Effect size is a measure of the strength of the relationship between variables in a study. It helps to understand the practical significance of the research findings.

A sampling frame is a list or database from which a sample is drawn. It should accurately represent the population being studied to ensure valid results.

Operationalizations refer to how abstract concepts are defined and measured in a study. It details the specific procedures or instruments used to measure variables.

The H index measures the productivity and citation impact of the publications of a researcher or journal. A higher H index indicates a higher number of influential articles.

A conflict of interest occurs when a researcher's personal, financial, or professional interests could influence their research outcomes. It's important to disclose these to maintain credibility and trustworthiness.

Peer review is a process where experts in the field evaluate a research paper before it is published. It ensures the research is credible, accurate, and of high quality.

A meta-analysis is a type of systematic review that combines the statistical results from multiple studies to draw broader conclusions. It provides a more powerful analysis by aggregating data from many sources.

Systematic reviews summarize the results of multiple empirical studies on a specific topic. They help to identify overall patterns and provide a comprehensive understanding of the research area.

Empirical studies are research studies that collect and analyze data to answer specific questions or test hypotheses. They are based on observed or measured phenomena.

A literature review is a comprehensive survey of existing research and studies on a particular topic. It helps to understand what has already been discovered and identifies gaps or areas for further research.

Intersectionality is the concept that various forms of social stratification, such as race, gender, and class, do not exist separately but are interwoven.

Essentialism is the belief that certain categories (e.g., gender, race) have an inherent, unchangeable essence.

Cronbach’s alpha is a statistic used to assess the internal consistency of a set of items, indicating how well they measure the same concept.

Intercoder reliability measures the extent to which different coders or observers produce consistent results when analyzing the same data.

The split-half method involves dividing a set of measures into two halves and comparing the results to assess reliability.

The test-retest method involves measuring the same individuals at two different points in time to assess the consistency of their responses.

Reliability refers to the consistency of a measure; a reliable measure produces stable and consistent results over time.

Content validity evaluates whether a measure covers the entire range of meanings within a concept.

Criterion-related validity assesses whether a measure is related to an outcome it should theoretically be related to.

Face validity refers to whether a measure appears to logically reflect the concept it is intended to measure.

Values are the numerical properties assigned to variables for statistical analysis.

Attributes are the different qualities or characteristics that make up a variable.

Variables are concepts that can take on different values in a study, as opposed to constants which do not vary.

Dimensions are aspects of a concept that group together logically, like subcategories within the overall concept.

Indicators are specific observations that show the presence or absence of a concept.

Operational definitions specify the exact procedures or measures used to observe or quantify a concept.

A conceptual definition is a theoretical explanation of what a concept means, similar to a dictionary definition.

Conceptualization involves defining the key terms and concepts used in a study to ensure clarity and shared understanding.

Code switching is the practice of alternating between different languages or dialects depending on the social context.

Otherness is a concept that describes how dominant groups marginalize non-dominant groups, making them feel excluded or diminished.

Operationalization refers to the process of defining and measuring concepts in a way that they can be empirically observed and tested.

Deficit-based thinking focuses on what marginalized groups lack or are deficient in, often blaming them for their circumstances. Asset-based thinking, on the other hand, focuses on their strengths and resources.

Cultural humility involves recognizing and respecting the cultural differences and perspectives of research participants. It means being open to learning from them and acknowledging your own limitations.

Process consent is the ongoing practice of checking in with participants throughout the research to ensure they still consent to participate and are not experiencing harm or stress.

These are the three core ethical principles from the Belmont Report. Respect means treating participants as autonomous agents, justice involves fairness in distribution of research benefits and burdens, and beneficence means minimizing harm and maximizing benefits.

The Belmont Report is a key document in the history of research ethics. It outlines ethical principles and guidelines for research involving human subjects, focusing on respect, justice, and beneficence.

IRBs are committees that review research proposals to ensure that they are ethical and that the rights and welfare of participants are protected. They are crucial for maintaining ethical standards in research.

The Hippocratic Oath is an ethical code attributed to the ancient Greek physician Hippocrates. It is still used today by medical professionals to swear to practice medicine ethically and honestly.

Satyagraha is a form of nonviolent resistance or civil disobedience. It was developed by Mahatma Gandhi as a way to fight injustice without using violence.

Ahimsa and satya are important ethical principles from Eastern philosophies. Ahimsa means non-violence, and satya means truth. These principles guide ethical behavior and decision-making.

Reflexivity is the practice of reflecting on your own values, biases, and influence on the research process. It's about being aware of how your background and perspective might shape your research.

The principle of objectivity in research aims to minimize the influence of the researcher's personal biases and ensure that findings are based on observable and replicable data.

Aggregation of numerical data points means combining individual data points to form a summary, such as an average or frequency, to identify patterns in the data.

Deductive reasoning involves starting with a general theory or hypothesis and then collecting data to test it. It contrasts with inductive reasoning, which starts with specific observations to develop broader generalizations.

The critical paradigm focuses on examining and challenging power structures and inequalities in society. It aims to use research as a tool for social change and justice.

Constructivism is a research paradigm that believes in multiple realities shaped by individual experiences and interpretations. Researchers are seen as active participants in the research process.

Positivism is a research paradigm that assumes a single, objective reality that can be discovered through scientific methods. It often involves generating and testing hypotheses.

Community-based research involves collaborating with community members to address local issues. It can use quantitative, qualitative, or mixed methods.

Mixed methods research combines both quantitative and qualitative approaches in a single study to provide a more comprehensive understanding of a research problem.

Qualitative research focuses on understanding social phenomena through subjective experiences, stories, and meanings. It often involves methods like interviews and ethnography.

Quantitative research focuses on numerical data and statistical analysis to understand phenomena. It often involves measuring variables and testing hypotheses.

External validity is about whether the results of an experiment can be applied to real-world settings outside the lab.

Internal validity is about ensuring that the results of an experiment are due to the manipulation of the independent variable and not other factors.

Debriefing involves explaining the purpose of the study to participants after it is completed, especially if any deception was used.

The Hawthorne effect occurs when people change their behavior simply because they know they are being observed.

Field experiments are conducted in real-world settings, making their findings more applicable to everyday life.

Pre-experiments are simpler and often used as a preliminary step. They don't have control groups or random assignment, which limits their ability to establish causation.

Quasi-experiments do not use random assignment, which can make it harder to rule out other explanations for the results.

Random assignment ensures that each participant has an equal chance of being placed in any group, which helps eliminate bias.

True experiments are done in controlled environments like labs to ensure that the results are due to the manipulated variables and not other factors.

Non-spurious relationships mean that the relationship between the variables is genuine and not caused by some other variable.

Time order means that the cause (independent variable) must occur before the effect (dependent variable). This helps establish that one variable is actually causing the other.

Association means that there is a relationship between the independent and dependent variables. They should change together in a way that is not due to chance.

A confounding variable is an extra variable that you didn't account for. It can ruin an experiment and give you useless results.

Control variables are factors that researchers keep constant to ensure that any changes in the dependent variable are due to the independent variable and not some other factor.

Dependent variables are what researchers measure in an experiment to see if they change due to the manipulation of the independent variable.

Independent variables are the factors that researchers change or control in an experiment to see how they affect the outcome.

Jane Elliot's experiment is a classic example of how social experiments can highlight the impact of discrimination. It shows how quickly and deeply people can be affected by arbitrary distinctions.

Experiments are crucial in scientific research because they allow us to test hypotheses and observe outcomes in a controlled environment.

External validity is about whether the results of an experiment can be applied to real-world settings outside the lab.

Internal validity refers to how well an experiment is conducted and whether it truly shows that the independent variable caused changes in the dependent variable.

Debriefing is when researchers explain the purpose of the study to participants after it is completed, especially if any deception was used.

Double-blind designs mean that neither the participants nor the experimenters know who is in the control or experimental group, reducing bias.

The Hawthorne effect occurs when participants change their behavior simply because they know they are being observed.

Field experiments are conducted in real-world settings, which makes them more applicable to everyday life but harder to control.

Pre-experiments are the simplest form of experiments and often lack control groups and random assignment, making them less reliable for establishing causality.

Quasi-experiments are similar to true experiments but lack random assignment, making them less rigorous but still useful in real-world settings.

Random assignment ensures that each participant has an equal chance of being placed in any group, which helps eliminate bias.

True experiments are the most rigorous type of experiment because they include random assignment and control groups to establish causality.

Non-spurious relationships mean that the observed relationship is not due to some other variable that wasn't accounted for.

Time order means that the cause (independent variable) must occur before the effect (dependent variable).

Association means that there must be a noticeable relationship between the independent and dependent variables.

Control variables are kept constant to prevent them from influencing the outcome, while confounding variables are those that might interfere with the results if not controlled.

Dependent variables are what researchers measure to see if they change as a result of manipulating the independent variable.

Independent variables are the ones that researchers change or control to see how they affect other variables.

Experiments are considered the best way to determine if one variable directly affects another because they allow for controlled testing.

A research hypothesis is a clear and testable statement predicting how two or more variables are related.

Basic research aims to expand our understanding of fundamental principles, while applied research seeks to address real-world issues using that knowledge.

Bandura's Bobo doll experiment showed that children can learn aggressive behaviors through imitation, which is important for understanding social learning.

Jane Elliot's experiment was designed to teach children about the effects of discrimination by treating them differently based on eye color.

Pavlov's experiments demonstrated how animals (and humans) can learn to associate a neutral stimulus (like a bell) with a significant one (like food).

Archimedes' water displacement experiment is a classic example of using observation and experimentation to discover scientific principles.

This means that experiments are essential for understanding social phenomena because they help us establish cause-and-effect relationships.

Experiments are a key part of scientific research because they allow us to test hypotheses and observe outcomes in a controlled way.

Debriefing is the process of explaining the purpose and methods of the experiment to participants after it is completed, especially if deception was used.

Random assignment ensures that participants have an equal chance of being placed in any group, which helps eliminate bias and confounding variables.

Field experiments are conducted in real-world settings, offering high external validity but less control over extraneous variables compared to lab experiments.

Pre-experiments are the simplest form of experiments, often lacking control groups and random assignment, making them less reliable for establishing causation.

Quasi-experiments are similar to true experiments but lack random assignment, making them less rigorous but still useful in real-world settings.

True experiments are the most rigorous type of experiment, involving random assignment and control groups to establish clear cause-and-effect relationships.

Non-spurious relationships mean that the observed relationship between variables is not due to some other external factor.

Time order means that the cause (independent variable) must occur before the effect (dependent variable) to establish a causal relationship.

Association means that there must be a noticeable relationship between the independent and dependent variables for causation to be considered.

Control variables are factors that researchers keep constant to prevent them from affecting the outcome, while confounding variables are those that might interfere with the results if not controlled.

Dependent variables are what researchers measure in an experiment to see if they change due to the manipulation of the independent variable.

Independent variables are the ones that researchers change or control to see how they affect other variables.

Experiments are highly valued in research because they can clearly show whether one variable causes changes in another, which is crucial for understanding relationships.

A research hypothesis is a clear, testable statement predicting how variables are related. It's what you aim to test in an experiment.

Basic research aims to expand our understanding of fundamental principles, while applied research seeks to address specific, practical issues using that knowledge.

Quota sampling involves selecting participants to match the proportions of certain characteristics in the population. It's not random but aims to approximate the population's demographics.

Snowball sampling involves participants referring others to the study. It's useful for reaching hidden or hard-to-find populations.

Convenience sampling involves selecting participants who are easily accessible. It's often used in early research stages and experiments but can introduce bias.

Systematic error, such as sampling bias, occurs when certain groups are overrepresented or underrepresented in a sample. This type of error is problematic and should be avoided.

Margin of error indicates the range within which the true value lies, considering the sample size and variability. A smaller margin of error means more precise results.

A confidence interval gives a range within which we can be fairly certain the true population parameter lies, based on our sample data.

Standard error measures the accuracy with which a sample represents a population. It's used to calculate confidence intervals.

Standard deviation measures the amount of variation or dispersion in a set of values. It helps us understand the spread of data points around the mean.

Sampling error is the discrepancy between the sample result and the true population value. It's an inevitable part of sampling.

Weighting adjusts the data to give more importance to underrepresented groups, making the sample more reflective of the actual population.

Oversampling involves intentionally selecting more participants from minoritized groups to ensure they are adequately represented in the study.

Minoritized groups are those that are marginalized or discriminated against, often having less access to power and resources in society.

Randomness in sampling ensures that every individual in the population has an equal chance of being selected, which helps to avoid bias.

Internal validity refers to the extent to which a study is methodologically sound and free from biases or errors within the study itself.

External validity refers to how well the results of a study can be applied to contexts outside of the study itself.

A statistic is a value that describes a characteristic of a sample, such as the average income of households in your sample.

A parameter is a value that describes a characteristic of an entire population, such as the average income of all households in a state.

A sampling frame is a list or database from which a sample is drawn. It should include all members of the target population.

Generalizability refers to the extent to which findings from a study can be applied to a larger population. In quantitative research, this is a key goal.

Quota sampling involves selecting participants to match the proportions of certain characteristics in the population, approximating probability sampling without random selection.

Snowball sampling involves participants referring others to the study, useful for reaching hidden or hard-to-find populations.

Purposive sampling involves selecting participants based on specific characteristics or qualities, making it more targeted than convenience sampling.

Convenience sampling involves selecting participants who are easily accessible, often used in early research stages or experiments.

Multi-stage cluster sampling involves multiple levels of random selection, such as selecting states, then cities, then schools, and finally students within those schools.

Stratified random sampling involves dividing the population into subgroups (strata) and randomly sampling from each, ensuring representation of key subpopulations.

Systematic random sampling involves selecting every nth individual from a list, ensuring a systematic approach to random sampling.

Simple random sampling ensures that every possible sample has an equal chance of being selected, similar to drawing names from a hat.

Systematic error, such as sampling bias, occurs when certain groups are overrepresented or underrepresented, leading to inaccurate results.

The margin of error indicates the range within which the true value lies, accounting for potential inaccuracies in the sample data.

A confidence interval provides a range within which we can be fairly certain the true population parameter lies, often expressed with a confidence level like 95%.

Standard error measures the variability or uncertainty of a sample statistic compared to the true population parameter, helping us understand how much the sample mean might vary.

The central limit theorem states that the distribution of sample means will approximate a normal distribution, regardless of the population's original distribution, given a large enough sample size.

A probability distribution shows the likelihood of different outcomes, such as the chances of rolling a specific number on a die.

Probability sampling involves random selection, ensuring that every individual has an equal chance of being included, which helps in achieving a representative sample.

Sampling error is the discrepancy between the sample results and the actual population values, which is inevitable when not surveying the entire population.

Weighting adjusts the data to give more importance to responses from underrepresented groups, making the sample more representative of the population.

Oversampling involves intentionally selecting more participants from minoritized groups to ensure they are adequately represented in the study.

Minoritized groups are those that are marginalized or discriminated against, often having less access to resources and power.

Random sampling ensures that every individual in the population has an equal chance of being included in the sample, reducing bias.

Internal validity refers to the extent to which a study is methodologically sound and free from biases or errors within the study itself.

External validity refers to how well the results of a study can be applied to contexts outside of the study itself.

Representativeness means that the sample accurately reflects the characteristics of the population from which it was drawn.

A statistic is a value that describes a characteristic of a sample, such as the average income of households in your sample.

A parameter is a value that describes a characteristic of an entire population, such as the average income of all households in a state.

A sampling frame is essentially a list or database from which a sample is drawn. It should include all members of the target population.

The target population is the specific group of people you are interested in studying and making conclusions about.

Generalizability refers to the extent to which the results of a study can be applied to or inform a larger population beyond the sample studied.

Inference in this context means making an educated guess or conclusion about a larger group based on a smaller sample.

Reflexivity involves researchers being aware of their own biases, values, and cultural background and how these might affect their research. It requires ongoing self-reflection to ensure that the research is conducted ethically and respectfully.

An Institutional Review Board (IRB) is a committee that reviews research proposals to ensure that they are ethical and that the rights and welfare of participants are protected. Researchers must get IRB approval before starting their studies.

Debriefing is a process that occurs after a research study, especially if deception was used. It involves explaining the true purpose of the study to the participants and ensuring they leave without any negative feelings or misunderstandings.

Anonymity means that participants' identities are completely unknown, even to the researchers. Confidentiality means that the researchers know who the participants are but take steps to protect their identities from being disclosed to others.

Vulnerable populations are groups of people who may not be able to fully protect their own interests in a research setting. This can include children, prisoners, and individuals with cognitive impairments. Special ethical considerations are needed when involving these groups in research.

Informed consent is the process of providing potential research participants with all the information they need to make an informed decision about whether to participate in a study. This includes details about the study's purpose, procedures, risks, and benefits.

Justice in research ethics means ensuring that the benefits and burdens of research are distributed fairly among all groups in society. No group should be unfairly burdened or excluded from the potential benefits of research.

Beneficence refers to the ethical principle of doing good and preventing harm. In research, this means maximizing the benefits of the study while minimizing any potential risks to participants.

Respect for persons means recognizing the autonomy and dignity of individuals. In research, this involves obtaining informed consent and ensuring that participants are treated with respect and honesty.

The Belmont Report is a key document in the history of research ethics. It outlines three fundamental ethical principles: respect for persons, beneficence, and justice, which guide ethical research practices.

The Stanford Prison Experiment was a study where participants were assigned roles as either prisoners or guards. The study had to be stopped early because the 'guards' began to treat the 'prisoners' inhumanely, causing significant psychological harm.

The Milgram experiment was a psychological study that tested how far people would go in obeying an authority figure, even if it meant harming another person. This study raised important ethical questions about the use of deception and the potential harm to participants.

The Tuskegee syphilis study was an unethical research study conducted by the U.S. government where Black men with syphilis were not treated so researchers could study the progression of the disease. This study is a key example of why ethical guidelines are crucial in research.

Social justice research focuses on studying and addressing issues of inequality and injustice in society. Researchers in this field often aim to promote fairness and equity through their work.

Procedural ethics refer to the formal rules and regulations that govern ethical behavior within institutions. These can include laws, policies, and guidelines that must be followed.

Professional codes of conduct are sets of guidelines that members of a profession agree to follow. These codes help ensure that professionals act ethically and responsibly in their work.

Research ethics are principles that guide how researchers should conduct their work responsibly and with integrity. This includes how they collect data, interact with participants, and report their findings.

Standards of Conduct are guidelines or rules that help us determine what is acceptable behavior and what is not. They help us make decisions about what is right and wrong.

Ethics is a branch of philosophy that deals with questions about what is morally right and wrong. In this course, we'll explore how these principles apply to research.

The critical paradigm focuses on critiquing power, privilege, and inequality. It aims to address oppression and injustice through its research.

Constructivists believe in multiple realities and meanings built from various experiences and interpretations. They often use qualitative research and inductive reasoning.

The post positivistic paradigm also uses hypotheses and deductive reasoning but acknowledges the limitations of the scientific method and the influence of researcher subjectivity.

The positivist paradigm is closely aligned with the natural sciences and involves generating and testing hypotheses using deductive reasoning. It aims for objective, measurable results.

Research paradigms are sets of assumptions about the world that shape how researchers view and conduct their studies. They influence the variables chosen, methods used, and explanations given.

Mixed methods research combines quantitative and qualitative methods into a single project to provide a more comprehensive understanding of the research question.

Qualitative research focuses on subjective experiences and meaning-making to understand social phenomena. It often uses open-ended questions and aims for a rich, meaningful understanding.

Quantitative research focuses on measuring variables, testing hypotheses, and explaining relationships using numerical data. It emphasizes objectivity and generalizability.

Research is systematic, meaning it follows a planned, orderly, and step-by-step process. This ensures that the research can be replicated and verified by others.

Deductive and inductive reasoning are two main forms of logical reasoning. Deductive starts with a general statement and moves to specifics, while inductive starts with specific observations and moves to general statements.

The scientific method is a systematic approach to acquiring knowledge based on forming hypotheses, experimenting, gathering data, and analyzing results. It's objective and based on logic.

Indigenous ways of knowing are traditional knowledge and beliefs passed down among indigenous communities. This way of knowing often includes a deep understanding of the environment and natural world.

Direct experience or observation is a way of knowing based on first-hand observation. It's often considered very reliable because it involves directly engaging with the world.

Intuition is knowledge gained through feelings and direct experiences rather than through reason or logic. It's often considered subjective but can provide valuable insights.

Authority as a way of knowing is based on the belief that knowledge comes from people with expertise or in positions of power. This could be your doctor, a teacher, or any other expert.

Tradition refers to beliefs and practices that are passed down from generation to generation. These can include cultural, religious, and even scientific traditions.

Ways of knowing refer to the different methods we use to gain knowledge about the world. These methods can vary widely and each has its own strengths and weaknesses.

Surveys generally have high external validity and generalizability if the sampling is done well. This means the results can be applied to the larger population.

Self-report surveys are useful for measuring things like attitudes, which might not be easily observed or measured in other ways.

Make sure the information you're seeking is something people can actually provide. If it's too complex or unknown, they won't be able to answer accurately.

Before creating survey questions, it's crucial to have a good understanding of the topic, especially if it's a new or emerging phenomenon. This ensures you ask relevant and appropriate questions.

Surveys are used to gather numerical data about trends, behaviors, attitudes, or opinions from a sample of a population. This helps researchers understand the larger population based on the sample.