Metod I

Causal relationship Refers to a relationship where one event (the cause) directly leads to another event (the effect)

Correlation A statistical measure of covariation which summarizes the direction (positive or negative) and strength of the linear relationship between two variables

Covariation When two variables vary together, when one variable changes, there is a corresponding change in the other variable

Dependent variable The effect or outcome that is measured or observed (Y)

Independent variable The cause or input that is changed or controlled (X)

Empirical evidence Information obtained through real-world observation or experiments

Hypothesis A testable prediction or explanation for an observation or phenomenon (explicit statement of a theory)

Normative questions Ask how things should be, or what policies are best

Null hypothesis A theory-based statement about what we would observe if there were no relationship between an independent variable and the dependent variable

Operationalize To define a concept in measurable terms

Theory A tentative conjecture about the causes of some phenomenon of interest, idea or set of ideas that explains how or why something happens, based on evidence and observations

Bivariate Involving just two variables

Confounding variable A variable that is correlated with both the independent and dependent variables and that somehow alters the relationship between those two variables

Deterministic relationship If some cause occurs, then the effect will occur with certainty

Multivariate Involving more than two variables

Probabilistic relationship Increases in X are associated with increases (or decreases) in the probability of Y occurring, but those probabilities are not certainties

Spurious Not what it appears to be, or false

Empirical questions Ask about how the world is, or how the world does work

Parsimony Means that the theory explains more using less

Predictiveness Means that the theory can help us to understand cases beyond those from which we derived it

Falsifiability Is the ability of a theory to be proven false through evidence or testing

Aggregate A quantity that is created by combining the values of many individual cases

Control group In an experiment, the subset of cases that is not exposed to the main causal stimulus under investigation

Treatment group In an experiment, the subset of cases that is exposed to the main causal stimulus under investigation

Observational study Involves watching and analyzing subjects without manipulating variables

Experiment The researcher both controls and randomly assigns values of the independent variable to the participants

External validity The extent to which research findings generalize to the broader population

Internal validity Refers to the degree to which a study can demonstrate a causal relationship between the variables being studied, free from confounding factors, related to causal inference

Population The entire group of cases you want to draw conclusions about

Random assignment When the participants for an experiment are assigned randomly to one of several possible values of X, the independent variable

Random sampling A method for selecting individual cases for a study in which every member of the underlying population has an equal probability of being selected

Replication A scientific process in which researchers implement the same procedures

Sample of convenience The sample is drawn from a part of the population that is easily accessible for the researcher

Natural (quasi) experiment Random assignment by some naturally occurring phenomenon, not controlled by the researcher

Temporal order To infer that one variable causes another, researchers need to demonstrate that the cause precedes the effect in time

Isolation Refers to the process of controlling or minimizing the influence of confounding variables to accurately assess the relationship between specific variables of interes

Mechanism Chain of events or reactions or changes, that connects the independent (cause) variable(s) with the dependent (outcome) variable

Case A spatially and temporally delimited phenomenon (a unit) observed at a single point in time or over some period of time

Case selection Procedures for selecting a subset of cases from a larger population for analysis, used in case study research where the focus is on in-depth analysis of a small number of cases

Case study The intensive study of a case (cases) for the purpose of understanding a larger class of similar units

Least-likely case A case that is very unlikely to validate the predictions of a model or a hypothesis, makes it hard for your theory, useful to prove a theory

Most-likely case A case that is very likely to validate the predictions of a model or a hypothesis, if found to be invalid, this may be regarded as strong disconﬁrming evidence

Typical case Case close to the regression line, represent the relationship between the independent and dependent variable in your population

Covariation across time Measuring both before and after (contrafactual), allows us to compare the measures in the treatment group from before X and after X.

Covariation across space Meauring variables in different geographical locations at the same point in time (measuring treatment and control group)

Level of analysis Refers to the scale at which a phenomenon is examined in research, ranging from individuals, groups, and organizations, to entire societies or the global system

Most-similar design Cases are similar in all respects except the variables of theoretical interest (Mills method of difference)

Most-different design Cases are different in all respects except the variables of theoretical interest (Mills method of agreement)

Deviant case An outlier that doesn’t follow the trend, lying far from the regression line (new explanations, theory generating)

Process tracing The systematic examination of diagnostic evidence selected and analysed in light of research questions and hypotheses posed by the investigator, studies causal mechanisms in a single-case research design

Causal criteria 1. Covariation 2. Temporal order 3. Isolation 4. Mechanism

Causal effect Requires covariation, temporal order, and isolation

Causal explanation Requires all four causal criterias

Research design The way in which empirical evidence is used to support or refute a hypothesis

Sample The set of cases (and observations) upon which the researcher is focused

Selection bias Occurs when the sampling method systematically favors certain groups or characteristics, resulting in a non-representative sample

Deductive approach From theory to empiric (theory testing)

Inductive approach From empiric to theory (theory development)

Extreme cases Shows the phenomenon in a highly intense form, sitting at one end of the range on the x- or y-axis

Influencial case Any case that significantly alters the value of a regression coefficient whenever it is deleted from an analysis

Crucial case Is most likely or least likely to exhibit a given outcome

Scientific ideal Evidence-based, Inference, Uncertainty, Falsifiability Today, Intersubjectivity, Cumulativity, Ethics, Causality

Inference The process of drawing conclusions or making judgments based on empirical evidence

Uncertainty All inference we draw about the world is preliminary and uncertain

Intersubjectivity Scientific research is transparent, replicability is a related concept

Cumulative Scientific knowledge builds over time, new evidence can refine or overthrow previous knowledge

Scope conditions Identifies the outer boundaries of a theory, what universe of cases the theory applies to

Measurement validity The degree to which a measurement actually measures the theoretical concept it is intended to

Sampling error Just by chance, there are random differences between the sample and the population

Sampling bias The individuals who participate in the study may differ systematically from the target population

Reliability The degree to which a measurement yields the same results every time it is applied to the same cases

Random error Comes from factors that affect responses randomly across the sample (respondents’ moods), affects the spread of the data (there is more variation in responses)

Systematic error Comes from factors that affects some responses systematically (respondents tend to exaggerate their answers), affects the mean of the sample (might be over-reported)

Social desirability bias When individuals understate behaviors they may be ashamed of and overstate those they are proud of

Sensitivity bias Respondents do not provide truthful answers for many reasons (social stigma, fear of punishment/stigma, in pursuit of material gains/status)

Recall bias Respondents have trouble remembering events or experiences accurately, especially under stressful circumstances

Field visit purpose 1. The collection of both primary and secondary data through various research techniques, 2. improved understanding of the case and the context

Control variables Variables that are held constant to prevent them from influencing the outcome

Mediating variable A variable that explains the causal mechanism

Triangulation At least two independent sources are confirming or supporting the same finding, combine different types of sources

Discrete variables Are counted, can only take on certain values (number of fatalities in armed conflict)

Continuous variables Are measured, can take on any value, sometimes within a specific range (income, length)

Dichotomous (dummy) variables Can only take on two values, also known as an indicator or binary variable, usually coded as 0 or 1

Measurement scales Nominal, ordinal, interval, and ratio scales

Nominal scale Measures whether units belong to different categories, cannot be ranked (gender, religion, etc)

Ordinal scale Values can be ranked, but the difference between each value is not constant (Educational level, likert scale, etc)

Interval scale Values can be rank ordered; we can assume that the difference between each value is the same, no true zero, zero is arbitrarily defined (tempeture, calender years, etc)

Ratio scale Possesses all the characteristics of an interval scale, with the additional feature of having a true zero point (income, length, distance, etc)

Sampling frame Is a list or database that includes individuals or units from a population, used as the basis for selecting a sample in research

Non-response bias Occurs when those who don't participate in a survey differ in important ways from those who do, potentially leading to inaccurate or skewed findings

Mutually exclusive When categorizing variables each unit or observation can belong to only one category, there is no overlap between categories

Mutually inclusive When categorizing variables a unit or observation can belong to multiple categories simultaneously

Large-N studies Involve a large number of cases or observations, good for detecting covariation and isolating

Small-N studies Focus on a small number of cases, good for establishing temporal order and uncovering causal mechanisms

Sampling Procedures for selecting a subset of cases from a larger population for analysis, typically used in large-N studies

Sampling strategies Simple random samples, stratification, cluster sampling, multistage sampling, spatial sampling, random-walk, convenience samples

Aspects that can introduce error to survey instruments Question content and wording, response format, length of survey, question order, order of response options

Snowball sampling Is a non-random sampling technique where existing participants help recruit future participants from their acquaintances, creating a chain-like referral process, often used in hard-to-reach or niche populations

Positionality One’s own social and political context affects your perspective, worldview, and interactions, and hence your research, can influence participants’ comfort levels, openness, and authenticity in sharing information, potentially leading to biased data or altered responses

Reflexivity The critical examination of one’s own beliefs, judgments, practices, and position during the research process and how these may influence the research

Gatekeepers Are individuals or organizations that have control over access to a certain group, community, or resource, and can influence who is allowed to participate in a study or gain entry to the research setting

Field work Research method that involves collecting data and observing phenomena in a real-world, natural setting, allows researchers to gather firsthand insights

Diverse case Represents variation within the expected pattern but still aligns with the overall trend, valuable for understanding subtler factors that influence variations within the expected pattern

Positionality-interviewer effects Refer to the influence that a researcher’s background, identity, and perceived position (such as race, gender, age, or socio-economic status) can have on participants’ responses during an interview

Klicka

Skriv

Lyssna

Spel

Skriv ut