Exam 2 Flashcards
A statistical relationship between two variables; when one variable changes, the other tends to change as well.
Correlation
A relationship where one variable directly affects or influences the other.
Causation
refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).
Validity
the extent to which the data or results of a research method represent the intended variable
Measurement validity
A test in which most people would agree that the test items appear to measure what the test is intended to measure would have strong face validity.
For example, a mathematical test consisting of problems in which the test taker has to add and subtract numbers may be considered to have strong face validity
Face validity
assesses whether a test is representative of all aspects of the construct. To produce valid results, the content of a test, survey or measurement method must cover all relevant parts of the subject it aims to measure.
• For example, a test to receive your driver’s license would not be valid if it had questions assessing pilot competency.
Content validity
the extent to which results from a study can be applied (generalized) to other situations, groups, or events.
External validity
the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables.
Internal validity
refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Reliability
How does the measure vary across time (i.e., weight). Will the same variables have similar results in future samples and research?
• For example, if I am measuring student safety in a survey. Would you be able to use the same safety survey next year?
Stability reliability
How does the measure do across groups (i.e., political opinion/rights). How will these measures differ when put against different samples? Should there be differences? Why or why not?
• For example, if I am measuring student safety in a survey, would you be able to use the same safety survey for freshman and seniors?
Representative reliability
Do other academics agree that your measure is reliable? A measure of consistency used to evaluate the extent to which different experts agree with the measure.
• For example, if I am measuring student safety in a survey, I would want other academics who have also measured this concept to agree that this my survey is measuring safety adequately.
Inter- rater reliability
taking abstract ideas (like “happiness,” “stress,” or “social justice”) and clearly defining what they mean in the context of your study.
Conceptualization
the step where abstract concepts (like “police legitimacy” or “recidivism”) are translated into specific indicators or variables that can be measured.
Operationalization
Ideas or phenomena that are being studied (i.e., crime)
Concepts
The measures/categories used to research the concepts (i.e., murder)
Variable
The specific, observable, and measurable question(s) used to make the variable (i.e., arrest or dead bodies)
Indicator
a variable that stands alone and isn’t changed by the other variables you are trying to measure.
Independent
result or outcome assumed by the independent variable.
Dependent
element which is constant and unchanged throughout the course of the investigation.
Control
third variable in a study examining a potential cause-and-effect relationship that is unaccounted for.
Extraneous (confounding)
the data can only be categorized. You can categorize your data by labelling them in mutually exclusive groups, but there is no order between the categories.
o Examples: city of birth, gender, car brands, and marital status
Nominal
the data can be categorized and ranked. You can categorize and rank your data in an order, but you cannot say anything about the intervals between the rankings.
o Examples: Satisfaction, steak rareness, and Likert-type questions (e.g., very dissatisfied to very satisfied)
Ordinal
the data can be categorized and ranked, and evenly spaced. You can categorize, rank, and infer equal intervals between neighboring data points, but there is no true zero point.
o Examples: IQ and temperature
Interval
the data can be categorized, ranked, evenly spaced and has a natural zero. You can categorize, rank, and infer equal intervals between neighboring data points, and there is a true zero point.
o Example: Height, age, and weight
Ratio
is the group of elements which has common characteristics. It is a collection of observations we would like to make inferences about.
Population
is the subset of a population
Sample
A collection of samples from the population is a sampling. In other words, sampling units are an overlapping collection of elements from the population
Sampling
A list of the items or people forming a population from which a sample is taken.
Sampling frame
Every element in the sample population has an equal chance of being selected. A sampling method is biased if every member of the population doesn’t have an equal likelihood of being in the sample.
Different Types of Probability Sampling
• Random Sampling
• Stratified Sampling
• Systematic Sampling
• Cluster Sampling
Probability sampling
It is a method of sampling in which every element of the universe has an equal probability of being chosen. For example, choosing an individual from a lottery. The advantage of this method is free from personal bias, and the universe gets fairly represented by samples.
Random sampling
The population is broken down into non-overlapping groups. In other words, strata (elements within the subgroups that are homogenous or heterogeneous). Then random samples are taken from each stratum, so the entire population is represented. The advantage of this method is it covers all the elements of the population. But there is a possibility of bias at the time of classification of the population.
Stratified sampling
Samples are selected from the population according to a pre-determined rule. In other words, every nth element is selected from the population as a sample. Arrange all the elements in a sequence and then select the samples from the population at regular intervals.
Systemic sampling
The population is broken down into many different clusters, and then clusters or subgroups are randomly selected. For example, clusters are of different ages, sex, locations, etc.
Cluster sampling
Purposive sampling is also known as judgment sampling. Samples are selected based on the purpose or intention of the research. The method is flexible to allow the inclusion of those items in the sample which are of special significance.
Judgement sampling
is one of the easiest sampling methods. Samples selection is based on availability as well as the selection of convenient samples for the researcher.
Convenience sampling
It is one type of stratified sampling, where samples are collected in each subgroup until the desired quota is met. The proportion of this sample does not match the proportion of the group to the population.
Quota sampling
or referral sampling is the method famous in medical and social science surveys where the population is unknown and difficult to get the sample. Hence researchers will take help from the existing elements to refer the others as samples who can fit in the population. Since it is based on referrals, there is a chance of bias.
Snowball sampling
