Choosing Between a Nonparametric Test and a Parametric Test It’s safe to say that most people who use statistics are more familiar with parametric analyses than nonparametric analyses. Nonparametric tests are also called distribution-free tests because they don’t assume that your data follow a specific distribution. You may have heard that you should use nonparametric tests when your data don’t meet the assumptions of the parametric test, especially the assumption about normally distributed data. That sounds like a nice and straightforward way to choose, but there are additional considerations. In this post, I’ll help you determine when you should use a: • Parametric analysis to test group means. • Nonparametric analysis to test group medians. …show more content…
Reason 2: You have a very small sample size If you don’t meet the sample size guidelines for the parametric tests and you are not confident that you have normally distributed data, you should use a nonparametric test. When you have a really small sample, you might not even be able to ascertain the distribution of your data because the distribution tests will lack sufficient power to provide meaningful results. In this scenario, you’re in a tough spot with no valid alternative. Nonparametric tests have less power to begin with and it’s a double whammy when you add a small sample size on top of that! Reason 3: You have ordinal data, ranked data, or outliers that you can’t remove Typical parametric tests can only assess continuous data and the results can be significantly affected by outliers. Conversely, some nonparametric tests can handle ordinal data, ranked data, and not be seriously affected by outliers. Be sure to check the assumptions for the nonparametric test because each one has its own data
For this experiment the null hypothesis is that the intensity of the step rate test (High and Low) has no effect on the persons’ heart rate and recovery time. While the alternate hypothesis is that the intensity of the step rate test (High and Low) has an effect on the persons’ heart rate and recovery time.
Inferential Statistics has two approaches for making inferences about parameters. The first approach is the parametric method. The parametric method either knows or assumes that the data comes from a known type of probability distribution. There are many well-known distributions that parametric methods can be used, such as the Normal distribution, Chi-Square distribution, and the Student T distribution. If the underlying distribution is known, then the data can be tested accordingly. However, most data does not have a known underlying distribution. In order to test the data parametrically, there must be certain assumptions made. Some assumptions are all populations must be normal or at least same distribution, and all populations must have the same error variance. If these assumptions are correct, the parametric test will yield more accurate and precise estimates of the parameters being tested. If these assumptions are incorrect, the test will have a very low statistical power. This will reduce the probability of rejecting the null hypothesis when the alternative hypothesis is true. So what happens with the data is definitely known not to fit any distribution? This is when nonparametric methods are used.
(b) Psychologists do not base such decisions or recommendations on tests and measures that are obsolete and not useful for the current purpose.
First, all data have both an objective and a subjective component. Numbers can be easily assigned to all qualitative data (such as open-ended questions in surveys), and any number obtained by a quantitative study is interpreted using a subjective or qualitative judgment. Second, using differen...
Thesis Statement: The most differences between the idiographic and nomothetic approach are measurements and development.
The father of quantitative analysis, Rene Descartes, thought that in order to know and understand something, you have to measure it (Kover, 2008). Quantitative research has two main types of sampling used, probabilistic and purposive. Probabilistic sampling is when there is equal chance of anyone within the studied population to be included. Purposive sampling is used when some benchmarks are used to replace the discrepancy among errors. The primary collection of data is from tests or standardized questionnaires, structured interviews, and closed-ended observational protocols. The secondary means for data collection includes official documents. In this study, the data is analyzed to test one or more expressed hypotheses. Descriptive and inferential analyses are the two types of data analysis used and advance from descriptive to inferential. The next step in the process is data interpretation, and the goal is to give meaning to the results in regards to the hypothesis the theory was derived from. Data interpretation techniques used are generalization, theory-driven, and interpretation of theory (Gelo, Braakmann, Benetka, 2008). The discussion should bring together findings and put them into context of the framework, guiding the study (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). The discussion should include an interpretation of the results; descriptions of themes, trends, and relationships; meanings of the results, and the limitations of the study. In the conclusion, one wants to end the study by providing a synopsis and final comments. It should include a summary of findings, recommendations, and future research (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). Deductive reasoning is used in studies...
Quantitative research is the oldest form of research; it is incredibly formal, stemming from positivism paradigm, or the outside looking in approach. The method is about trying to establish cause and affect relationships between variables. This method can be considered non biased as the researcher isn’t us...
If the test cases have to be run a small number of times then manual testing is more likely to be performed.
The methods used to determine whether the samples are different are Paired Comparison test, Duo-Trio test and Triangle test. Paired Comparison method is the method used to determine whether the two samples differ in a specified dimension such as sweetness or crispness. The two samples are presented to the panelist simultaneously and the panelist is asked to identify the sample that is higher in the specified sensory attribute.
Reliability (extent to which a test yields consistent results, as assessed by the consistency of scores on two halves of the test, on alternate forms of the test or on retesting)- Comparing test scores to those of the standardizing group still won't tell us much about the individual unless the test has reliability.
Quantitative studies are primarily numbers based. They deal with large cohort groups as well as analyze large amounts of data. “A quantitative researcher typically tries to measure variables in some way, perhaps by using commonly accepted measures of the physical world (e.g., rulers, thermometers, oscilloscopes) or carefully designed measures of psychological characteristics or behaviors (e.g., tests, questionnaires, rating scales)” (Leedy & Ormrod, 2010, p. 94).
...entity beyond a reasonable doubt, then neither could a second test on an even smaller sample.”
On the other hand, quantitative research allows you to test hypothesis derived from theories, associated with the issues being investigated. It is less flexible, as there are standardized procedures and techniques for collecting, organizing and analyzing the data (Kuada, 2012).
Qualitative and quantitative research methods take different approaches to gathering and analysing information. Whether it is a qualitative or quantitative study, the research study begins with a question or series of questions. Both use rigorously designed studies to get the most accurate, detailed and complete results. Qualitative studies common methods are interviews, surveys and observation. A qualitative study aims to provide a detailed description of the study results, often using pictures and written descriptions to describe what the research revealed. A qualitative study looks at the big picture, helping researchers to narrow in on points of interest that then can be followed up on in a quantitative study. While a quantitative study has a narrower focus, it attempts to provide a detailed explanation of the study focus, along with this using numbers and statistics. And the results from a quantitative study can reveal bigger questions that call for qualitative study. Or vice versa a qualitative study may reveal at analysis that a more focus and direct approach may be needed. With both methods analysis is a key part of any study whether qualitative or quantitative.
Qualitative and quantitative researcher’s exhibited conflicting ways of approaching a research. Some researchers prefer qualitative over quantitative and vice-versa. Also, it is common for qualitative and quantitative to be used together in a research. But, both methods when carried out correctly provide good research. Plus, both methods have their own unique differences and characteristics. In this paper I will define three of these characteristics in a quantitative and qualitative research design and discuss and compare their differences. (Smith & Davis, 2010)