studies - Researchers observe and collect data without manipulating any variables
- examples
- Cross-sectional studies: Data is collected at a single point in time.
- Longitudinal studies: Data is collected over an extended period
- Stratified longitudinal studies: Participants are divided into subgroups (strata) based on shared characteristics, and data is collected over time.
Types of Research Designs: Experimental Studies - Researchers manipulate an independent variable to observe its effect on a dependent variable
- examples
- Randomized controlled trials (RCTs): Participants are randomly assigned to either an experimental group (receives the intervention) or a control group.
- Quasi-experimental studies: Like RCTs but without random assignment of participants.
- Pre-experimental studies: These designs lack a control group or may have other limitations that weaken the strength of the evidence
Types of Research Designs: Qualitative Research Approaches - understand experiences, perspectives, and meanings
- examples
Case studies: In-depth examination of a particular individual, group, or situation.
Ethnographies: Focus on understanding cultures or cultural groups.
Phenomenological studies: Explore the lived experiences of individuals related to a particular phenomenon.
Sampling methods
- examples
Target Population: The entire group of individuals to which the study wants to generalize the results.
Accessible Population: The portion of the target population that is accessible to the researcher.
Sampling Bias: Occurs when the sample chosen for the study is not representative of the target population, which can lead to inaccurate conclusions
Hypothesis Testing: The process of evaluating evidence to determine whether to reject or fail to reject a null hypothesis
- examples
Null Hypothesis (H0): The hypothesis that there is no significant difference or relationship between variables
Alternative Hypothesis (Ha): The hypothesis that there is a significant difference or relationship between variables
Type I Error: Rejecting a true null hypothesis (false positive) - fire alarm goes off, no fire
Type II Error: Failing to reject a false null hypothesis (false negative) - fire and don’t pull alarm
P-value: The probability of obtaining the observed results if the null hypothesis is true. A p-value below a predetermined threshold (e.g., 0.05) typically leads to the rejection of the null hypothesis
Data Analysis: Different approaches are used for analyzing qualitative and quantitative data.
Quantitative Data Analysis: Uses statistical methods to analyze numerical data. The goal of data representation is to present findings clearly and concisely using tables, graphs, and figures
Qualitative Data Analysis: Involves interpreting non-numerical data, such as text, images, or audio recordings. Coding is a key step where researchers assign labels to segments of data to categorize and group them thematically.
Tri-Council Policy Statement (TCPS 2): The TCPS 2 provides guidelines for ethical research involving humans in Canada. It is based on three core principles
Respect for Persons: Recognizing the inherent dignity and worth of all individuals, respecting their autonomy, and protecting those with diminished autonomy
Concern for Welfare: Protecting the well-being of participants, minimizing risks, and maximizing benefits
Justice: Ensuring fairness and equity in the distribution of benefits and burdens of research.
Privacy and Confidentiality: Protecting participants’ privacy and confidentiality is crucial
Privacy: Refers to an individual’s right to control access to their personal information and to be free from intrusion
Confidentiality: Refers to the researcher’s obligation to protect information entrusted by participants.
Informed Consent: Participants must provide informed consent before participating in research
Providing clear and comprehensive information about the study, including its purpose, procedures, risks, and benefits
Ensuring that participants understand the information and can make a voluntary decision about whether to participate
Obtaining written documentation of consent
Epistemology and Ontology
Epistemology: The study of knowledge and justified belief. It explores questions about how we acquire knowledge, what constitutes valid knowledge, and how we can justify our beliefs
Ontology: The study of being and the nature of reality. It explores questions about what exists, what the basic categories of being are, and the relationships between them.
Research Paradigms: Different paradigms reflect different assumptions about the nature of reality and how knowledge can be acquired
Positivism: Emphasizes objective, measurable observations and seeks to establish universal laws and principles. It relies heavily on quantitative methods and hypothesis testing
Interpretivism: Focuses on understanding the subjective experiences and meanings individuals attach to their world. It favors qualitative methods and inductive reasoning
Pragmatism: Emphasizes the practical implications and usefulness of research findings. Pragmatists are often open to using both quantitative and qualitative methods, depending on the research question
Inductive and Deductive Reasoning
Deductive Reasoning: Starts with general principles or premises and uses logical steps to arrive at specific conclusions.
Inductive Reasoning: Starts with specific observations or experiences and draws general conclusions.
Worldviews: A researcher’s worldview can influence their research approach and interpretation of findings
Positivism: Seeks objective truths through empirical observation and measurement
Constructivism: Believes knowledge is constructed through individual and social interactions
Pragmatism: Focuses on the practical implications and consequences of research findings
Transformative Paradigms: Seek to address social justice issues and promote positive social change
Two-Eyed Seeing: Emphasizes the integration of Indigenous and Western knowledge systems.
Top-Down and Bottom-Up Processing: These two processes constantly interact in visual perception.
Bottom-Up Processing: Processing information from the senses upward to the brain, analyzing basic features before constructing a complete perception. For example, recognizing a letter ‘A’ by first processing its individual lines and angles
Top-Down Processing: Using prior knowledge, expectations, and context to interpret incoming sensory information. For example, reading a partially obscured word by using the context of the sentence to fill in the missing letters.
Cognitive Biases: Cognitive biases are systematic errors in thinking that can arise from our brain’s attempts to simplify information processing. They can influence how we interpret information, make decisions, and even design and conduct research
Confirmation Bias: The tendency to favor information that confirms our pre- existing beliefs
Motivated Reasoning: The tendency to process information in a way that protects our self-esteem or reinforces desired beliefs.
The “Replication Crisis”
Refers to the challenge of replicating the findings of previously published research. This crisis highlights the importance of rigorous research methods, transparency in reporting, and the need to consider potential biases and limitations in research.
Appeals to Authority: While expert opinions can be valuable, relying solely on authority figures without considering the evidence is a fallacy. Consider:
The relevance of the authority’s expertise to the topic being discussed. For example, a celebrity endorsing a weight loss product may not have the relevant scientific expertise
Whether the authority’s claims are supported by evidence.
Secondary sources
Secondary sources interpret, analyze, or summarize
information from primary sources. Examples include review articles, textbooks, and news reports. When relying on secondary sources, consider the author’s expertise, potential biases, and the accuracy of their interpretation of the primary sources.
Peer review sources
A quality control mechanism in academic publishing. Submitted manuscripts are reviewed by experts in the field to assess the rigor of the research, the validity of the findings, and the significance of the contribution. The peer review process helps to ensure the quality of published research, but it is not infallible and can sometimes be subject to biases.
Confounding Variable:
An extraneous variable that correlates with both the
independent and dependent variables, potentially distorting the relationship
between them
Double-Blind Study:
Neither the researcher nor the participant knows which treatment the participant is receiving. This helps to reduce bias in research.
Knowledge Translation:
The process of moving research findings into practice to improve health outcomes.
Meta-Analysis:
A statistical technique that combines the results of multiple studies to obtain a more comprehensive understanding of a phenomenon.
Member Checking:
A technique used in qualitative research to ensure that the researcher’s interpretations accurately reflect the participants’ experiences and perspectives.
P-hacking:
Manipulating data analysis procedures to achieve a desired p-value, often leading to false positive results.
Systematic Review:
A comprehensive and rigorous review of existing literature on a specific research question.
Triangulation:
Using multiple data sources or methods to enhance the credibility and trustworthiness of qualitative research findings
