Internal and External Validity.

Internal validity

According to Wimmer & Dominick (2011),
- control over research conditions is necessary to enable researchers to rule out plausible but incorrect explanations of results.

- Any such variable that creates a possible but incorrect explanation of results is called an artifact (also referred to as a confounding variable). The presence of an artifact indicates a lack of internal validity; that is, the study has failed to investigate its hypothesis.

- Example :-

• Suppose that researchers discover through a study that children who view television for extended periods have lower grade point averages in school than children who watch only a limited amount of television. Could an artifact have created this finding?
• It may be that children who view fewer hours of television also receive parental help with their school work; parental help (the artifact), not hours of television viewed, may be the reason for the difference in grade point averages between the two groups.

- Artifacts in research may arise from several sources. Those most frequently encountered are described next. Researches should be familiar with these sources to achieve internal validity in the experiments they conduct (Campbell & Stanley, 1963; Cook & Campbell, 1979).

• History - various events that occur during a study may affect the subjects' attitudes, opinions, and behavior.

• Maturation - subjects' biological and psychological characteristics change during the course of a study. Growing hungry or tired, or becoming older may influence how subjects respond in a research study.

• Testing - testing itself may be an artifact, particularly when subjects are given similar pretests and posttests. A pretest may sensitize subjects to the material and improve their posttest scores regardless of the type of experimental treatment given to them.

• Instrumentation - also known as instrument decay, this term refers to the deterioration of research instruments or methods over the course of a study. Equipment may wear out, observers may become more casual in recording their observations, and interviewers who memorize frequently asked questions might fail to present them in the proper order.

• Statistical regression - subjects who achieve either very high or very low scores on a test tend to regress to (move toward) the sample or population mean during subsequent testing sessions. Often outliers (subjects whose pretest scores are far from the mean) are selected for further testing or evaluation.

• Experimental mortality - all research studies face the possibility that subjects will drop out for one reason or another. Especially in long-term studies, subjects may refuse to continue with the project, become ill, move away, drop out of school, or quit work. This mortality, or loss of subjects is sure to have an effect on the results of a study because most research methods and statistical analyses make assumptions about the number of subjects used.

• Sample selection - most research designs compare two or more groups of subjects to determine whether differences exist on the dependent measurement. These groups must be selected randomly and tested for homogeneity to ensure that results are not due to the type of sample used.

• Demand characteristics - is used to describe subjects' reactions to experimental situations. According to Orne (1969), he suggests that under some circumstances subjects' awareness of the experimental purpose may be the sole determinant of how they behave; that is, subjects who recognize the purpose of a study may produce only "good" data for researches.

• Experimenter bias - Rosenthal (1969) discusses a variety of ways in which a researcher may influence the results of a study Bias can enter through mistakes made in observation, data recording, mathematical computations, and interpretation. Whether experimenter errors are intentional or unintentional, they usually support the researcher's hypothesis and are biased (Walizer & Wienir, 1978).

• Evaluation apprehension - Concept of evaluation apprehension is similar to demand characteristics, but it emphasizes that subjects are essentially afraid of being measured or tested.

• Causal time order - the organization of an experiment may create problems with data collection and interpretation. It may be that an experiment's results are not due to the stimulus (independent) variable but rather to the effect of the dependent variable.

• Diffusion or imitation of treatments - in situations where respondents participate at different times during one day or over several days, or where groups of respondents are studied one after another, respondents are studied one after another, respondents may have the opportunity to discuss the project with someone from another session and contaminate the research project.

• Compensation - sometimes individuals who work with a control group (the one that receives no experimental treatment) may unknowingly treat the group differently because the group is "deprived" of some thing. In this case, the control group is no longer legitimate.

• Compensatory rivalry - occasionally, subjects who know they are in a control group may work harder or perform differently to outperform the experimental group.

• Demoralization - control group subjects may literally lose interest in a project because they are not experimental subjects. These people may give up or fail to perform normally because they may feel demoralized or angry that they are not in the experimental group.