Applied Research Questions Paper

What are the similarities between descriptive and inferential statistics? What are the differences? When should descriptive and inferential statistics be used?

Basic features of the data within a study are what can be used to describe descriptive statistics. According to the text, "descriptive statistics in forms of means and standard deviations summarize what happened in the experiment as a function of the independent variables (age)" (Shaughnessy, Zechmeister, & Zechmeister, 2009, p. 425). Descriptive statistics are important because it allows researchers to present the data in a more meaningful manner. Typically, two general types of statistics are in use to describe data, which are measurements of control tendency and measures of spread. Inferential statistics occurs when a researcher is attempting to reach conclusions that extend beyond the available data. In addition, "statistical inference is both inductive and indirect" (Shaughnessy, et al., 2009, p. 415).

Furthermore, inferential statistics are used to make judgments on the probability that there is an observational difference between groups. "The differences are either a dependable one or one that happened by chance in the study" (Trochim, 2006). Descriptive statistics are different from inferential statistics because descriptive statistics describe what the data is showing. In contrast, inferential statistics attempt to reach a conclusion that goes beyond the presented data. Researchers would use descriptive data to present quantitive descriptions, which are in a more manageable form. Descriptive statistics also help to simplify large amounts of data in more sensible manner. Moreover, researchers would use inferential statistics to determine or judge the probability through observation of the differences between groups, "thus, we use inferential statistics to make inferences from our data to more general conditions" (Trochim, 2006).

What are the similarities between the case study method and the single-subject (small n) experimental designs? What are the differences? When should the case study and small-n research designs be used?

Case studies are an analysis of a person, which oftentimes are in-depth studies. This method of research can provide a vast amount of information about a specific individual. However, the results of a case study are difficult to generalize to mass populations. Because of this issue, case studies are often done in clinical research cases because certain areas of the subject's life cannot be duplicated. Single-subject experiments take place when a case is studied over a long period. "In single-subject experiments researchers typically measure one or more classes of performance of one or more subjects, over extended temporal intervals and visually inspect graphed data to determine whether and how these treatments controlled the performance of the individual subjects" (Dermer & Huch, 1999).

Case study and single-subject experimental designs share some similarities. First, both case studies and single-subject research involve multiple observations, which are repeated studies of the participant. Second, throughout the evaluation, process of both single subject and group designs the identical techniques and criteria are used. Both case studies and single-subject research have an important role in the identification and documentation of solutions for people with illnesses or disabilities. In addition, case studies are a form of descriptive research that identifies patterns for phenomena, which can generate a hypothesis for future research. In contrast, a single-subject research can provide a quasi-experiment approach, which is used to investigate the casual relationship between independent and dependent variables. Last, case studies are used to describe an individual's symptom and to understand and treat the symptoms, whereas researchers to describe behaviors because of manipulated treatments use single-subject experimental designs.

What are true experiments? How are threats to internal validity controlled by true experiments?

With the use of true experiments, subjects are randomly assigned to treatment conditions, which can be an excellent method to show cause and affect relationships. In addition, true experiments possess manipulation of variables "researchers will manipulate both treatment and comparison conditions and exercise a high degree of control" (Shaughnessy, et al., 2009, p. 338). A true experiment is one that can lead toward a definite result showing researchers exactly what may have caused an event to happen. Additionally, true experiments possess three main characteristics. The first characteristic is that some type of treatment or intervention is implemented. Second, the true experiment has a high degree of control, which means the researcher has control