Analysis of Psychology - Article Example

a. Summarizing data pertains to the act of describing the information that has been collected from an investigation. This activity may involve the use of statistical tests that would determine whether there is any correlation between two variables, such as when an increase in one factor may influence the increase in a second factor. It should be understood that correlations may be described through the change in a particular value, wherein an increase would signify a positive correlation, while a decrease would indicate a negative correlation. In the case where an increase in the value of one variable would result in the decrease in the value of the second variable, then an inverse correlation is occurring. Summarizing the data also involves the determination of significance of the results, which means that the data gathered from the investigation did not simply happen by chance. Significance also reflects the robustness of the data gathered from the research study. Summarizing the data also allows the researcher to find any patterns, trends and motifs that are commonly observed during a particular situation such as that investigated in the study. On the other hand, the process of confirming the data involves the validation of the hypotheses of the study. Based on the information gathered from the investigation, as well as the analysis performed for robustness and significance, it is possible to know whether the initial hypothesis should be accepted or refuted. Confirming data thus allows the researcher judge the information that was collected from the investigation. This process also provides a way for the investigator to determine whether the results are indeed credible, as evidenced by the statistical tests that were performed when the data was being summarized. The processes of summarizing and confirming the data are two separate steps generally employed in research investigations yet each is highly dependent on the other. It is thus important to perform both steps in sequence in order to attain a highly reliable research output. b. The Null Hypothesis Significance Testing allows the researcher to determine whether the results obtained from the investigation was truly based on the effects of the variables being studies. The most important component of a research investigation is the formulation of the hypotheses that would serve as the basis of the investigation. In most studies, a certain association or correlation is identified as a research topic of interest, such as that of the effect of a particular scenario that results in a specific response. The null hypothesis is regarded as a type of hypothesis that states that there is no association or correlation between two variables. On the other hand, the alternative hypothesis describes the opposite of the null hypothesis, wherein there is indeed a correlation or association between the two variables. In order to determine whether the null hypothesis should be accepted or rejected, it is thus important to test the data gathered from the investigation using statistical tests such as the t-test or the chi-square test. These tests allow the researcher to determine that the data gathered were not generated simply by chance. These tests allow the calculation for P values, which represent the significance of the results. A significant result would show a P value of at least 0.05, which shows that 95% of the time a certain pattern will be observed if the experiment is repeated using the same settings. For example, the administration of an anti-depressant drug is hypothesized to result in the alleviation of depression symptoms in a patient. The null hypothesis of this study would state that the administration of the drug would not result in the alleviation of the symptoms. The alternative hypothesis, on the other hand, would result in the alleviation of the symptoms. If the study population experienced a better condition after taking the drug, then a Type I error or rejection of the null hypothesis occurs. The opposite condition would then involve a Type II error. c. True experiments pertain to investigatory situations that involve independent and dependent variables. In a true experiment, it is important that the independent variable can be controlled by the investigator because this will allow the monitoring of the response or influence of a specific condition on another variable. For example, the increase in the dose of a specific drug may be varied by an investigator in order to determine whether a specific dose will be harmful or beneficial to a patient. If this condition is not attained by an investigation, then it is highly likely that the results of the study would result in erroneous results. In addition, the conclusions of the study would not be credible. The application of statistical tests such as the t-test or the chi-square test would also result in insignificant P values, suggesting that the results were only obtained by chance. Another characteristic of a true experiment is the systematic control of the independent variable. With this feature, the investigator has an idea of the extent of changes that are occurring in the experiment. For example, the investigator has to know the amount of a specific drug that was administered to patient, resulting in a specific response. It is unacceptable to find an investigation that simply states that the dosage of a drug was doubled or increased, with the actual concentration remaining to be unknown. Another characteristic of a true experiment is that a control situation should be present in order to compare the results of the test situation to that of the natural or normal situation. For example, if a drug of a specific dosage is given to a patient, then a control group should involve a patient with the same medical condition that does not receive the test drug being investigated. d. Quasi-experiments are investigations that involve conditions wherein the characteristics of a true experiment could not be achieved. For example, it may be quite difficult to achieve a certain condition that involves responses of individuals with regards to the approaching death of a patient. Since there are ethical considerations that need to be considered with regards to such investigations, the next best possible approach to such studies are quasi-experiments. When performing quasi-experiments, it is important to determine the actual details of the scenario in order to establish whether a certain study participant may be grouped with another individual of the same setting. Quasi-experiments are thus important because it allows the researcher to perform a study that is generally difficult to perform using the requirements of a true experiment. Since quasi-experiments are not exactly true experiments, it is important for these types of investigations to have strong procedures with regards to the control groups. In addition, any possible threats to the validity of the investigation should be identified and addressed. This consideration will allow the researcher to better analyze the data that would be gathered from the research study. It is also important that establish a strong criteria for inclusion of study participants for the investigation. The length or duration of investigation should also be maturely established in a quasi-experiment because it may be possible that the observations collected during the investigation may not be reliable anymore if an extended duration of time is followed. It is also important for the researcher to be highly sensitive to any changes in the results, as the conditions of a quasi-experiment may sway the results to a particular trend and generate erroneous results. Read More