PSY325: Statistics for the Behavioral & Social Sciences Discussion responses

timer Asked: Aug 10th, 2014

Question Description

Guided Response:  Review your classmates’ posts.  Respond substantively to three of your peers.  Do you agree or disagree with their interpretations of correlation and cause?  How strong or were their explanations? What might you suggest they do to strengthen their arguments?

Guided Response.docx

Guided Response: Review your classmates’ posts. Respond to at least three of your classmates. Do you agree or disagree with the test selected by your peer? How does the number of groups being compared affect the statistical analysis? What suggestions would you make for change or improvement? Why would these suggestions potentially be more useful? Response 1 Lisa The first thing that I would do would be to recommend an ANOVA (Analysis of Variance) for the research study of the new blood pressure medication. The reason being would be because the one tailed t-test is more likely to be used for the prediction of the direction something will go and the z-test indicates the distance the sample mean is from the standard error of the mean for different groups. This would depend on the different variances that I would allow in the data set as well as if the test was directional or non-directional, for this set it is important to know that my test is non-directional in means of how the information will play out with the hypothesis. The test would have three groups; group A would be my placebo group and group B would be given the new blood pressure medication while group C would stay on the drug Lisinopril. Out of the three groups; group A & B would come off of their medication doses while Group C stayed on current medication and dosage. This test would be effective for a week with the hypothesis being this: Is the new medication more effective for the control of blood pressure or is it the same as what the standard is on a dosage of 20mg... This would be controlled by daily blood pressure readings as well as every other day doctor visits with the patients recording all feelings and blood pressure readings. Our test will need to be two-tailed to record the different information; this will be easier to detect the different means from the two different medications that we will be utilizing. The null hypothesis in this test would be the group that was on the new blood pressure medication would have the same effect as the group that was utilizing the medication Lisinopril for blood pressure control, and the placebo group showed to much increase in blood pressure readings. Response 2 Jessica Many people get away with eating whatever they want since they have medicine to take care of their high blood pressure. A new drug on blood pressure, could affect people with headaches, nausea, and fatigue. Their eating habits could affect those symptoms, depending on the number of times they have eaten, a day of the week... 2 or 3 times a day in a week. Twenty five people have chosen the most symptoms they have during the week. ANOVA would be recommended for this analysis because two groups will have the same mean out of twenty five people, leaving one group to not have the same mean, known as the null hypothesis (Tanner 2011). The alternative hypothesis would be the groups not being equal. One would have more number of individuals in the group that have chosen under the category of symptoms. Not every group going to have the same amount of mean because of different sums in a week, by those individuals. They hypothesis could be non-directional because even though most symptoms are chosen, does not mean that a greater amount of time each individual have eaten in that symptom group, determines the difference of the symptoms being severely two-tailed. There have to be more variables added, such as the type of foods, main foods that causes these symptoms in order for the hypothesis to make more sense as an alternative hypothesis tested to be a one-tailed test that states either H1: μ > μ0 OR H1: μ < μ0 (Stone, 2006). Stone & Ellis (2006 ). 1- vs 2-Tailed Tests. One-tailed tests. Retrieved from Tanner, D. (2011). Statistics for the Behavioral & Social Sciences. San Diego, CA: Bridgepoint Education, Inc. Response 3 Mechele A researcher wishes to study the effect of a new drug on blood pressure… My hypothesis for this research question would be: Drug X will have an effect on blood pressure. I would choose ANOVA (analysis of variance). There is only one independent variable and several possible outcomes. The hypothesis states the new drug will have an effect on blood pressure. The drug could have no effect on blood pressure, it could raise blood pressure, or it could lower blood pressure. Instead of doing separate T tests on all possible outcomes, ANOVA allows for the type of analysis needed for this particular hypothesis. The fact that we are working with humans and their reaction to drugs allows for a high probability of a multitude of variances and variables ANOVA is the best test. “When researchers work with human subjects, some level of error variance is inescapable. Even under tightly controlled conditions where all members of a sample receive exactly the same treatment, the subjects are unlikely to respond the same way”. (Tanner, 2011, p. 152) ANOVA is designed to analyze all of these variances. My study group would consist of 100 participants all who have tracked their blood pressure for a period of three months and document the pattern of blood pressure readings for comparison. Anyone currently on blood pressure medication would need to be eliminated. My hypothesis is non directional. It states the drug will have an effect but doesn’t say how. If my hypothesis was to state the new drug will lower blood pressure it would be directional. In the case of my second example, the hypothesis states the specific change therefore making it directional. This test would be a two-tailed test, regardless of the direction of the relationship you hypothesize; you are testing for the possibility of the relationship in both directions. When charted on the curve it will be important to see there is no change, an increase or a decrease. A null hypothesis says there will be no observed effect from the experiment. It is the researchers attempt to disprove his/her theory if the theory is that there will be change. Null hypothesis is symbolized as H0. The null hypothesis would be written out as H0: μ1 = μ2 = μ3. The alternative hypothesis is the opposite of the null hypothesis. It is what the researcher thinks will really be the outcome. It is an attempt to disprove the null hypothesis. It speculates there will be an observed effect from the experiment. “Because the several possible alternative outcomes multiply rapidly when the number of groups increases, a more general alternate hypothesis is given. Either all the groups involved come from populations with the same means, or at least one of them does not”. (Tanner, 2011, p. 152) The simplified general alternative hypothesis is written out as, HA: not so. Reference Tanner, D. (2011). Statistics for the Behavioral & Social Sciences. San Diego, CA: Bridgepoint Education, Inc.

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