Pre-Finals Flashcards
(35 cards)
raw information or facts that become useful information when organized in a meaningful way
Data
Is concerned with “looking after” and processing data
Data Management
Importance of Data Management
- Ensures that data for analysis are of high quality
- Good data management allows further use of the data in the future and enables efficient integration of results with other studies.
- Good data management leads to improved processing efficiency, improved data quality, and improved meaningfulness of the data.
this is the procedure of systematically acquiring and recording information about all members of a given population
Census
Researchers rarely survey the entire population for two (2) reasons _______________
Cost is too high
Population is dynamic
Is a selection of a subset within a population
Sample Survey
Three main advantages of sampling
Cost is lower
Data collection is faster
Improve the accuracy and quality
of the data.
performed when there are some controlled variables
Experiment
main requirements to experiments?
Possibility of Replication
appropriate when there are no controlled variables and replication is impossible
Observation Study
Ex: explores the correlation
between smoking and lung cancer.
Observation Study
Characteristics of a well-designed and well-conducted survey
- Good survey must be representative of the population
- Always incorporates a chance, such as a random number generator
- Wording of the question must be neutral
- Possible sources of errors and biases should be controlled
sampling method where some elements of the population have no chance of selection
Non-Probability Sampling
Ex: customers in a supermarket are asked questions
Convenience sampling
judgment is used to select the subjects based on specified proportions
Quota sampling
Ex: interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.
Quota sampling
All samples of a given size have an equal probability of being
selected and selections are independent.
Simple Random Sampling
Dividing the target population into strata (subpopulations) of equal size and then selecting randomly one element from the first stratum and corresponding elements from all other strata
Systematic Sampling
when the population embraces a number of distinct categories, the frame can be organized by these categories into separate “strata”
Stratified sampling
A stratified sampling approach is most effective when three conditions are met:
a. Variability within strata are minimized
b. Variability between strata are maximized
c. The variables upon which the population is stratified are strongly correlated with the desired
dependent variable.
(e.g. by selecting respondents from certain areas only, or certain time-periods only). Is an example
of two-stage random sampling
Cluster sampling
(2) samples in which the members are clearly paired, or are matched explicitly by the researcher
Matched Random Sampling
Characteristics of a well-designed and well-conducted experiment
- Stating the purpose of research
- Design of experiments
- Examining the data set in secondary analyses
- Documenting and presenting the results of the study
an extraneous variable in a statistical model that correlates
(positively or negatively) with both the dependent variable and the independent variable.
Confounding