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chapter two

theory

simplified explanation of the world

what does a theory talk about

relationship between a dependent and independent event.

what can a theory do

create a hypotheis. test and not prove right

Political scientists strive for theories that are:

parsimonious
general

falsied

theory...

Theory grows and develops through a balance between theory- building research and theory-testing research

theory building research

Also called “inductive” or “exploratory research”
qual research well suitednfor theory building

theory testing research

also knowj deductive research
the researcher collects and analyzes real-world observations to assess if

the theory is supported

theory explains

relationships between concepts

concepts

Helps us organize and classify phenomena and are central to theory

concepts must have...

conceptual definition (specific and clear about what the concept is and is not.)

What makes good concepts for research?

They are clearly expressed in order to prevent misunderstanding or obfuscation.
• They will make sense to other researchers and help locate the concept in the literature.

• The operational definitions (how we actually measure a concept) are a good reflection of the conceptual

definition.

concepts can be

Typologies or Continuum:
Unidimensional or Multidimensional

Concrete or abstract

Typologies or Continuum:

Typologies: The classification of cases based on their characteristics.
Continuum: a continuous series or event whose parts are indistinguishable from each other.

Unidimensional or Multidimensional

Unidimensional: a concept with only one underlying dimension. (for example: age or height)
• Multidimensional: is one in which more than one factor, or dimension, exists within a concept (for

example: social status)

Concrete or abstract

Concrete: being straightforward
Abstract: (e.g democracy)

Conceptualization

the process of choosing the conceptual definition for a study.

conceptulization three reasons

Choice of Conceptual Definition:
Clarity and Consistency:

Link between Concepts and Measures:

what is the process or transforming abstract concepts into measurable forms

operalization

conceptualized

having a strong conceptual definition that makes sense and
makes sense in the context of the study – all of these can be good concepts to use for research.

what is indutive reasoning

drawing conclusions from empirical evidence) to form or adjust conceptual definitions.

in theory building

concepts and their expected relationship are defined

in theory testimg

hypotheses are tested with empirical evidence.

explain measure

is a tool by which we obtain observable evidence about our concept of interest.

applied research

focuses on answering specific questions or solving immediate problems

operationlization

variable which is a more concrete representation of the concept that has within it variations, to an
◦ indicator, which is the means by which we assign each individual case to the different values of the

variable.

concepts are

abstract ideas, while variables are measurable manifestations of those concepts, and indicators are specific ways to measure the variables.

operalization involves

making choices about how to translate concepts into variables and indicators.

back and forth process

constant movement between concepts, variables, and indicators to ensure that empirical
measurements accurately reflect the abstract concepts.

trade offs

Moving from concepts to variables and indicators involves trade-offs, as some aspects of a concept may not be fully captured.

What do you do when a concept is too difficult to capture in a single variable?

devise an index

Hypotheses

central to theory-testing research and to our understanding of causal relationships.

correlation

means there is a relationship between two variables, but it doesn’t indicate which is the
cause and which is the effect.

casuality

when one event produces a change in another.

casual mechanism

explains why a relationship exists between two variables

importance of casuality

understanding cause and effect

5 Causality Criterions

correlation
temporal order

Absence of confounding variables

plausible causal mechanism

consistency

null hypothesis

The default assumption that there is no relationship between the two variables of interest.

The research hypothesis:

the relationship you actually want to test

Bivariate relationships

relationship between two variables, one dependent and one independent.

Multivariate relationships

relationship between more than two variables (one dependent and two or more independe

types of variables

intervening variable
conditional

reinforcing

spurious

intervening variable

one that comes between the independent and dependent

conditional variables

variables that strengthen the relationship between an independent and a dependent variable for some
categories

reinforcing variables

variables that strengthen and magnify the relationship between an independent and dependent
variable

spurioous variables

something that inflences both IV and DV and creates a problem for casuality

categorial concept

differences between values are differences in kind

Continuous concept:

differences between values are differences in amount

Level of measurement:

the precision used in interpreting the numerical values of the measures used in the research project;
distinction is made among nominal, ordinal, and interval/ratio measures.

Nominal variables:

The levels of measurement, moving from the lowest to the highest level of precision, are as follows

Ordinal variables:

variables that allow for the ordering of categories along a continuum, but without a precise distance
between the categories.

Interval variables:

variables that can be ordered and where the categories are separated by a standard unit; do not have
an absolute zero.

Ratio variables:

have the same qualities as interval variables, with the additional quality of an absolute zero

how to determine what level of measurement to use

concept itself
the analysis yo want

generlizability and accuracy

measurement error

random error
non random error

how to decrease measurement error

reliability
measureneb validitiy

other types of validity

face
internal

extrenal

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