preFinals Math In Modern World Flashcards
(39 cards)
are raw information or facts that become useful information when organized in a meaningful way. It could be of qualitative and quantitative nature.
Data
is concerned with “looking after” and processing data
Data Management
Data management involves the following: (there are 4 items)
• Looking after field data sheets
• Checking and correcting the raw data
• Preparing data for analysis
• Documenting and archiving the data and meta-data
Importance of Data Management
(there are 3 items)
•Ensures that data for analysis are of high quality so that conclusions are correct
• 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.
Methods of Data Collection
(4 items)
Census
Sample survey
Experiment
Observation study
this is the procedure of systematically acquiring and recording information about all members of a given population. Researchers rarely survey the entire population for two (2) reasons: the cost is too high and the population is dynamic in that the individuals making up the population may change over time.
Census
sampling is a selection of a subset within a population, to yield some knowledge about
the population of concern. The three main advantages of sampling are that (i) the cost is lower, (ii) data
collection is faster, and (iii) since the data set is smaller, it is possible to improve the accuracy and quality
of the data.
Sample survey
this is performed when there are some controlled variables (like certain treatment in medicine) and the intention is to study their effect on other observed variables (like health of patients). One of the main requirements to experiments is the possibility of replication.
Experiment
this is appropriate when there are no controlled variables and replication is impossible. This type of study typically uses a survey. An example is one that explores the correlation
between smoking and lung cancer. In this case, the researchers would collect observations of both smokers and non-smokers and then look for the number of cases of lung cancer in each group.
Observation study
Planning and Conducting Surveys
1. Characteristics of a well-designed and well-conducted survey
a. ?
b. ?
c. ?
d. ?
- A good survey must be representative of the population.
-
To use the probabilistic results, it always incorporates a chance, such as a random number generator.
Often we don’t have a complete listing of the population, so we have to be careful about exactly how
we are applying “chance”. Even when the frame is correctly specified, the subjects may choose not to
respond or may not be able to respond. - The wording of the question must be neutral; subjects give different answers depending on the phrasing.
-
Possible sources of errors and biases should be controlled. The population of concern as a whole may not be available for a survey. Its subset of items possible to measure is called a sampling frame (from
which the sample will be selected). The plan of the survey should specify a sampling method, determine
the sample size and steps for implementing the sampling plan, and sampling and data collecting.
2 types of sampling method
Non-probability sampling
Probability sampling
is any sampling method where some elements of the population have no
chance of selection or where the probability of selection can’t be accurately determined.
Non-probability sampling
it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected.
Probability sampling
One example of nonprobability sampling is** 1. ___________ **sampling (customers in a supermarket are
asked questions). Another is 2. _____ sampling, when judgment is used to select the subjects based on
specified proportions.
- convenience sampling
- quota sampling
The following sampling methods are example of probability sampling:
(There are 5 of them)
Simple Random Sampling (SRS)
Systematic sampling
Stratified sampling
Cluster sampling
Matched random sampling
all samples of a given size have an equal probability of being
selected and selections are independent. The frame is not subdivided or partitioned. The sample variance is a good indicator of the population variance, which makes it relatively easy to estimate
the accuracy of results.
Simple Random Sampling (SRS)
relies on 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
is an example
of two-stage random sampling: in the first stage a random sample of areas is chosen; in the second
stage a random sample of respondents within those areas is selected.
Cluster sampling
in this method, there are two (2) samples in which the members are
clearly paired, or are matched explicitly by the researcher (for example, IQ measurements or pairs
of identical twins). Alternatively, the same attribute, or variable, may be measured twice on each
subject, under different circumstances (e.g. the milk yields of cows before and after being fed a
particular diet).
Matched random sampling
C. Planning and conducting experiments:
1. Characteristics of a well-designed and well-conducted experiment
A good statistical experiment includes: (4 items)
a. Stating the purpose of research, including estimates regarding the size of treatment effects,
alternative hypotheses, and the estimated experimental variability. Experiments must compare the
new treatment with at least one (1) standard treatment, to allow an unbiased estimates of the
difference in treatment effects.
b. **Design of experiments, **using blocking (to reduce the influence of confounding variables) and
randomized assignment of treatments to subjects
c. **Examining the data set in secondary analyses, **to suggest new hypotheses for future study
d. Documenting and presenting the results of the study
- Treatment, control groups, experimental units, random assignments and replication
(3 items)
a. Control groups and experimental units
- To be able to compare effects and make inference about associations or predictions, one typically has to subject different groups to different conditions. Usually, an experimental unit is subjected to treatment and a control group is not.
b. Random Assignments
- The second fundamental design principle is randomization of allocation of (controlled variables)
treatments to units. The treatment effects, if present, will be similar within each group.
**c. Replication **
- All measurements, observations or data collected are subject to variation, as there are no
completely deterministic processes. To reduce variability, in the experiment the measurements
must be repeated. The experiment itself should allow for replication itself should allow for
replication, to be checked by other researchers.
To be able to compare effects and make inference about associations or predictions, one typically
has to subject different groups to different conditions. Usually, an experimental unit is subjected to
treatment and a control group is not.
Control groups & experimental units
The second fundamental design principle is randomization of allocation of (controlled variables)
treatments to units. The treatment effects, if present, will be similar within each group.
c. Replication
Random Assignments