MMW - Prefinals

Data

raw information or facts that become useful information when organized in a meaningful way.

Data

It could be of qualitative and quantitative nature.

Data Management

is concerned with "looking after" and processing data.

Data Management

Looking after field data sheets

Data Management

Checking and correcting the raw data

Data Management

Preparing data for analysis

Data Management

Documenting and archiving the data and meta-data

Importance of Data Management

Ensures that data for analysis are of high quality so that conclusions are correct

Importance of Data Management

Good data management allows further use of the data in the future and enables efficient integration of results with other studies.

Importance of Data Management

Good data management leads to improved processing efficiency, improved data quality, and improved meaningfulness of the data.

Census

this is the procedure of systematically acquiring and recording information about all members of a given population.

Researchers rarely survey the entire population

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.

advantages of sampling

(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).

possibility of replication

One of the main requirements to experiments

Observation study

this is appropriate when there are no controlled variables and replication is impossible.

Observation study

This type of study typically uses a survey.

Observation study

An example is one that explores the correlation between smoking and lung cancer.

Characteristics of a well-designed and well-conducted survey

A good survey must be representative of the population.

Characteristics of a well-designed and well-conducted survey

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.

Characteristics of a well-designed and well-conducted survey

The wording of the question must be neutral; subjects give different answers depending on the phrasing.

Characteristics of a well-designed and well-conducted survey

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.

sampling frame

Its subset of items possible to measure is called this (from which the sample will be selected).

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.

Nonprobability sampling

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

Nonprobability sampling

These conditions give rise to exclusion bias, caused by the fact that some elements of the population are excluded.

Nonprobability sampling

does not allow the estimation of sampling errors.

Nonprobability sampling

Information about the relationship between sample and population is limited, making it difficult to extrapolate from the sample to the population.

Nonprobability sampling

We visit every household in a given street, and interview the first person to answer the door.

convenience sampling

customers in a supermarket are asked questions

quota sampling

when judgment is used to select the subjects based on specified proportions.

quota sampling

For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.

nonresponse effects

may turn any probability design into a nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled.

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.

Simple Random Sampling (SRS)

The frame is not subdivided or partitioned.

Simple Random Sampling (SRS)

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)

can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population.

Systematic and stratified techniques

attempt to overcome this problem by using information about the population to choose a more representative sample.

Simple Random Sampling (SRS)

cannot accommodate the needs of researchers in this situation because it does not provide subsamples of the population.

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.

Systematic Sampling

A simple example would be to select every 10th name from the telephone directory, with the first selectin being random.

Systematic Sampling

may select a sample from the beginning of the list.

Systematic Sampling

helps to spread the sample over the list.

Systematic Sampling

starting point is randomized.

Every 10th sampling

is especially useful for efficient sampling from databases.

Systematic Sampling

is especially vulnerable to periodicities in the list.

drawback of systematic sampling

is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to quantify that accuracy.

Systematic sampling

is not SRS because different samples of the same size have different selection probabilities e.g. the set (4,14, 24,) has a one-in-ten probability of selection, but the set (4,1,24, 34,) has zero probability of selection.

Stratified Sampling

when the population embraces a number of distinct categories, the frame can be organized by these categories into separate "strata".

Stratified Sampling

Each stratum is then sampled as an independent sub-population.

Dividing the population into strata

can enable researchers to draw inferences about specific subgroups that may be lost in a more generalized random sample.

stratum

is treated as an independent population, different sampling approaches can be applied to different strata.

Stratified Sampling

To determine the proportions of defective products being assembled in a factory.

stratified sampling approach is most effective

Variability within strata are minimized

stratified sampling approach is most effective

Variability between strata are maximized

stratified sampling approach is most effective

The variables upon which the population is stratified are strongly correlated with the desired dependent variable (beer consumption is strongly correlated with gender).

Cluster Sampling

sometimes it is cheaper to 'cluster' the sample in some way (e.g. by selecting respondents from certain areas only, or certain time-periods only).

Cluster 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

This works best when each cluster is a small copy of the population.

Cluster sampling

This can reduce travel and other administrative costs.

Cluster sampling

increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between themselves, as compared with the within-cluster variation.

clusters chosen are biased

inferences drawn about population parameters will be inaccurate.

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).

Matched random sampling

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).

good statistical experiment

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.

good statistical experiment

Design of experiments, using blocking (to reduce the influence of confounding variables) and randomized assignment of treatments to subjects

blocking

(to reduce the influence of confounding variables)

good statistical experiment

Examining the data set in secondary analyses, to suggest new hypotheses for future study

good statistical experiment

Documenting and presenting the results of the study

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.

experimental unit

is subjected to treatment.

control group

is not subjected to treatment.

Random Assignments

The second fundamental design principle is randomization of allocation of (controlled variables) treatments to units.

Random Assignments

The treatment effects, if present, will be similar within each group.

Replication

All measurements, observations or data collected are subject to variation, as there are no completely deterministic processes.

Replication

To reduce variability, in the experiment the measurements must be repeated.

Replication

The experiment itself should allow for this itself, to be checked by other researchers.

Confounding

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

false positive (Type I) error

(an erroneous conclusion that the dependent variables are in a causal relationship with the independent variable).

not controlling confounding variables

It can lead to a false positive (Type I) error, suggesting a causal relationship that does not exist.

Confounding

statistical relationship between ice cream sales and drowning deaths.

placebo

is an imitation pill identical to the actual treatment pill, but without the treatment ingredients.

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.

Blinding

is a technique used to make the subjects "blind" to which treatment is being given.

Blocking

is the arranging of experimental units in groups (blocks) that are similar to one another.

blocking factor

is a source of variability that is not of primary interest to the experimenter.

blocking factor

An example of this might be the sex of a patient; by blocking on sex (that is comparing men to men and women to women), this source of variability is controlled for, thus leading to greater precision.

Completely randomized designs

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

Completely randomized designs

The experiment compares the values of a response variable (like health improvement) based on the different levels of that primary factor (e.g., different amounts of medication).

Completely randomized designs

the levels of the primary factor are randomly assigned to the experimental units (for example, using a random number generator).

Randomized block design

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

Randomized block design

A matched pairs of design is its special case when the blocks consist of just two (2) elements (measurements on the same patient before and after the treatment or measurements on two (2) different but in some way similar patients).

chi-square test

is used to determine whether there is significant difference between the expected value frequencies and the observed frequencies in one or more categories.

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.

chi-square test for independence

It tests to see whether the distributions of categorical variables differ from each other.

A very small chi-square test statistic

means that your observed data fits your expected data well. In other words, there is a relationship.

A very large chi-square test statistic

means that the data does not fit very well; there is no relationship.