Discuss the various data collection methods described in the section.

Date: 2015-10-07; view: 507.

Ratio Scale

Interval Scale

Ordinal Scale

Measurements with ordinal scales are ordered in the sense that higher numbers represent higher values. However, the intervals between the numbers are not necessarily equal. For example, on a five-point rating scale measuring attitudes toward gun control, the difference between a rating of 2 and a rating of 3 may not represent the same difference as the difference between a rating of 4 and a rating of 5. There is no "true" zero point for ordinal scales since the zero point is chosen arbitrarily. The lowest point on the rating scale in the example was arbitrarily chosen to be 1. It could just as well have been 0 or -5.

In the interval scale of measurement the value of zero is assigned arbitrarily and therefore we cannot take ratios of two measurements. But we can take ratios of intervals.

Highest level of measurement in which not only the differences between observations are quantifiable, but the observations can themselves be expressed as a ratio. A 10,000-square-foot warehouse is both 50% smaller and 10,000 square feet less than a 20,000- square-foot warehouse. The ratio scale is the most powerful measurement scale.

To derive conclusions from data, we need to know how the data were collected; that is, we need to know the method(s) of data collection.

Methods of Data Collection

There are four main methods of data collection.

Census. A census is a study that obtains data from every member of a population. In most studies, a census is not practical, because of the cost and/or time required

Sample survey. Sample survey is the technique used to study about a population with the help of a sample. Population is the totality all objects about which the study is proposed. Sample is only a portion of this population, which is selected using certain statistical principles called sampling designs (this is for guaranteeing that a representative sample is obtained for the study). Once the sample decided information will be collected from this sample, which process is called sample survey.

Experiment. An experiment is a controlled study in which the researcher attempts to understand cause-and-effect relationships. The study is "controlled" in the sense that the researcher controls (1) how subjects are assigned to groups and (2) which treatments each group receives.

In the analysis phase, the researcher compares group scores on some dependent variable. Based on the analysis, the researcher draws a conclusion about whether the treatment ( independent variable) had a causal effect on the dependent variable.

Observational study. Like experiments, observational studies attempt to understand cause-and-effect relationships. However, unlike experiments, the researcher is not able to control (1) how subjects are assigned to groups and/or (2) which treatments each group receives.

Data Collection Methods: Pros and Cons

Each method of data collection has advantages and disadvantages.

Resources. When the population is large, a sample survey has a big resource advantage over a census. A well-designed sample survey can provide very precise estimates of population parameters - quicker, cheaper, and with less manpower than a census.

Generalizability. Generalizability refers to the appropriateness of applying findings from a study to a larger population. Generalizability requires random selection. If participants in a study are randomly selected from a larger population, it is appropriate to generalize study results to the larger population; if not, it is not appropriate to generalize.

Observational studies do not feature random selection; so it is not appropriate to generalize from the results of an observational study to a larger population.

Causal inference. Cause-and-effect relationships can be teased out when subjects are randomly assigned to groups. Therefore, experiments, which allow the researcher to control assignment of subjects to treatment groups, are the best method for investigating causal relationships.

Therefore, a sample survey is not an experimental study; rather, it is an observational study. An observational study may or may not require fewer resources (time, money, manpower) than an experiment. The best method for investigating causal relationships is an experiment - not an observational study - because an experiment features randomized assignment of subjects to treatment groups.

1-1.Describe the variables implicit in 11 items as quantitative or qualitative, and describe the scales of measurement:

Statistical analysis often involves an attempt to generalize from the data. Statistics is a science – the science of information. Information may be Qualitative and Quantitative.

For example, price is a quantitative variable: it conveys quantity - the price in dollars. For instance, the number of rooms.

Qualitative variable conveys a quality (east, west, north, south)

A quantitative variable can be described by a number for which arithmetic operations such as averaging make sense.

A qualitative (categorical) variable simply records a quality. If a number is used for distinguishing members of different categories of a qualitative variable, the number assignment is arbitrary.

The field of statistics deals with measurements – some quantitative and others qualitative. The measurements are the actual numerical values of a variable. Qualitative variables could be described by numbers, although such a description might be arbitrary; for example, N=1; E=2)

1.quantitative/ratio;

2.qualitative/nominal;

3.quantitative/ratio;

4.qualitative/nominal;

5 quantitative/ratio;

6.quantitative/interval;

7. quantitative/ratio;

8. quantitative/ratio;

9. quantitative/ratio;

10. quantitative/ratio;

11.quantitative/ordinal.