Chapter 1 Flashcards
how research is being used
1- basic research –> motivation is understanding
2- applied research –> motivation is application, adres issues
what are the motives for social science research
policy motivations
academic motivations
personal motivations
types of social research
description - describe
exploration - what it is to be like ..
explanation - why this happens
evaluation - what can be done about this situation
research strategies in SS
deductive research
indecutive research
deductive research
Deductive research is a method that starts with a general theory or hypothesis and tests it by gathering specific observations or data. It’s often used to confirm or disprove theories and follows a linear process, moving from the general to the specific.
theory –> hypothesis –> data –> conclusion
inductive research
Inductive research is a method that begins with specific observations and works toward broader generalizations and theories. Unlike deductive research, which starts with a hypothesis and tests it, inductive research seeks to generate new theories based on data. Here’s a short overview of the steps:
observations –> pattern –> hypothesis –> theory
criteria for good social research questions
feasible
social importance
scientific relevance
types of research design
cross-sectional designs
longitudinal designs
cross sectional design
single snap shot –> causality is very difficult to established
longitudinal design types
repeated observations over a period of time. problem of selective attrition
trend - cohort - panel
longitudinal - trend
data are collected at two or more points of time points from different samples of the same population
example: different people aged 20 in 1980 & 1990 & 2000
longitudinal - cohort
members of the population doesn’t change new sample taken at each different point in time
example : different people who are 20 - 1980 and 30- 1990 and 40 -2000
longitudinal - panel
the same exact sample is used at each point in time
same people in differnet time points ( original version )
what are the advantages and limitations of longitudinal study
better at establishing causality as iv and dv s are measured at different times
costs to much and time consuming
what are the advantages and limitations of cross sectional study
easy and less costly but causality is hard to establish
what is ecological fallacies
when the unit of analysis is groups but the conclusionis done to individuals
what is reductionist fallacy
when the unit of analysis is individuals but the conclusions are done based on groups
theory
A theory is an explanation of the relationships among phenomena
A generalized explanation of the relationship between two or more variables.
Continuous variable
A variable with a unit of measurement that can be subdivided infinitely.
Data
Information expressed as numbers.
Data reduction
Summarizing many scores with a few statistics.
Dependent variable
A variable that is identified as an effect or outcome. The dependent variable is thought to be caused by the independent variable.
Descriptive statistics
The branch of statistics concerned with (1) summarizing the distribution of a single variable or (2) measuring the relationship between two or more variables.
Discrete variable
A variable with a basic unit of measurement that cannot be subdivided.
Hypothesis
A specific statement, derived from a theory, about the relationship between variables
Independent variable
A variable that is identified as a cause. The independent variable is thought to cause the dependent variable.
Inferential statistics
The branch of statistics concerned with making generalizations from samples to populations.
Level of measurement
The mathematical characteristic of a variable and the major criterion for selecting statistical techniques. Variables can be measured at any of three levels, each permitting certain mathematical operations and statistical techniques. The characteristics of the three levels are summarized in Table 1.2.
Measures of association
Statistics that summarize the strength and direction of the relationship between variables.
Population
The total collection of all cases in which the researcher is interested.
Quantitative research
Research projects that collect data or information in the form of numbers.
Research.
Any process of gathering information systematically and carefully to answer questions or test theories. Statistics are useful for research projects that collect numerical information or data.
Sample
A carefully chosen subset of a population. In inferential statistics, information is gathered from a sample and then generalized to a population.
Statistics
A set of mathematical techniques for organizing and analyzing data.
Variable
Any trait that can change values from case to case.
conceptualization
coming to an agreement about what the term mean, give definite meaning
operationalization
specify precisely how a concpet will be measured
levels of measurement
qualitative - nominal and ordinal
quantitative - interval and ratio
nominal measurment
categories categories but there is no order between them. Eye color, gender, rank the phone brands
ordinal measurement
rated or ordered categories
but there are unequal intervals. Usually
non mathematical ideas such as
happiness and satisfaction.
Ex: 1- strongly agree, 2- disagree, 3-
agree, 4- strongly agree. Time of the day:
night dawn, morning, noon, afternoon,
evening
interval
INTERVAL: categories are approximately equally spaced or ordered ( units ).
Numeric properties are taken seriously. Intervals can be calculated and compared meaningfully. ALWAYS NUMERIC
Example: Fahreinheight 70-80 and
90-100. ( the intervals can be interpretable. But the values don’t make sense such as 80 degrees hot isn’t twice as hot as 40 degrees. THe value 0 is ARBITRARY.
ratio measurement
equal intervals with a true
zero. Ex: money, height, weight
A true zero point. Mean, mode and
median can be calculated using ratio scale
reliability
Reliability: This refers to the consistency or stability of a measurement over time. A research study or measurement tool is reliable if it produces the same results under consistent conditions. Types of reliability include:
Test-Retest Reliability: Checking if the results are consistent over time.
Inter-Rater Reliability: Ensuring different observers or raters provide consistent scores.
Internal Consistency: Verifying that different items within a test measure the same concept.
validity
Validity: This indicates how well a test or instrument measures what it’s intended to measure. Validity ensures the accuracy of the results and that they truly represent the phenomenon being studied. Types of validity include:
Construct Validity: The degree to which a test measures the concept it claims to measure.
Content Validity: Ensuring the measurement covers all aspects of the concept.
Criterion Validity: The extent to which a measurement correlates with an outcome or other measures (e.g., concurrent or predictive validity).
