INTRODUCTION TO SOCIAL SCIENCE RESEARCH PRINCIPLES AND TERMINOLOGY

Populations And Samples, Statistics And Parameters

• a population is an individual or group that represents all the members of a certain group or category of interest.
• a sample is a subset drawn from the larger population.
• statistics are values derived from sample data, whereas parameters are values that are either derived from, or applied to, population data.
• a population does not need to be large to count as a population.
• populations and samples dont have to include people.
• the researcher generally defines the population, either explicity or implicity.
• samples are not necessarily good representations of the populations from which they were selected.

Inferential And Descriptive Statistics

• it is important to determine which of these two populations is of interest in the study, because the consumer of the research must be able to determine how well the results from the sample generalize to the larger population.
• Descriptive statistics apply only to the members of a sample or population from which data have been collected.
• Inferential statistics refer to the use of sample data to reach some conclusions.

Types Of Variables and Scales of Measurement

• in social science research, a number of terms are used to describe different types of variables.
• A variable is pretty much anything that can be codified and have more than a single value. (income, gender, age, height, attitude about school, score  on a measure of depression, etc)
• A constant, in contrast, has only a single score.
• Types of variables include quantitative (continous) and qualitative (categorical).
• A quantitative variable is one that is scored in such a way that the numbers, or values, indicate some sort of amount.
• qualitative variables are those for which the assigned values do not indicate more or less of a certain quality.
• Most statistics textbooks describe four different scales of measurement for variables : nominal, ordinal, interval, ratio).
• variables scored using either interval and ratio scales, in contrast contain information about both relative value and distance)

Sampling Issues

• In statistics, the term random has a much more specific meaning than the common usage of the term.
• In statistics jargon, random means that every member of a population has an equal chance of being selected into a sample.
• The major benefit of random sampling is that any differences between the sample and the population from which the sample was selected will not be systematic.
• Representative sampling purposely selects cases so that they will match the larger population on specific characteristics.
• Convenience sampling generally selects participants on the basis of proximity, ease of access, and will willingness to participate.

Research Designs

• Experimental design, in this type of research, the experimenter divides the cases in the sample into diffferent groups and then compares the groups on one or more variables of interest.

• Correlational research designs, in this type of reesarch , participants are not usually randomly assigned to groups.

• Experimental research designs allow the researcher to isolate sprecific independent variables that may caue variation or changes in dependent variables.