validity and reliability in quantitative research ppt

In the absence of the data that would be necessary to interpret these presidential job-approval numbers, it’s difficult to say why people approve or disapprove of the job that President Obama is doing. Figure 1-Quantitative data for Gallup’s presidential approval poll For example, Gallup polls commonly provide data about approval rates for the President of the United States, as shown in Figure 1, but don’t provide the crucial information that we would need to interpret that data.

Quantitative studies’ great strength is providing data that is descriptive-for example, allowing us to capture a snapshot of a user population-but we encounter difficulties when it comes to their interpretation. Quantitative studies often employ automated means of collecting data such as surveys, but we can also use other static methods-for example, examining preferences through two-alternative, forced-choice studies or examining error rates and time on task using competitive benchmarks. Multivariate statistics like the MRC or stepwise correlation regression break the data down even further and determine what factors-such as variances in preferences-we can attribute to differences between specific groups such as age groups. Statistical analysis lets us derive important facts from research data, including preference trends, differences between groups, and demographics. These include descriptive statistics like the mean, median, and standard deviation, but can also include inferential statistics like t-tests, ANOVAs, or multiple regression correlations (MRC). Because the data is in a numeric form, we can apply statistical tests in making statements about the data. Quantitative studies provide data that can be expressed in numbers-thus, their name.

Validity and reliability in quantitative research ppt