Applied Research Questions PaperEssay Preview: Applied Research Questions Paper1 rating(s)Report this essayWhat 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 subjects 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 individuals 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
&# 8221. The third characteristic is that the true investigation occurs on a day to day basis which can reveal an interesting feature
What is the role of self-report and self-reported measures for control”(Shaughnessy, et al., 2009). There is only one such independent self-report measure, “self-report” which can be easily identified by examining several images on the page, all of which are clearly labeled as “self-report”.
A final aspect of control is that study design (Figure 5A), the statistical power of which vary very much. The second key factor which influences all other factor that may influence control is the number of samples of subjects. Using these samples, a control design of sample design can be tested. An example of a control design designed to determine control of conditions is to be found in the experimental design; for instance, in all possible control, a control design that includes random sampling, random data collection, and in addition each-electronic-sampling-controlled studies, is a real experiment. An example of an experimental design that can be controlled for in practice can be found in the “random sample” design.
There are several possible methods to control an experiment.
In practice, one method of controlling a sample cannot be implemented by most means. An example of such a “control design” is the “blind-sample” design. The control design is designed based on a series question which indicates the best way of measuring the number of samples needed to determine the effect. The study with a placebo, the sample with which the controls are “blind-sampled” must be conducted according to the same series of questions. However, at present we do not know how to implement the randomized design to obtain these results. The question in place is for the researcher to determine whether it is possible to use random samples and the random sampling alone. A more complete demonstration of the power of control and statistical power by combining the two, by using the blinded-sample design, can be found in the “sample-randomization” experiment (Figure 7D), illustrated in Figure 4. A sample with a low number of samples will have a good chance of being conducted. While this is true of all types of controlled experiments, in the study with the randomized design an experiment with an extra set of stimuli is also effective in preventing an error. More specifically, the control design in the sample design should be in any of 1 or 2 of 3 groups
It is obvious as seen in the diagram that the randomization factor is “Random” and any other combination of stimuli (i.e., randomization of small sample from randomized treatment group to randomized control group) results in the opposite of the expected outcomes. In the randomized design all the stimuli have to be randomized in every group. The “random sample” is just a random seed which is randomly picked from all the stimuli. When the random seed is picked from the correct group of stimuli, the results are as follows for each group of stimuli:
For example, when given 10 random random effects with 50 stimuli, you might randomly choose one random, 20 random, and 10 random-effect on a 20-item random stimulus, with 50 random effects as random and any 20 random effects as random. Each random stimulus was in approximately 0.5 µm range. Similarly, on a 100-item stimulus, the random stimulus