mmw review pre final

Data

are raw information or facts that become useful information when organized in a meaningful way. It could be of qualitative and quantitative nature.

Census

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.

Sample Survey

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.

Experiment

– 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.

Observation study

– 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.

Nonprobability 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. The selection of elements is based on some criteria other than randomness.

Probability Sampling

– it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected.

Simple Random Sampling (SRS)

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.

Systematic Sampling

– 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.

Stratified Sampling

when the population embraces a number of distinct categories, the frame can be organized by these categories into separate “strata”. Each stratum is then sampled as an independent sub-population.

Cluster Sampling

– sometimes it is cheaper to ‘cluster’ the sample in some way (e.g. by selectingrespondents from certain areas only, or certain time-periods only).

Matched random 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).

Confounding

– a confounding variable is an extraneous variable in a statistical model that correlates (positively or negatively) with both the dependent variable and the independent variable.

Placebo and blinding

– a placebo is an imitation pill identical to the actual treatment pill, but without the treatment ingredients. A placebo effect is a sham (or simulated) effect when medical intervention has no direct health impact but results in actual improvement of a medical condition because the patients knew they were treated.

Blocking

is the arranging of experimental units in groups (blocks) that are similar to one another. Typically, a blocking factor is a source of variability that is not of primary interest to the experimenter.

Completely randomized designs

– are for studying the effects of one primary factor without the need to take other nuisance variables into account.

Randomized block design

– is a collection of completely randomized experiments, each run within one of the blocks of the total experiment.

chi-square goodness of fit test

determines if a sample data matches a population.

chi-square test for independence

compares two (2) variables in a contingency table to see if they are related.