External Validity - Mass Media Research.pptx

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How well the results of a study can be generalized across populations. A study that lacks external validity cannot be projected to other situations; it is valid only for the sample tested. Usually associate to sample selection. Use random samples. Use heterogeneous samples.


 How well the results of a study can be generalized
across populations.
 A study that lacks external validity cannot be
projected to other situations; it is valid only for the
sample tested.
 Usually associate to sample selection.
 Use random samples.
 Use heterogeneous samples.
10/14/2022
External Validity
Muhammad Awais
(facebook.com/awwaiis)

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Quantitative research uses a static or standardized set of questions. All respondents are asked the same questions. Although follow-up questions (and skips) can be designed into a questionnaire, they must be included in the questionnaire or measurement instrument before the research project begins. Interviewers conducting the interview are not allowed to stray from the questionnaire. Qualitative research uses a flexible questioning approach. Although a basic set of questions is designed to start the project, the researcher can change questions or ask follow-up questions at any time.

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Content analysis alone cannot serve as the basis for making statements about the effects of content on an audience. A study of Saturday morning cartoon programs on television might reveal that 80% of these programs contain commercials for sugared cereal, but this finding alone does not allow researchers to claim that children who watch these programs will want to purchase sugared cereals. An additional study of the viewers is necessary

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There are two broad types of error present in all research: Sampling error: or error related to selecting a sample from a population. A sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data and the results found in the sample do not represent the results that would be obtained from the entire population. Sampling error can be reduced by increasing the sample size. If the sample size n is equal to the population size N, then the sampling error is zero. Non-Sampling error: It is an error created by every other aspect of a research study, such as measurement errors, data analysis errors, the influence of the research situation itself, or even error from an unknown source that can never be identified and control or eliminated. Random errors are (like the name suggests) completely random. They are unpredictable and can't be replicated by repeating the experiment again.

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Perhaps the most commonly used scale in mass media research is the Likert scale, also called the summated rating approach. A number of statements are developed with respect to a topic, and respondents can strongly agree, agree, be neutral, disagree, or strongly disagree with the statements. Each response option is weighted, and each subject’s responses are added to produce a single score on the topic.

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Thurstone scales are also called equalappearing interval scales because of the technique used to develop them and are typically used to measure the attitude toward a given concept or construct. To develop a Thurstone scale, a researcher first collects a large number of statements (Thurstone recommends at least 100) that relate to the concept or construct to be measured. Next, judges rate these statements along an 11-category scale in which each category expresses a different degree of favorableness toward the concept. The items are then ranked according to the mean or median ratings assigned by the judges and are used to construct a questionnaire of 20 to 30 items that are chosen more or less evenly from across the range of ratings.

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Exploratory factor analysis is a statistical technique that is used to reduce data to a smaller set of summary variables and to explore the underlying theoretical structure of the phenomena. It is used to identify the structure of the relationship between the variable and the respondent. Confirmatory factor analysis (CFA) is a multivariate statistical procedure that is used to test how well the measured variables represent the number of constructs. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) are similar techniques, but in exploratory factor analysis (EFA), data is simply explored and provides information about the numbers of factors required to represent the data. In exploratory factor analysis, all measured variables are related to every latent variable. But in confirmatory factor analysis (CFA), researchers can specify the number of factors required in the data and which measured variable is related to which latent variable. Confirmatory factor analysis (CFA) is a tool that is used to confirm or reject the measurement theory.

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