What are the similarities between descriptive and inferential statistics? What are the differences? When should you use descriptive and inferential statistics?
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Descriptive and inferential statistics have various similarities that can be explored. Most notably, they can be used in the analysis of data without any problem which leads to the drawing of conclusions in relation to results that have been evaluated (Campbel, 2002, p. 24). Descriptive and inferential statistics can provide information about specific aspects like data which is a good similarity. In this case, it can revolve around a population that is used to explain any group of data that one is interested in. All this can be done with an aim of coming up with meaningful conclusions. It should be understood that descriptive and inferential statistics can be used to make judgments in relation to probability. In fact, the observed difference between different groups of data can be evaluated. Both statistics can be used to look at a given sample from some population that was supposed to be evaluated.
The difference between descriptive and inferential statistics revolves around what they do with a given sample. In this case, it can be said that descriptive statistics is mostly concerned with summarization of samples. This is always done by using various and existing statistical measures like the median and average. On the other hand, inferential statistics is mostly concerned with drawing conclusions. These conclusions are always about the population from the sample in question. For instance, the success rate of a given aspect can be evaluated based on the sample (Robson, 1993, p. 46). Rational deductions from data are made in inferential statistics while evaluation and description of data is done in descriptive statistics. Descriptive and inferential statistics can be used in different circumstances. Inferential statistics can be used to look at data so that it gives general conditions. On the other hand, descriptive statistics should be used to reveal different patterns. This is mostly done through the analysis of numerical data.
What are the similarities between single-case and small-N research designs? What are the differences? When should you use single-case and small-N research designs?
Single-case and small-N research designs are used in various ways and this means that they have some similarities. In fact, some of these similarities enhance research from different perspectives. One rare similarity between single-case and small-N research designs is their ability to be used effectively in researching some rare phenomena. In this case, they are very effective when an unknown condition is being studied. This is mostly practicable where we have a lot of participants to draw from. Single-case and small-N research designs will come in handy when there is need to access information in different dimensions. The efficiency of various research designs can be evaluated through experimental designs (Bradley, 2007, p. 19). In the long run, various elements will be integrated in the design to demonstrate control. Both designs are observing cases in the sense that there are some idiographic characteristics.
When it comes to differences, single-case design is mostly used in psychology. This is normally done to know the meaning of various social activities. Single case therefore combines various aspects that need to be integrated together to produce a good and meaningful outcome. Small-N research design requires a lot of information and clinical observation to come to conclusions. In the process, the research that was desired can be achieved for long term sustainability. There is some limited validity when it comes to single-case design which is the direct opposite of small-N research design. This is as a result of the process and procedures that are used in these two aspects (Campbel, 2002, p. 58). In fact, Small-N research is very simple and that is why you can easily understand the outcome and process. Single-case design should be used to draw scientific conclusions by insisting on basic aspects. This means that they should be used to investigate a specific phenomenon. Small-N research design should be used as a prerequisite of unearthing valid facts in relation to research.
What are true experiments? How are threats to internal validity controlled by true experiments? How are they different from experimental designs?
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True experiments can be described as experiments that are characterized by two kinds of variables. This means that it has more than one group that is created with a specific reason. In the process, there are common outcomes that are achieved through some random assignments. There are certain circumstances under which true experimental designs can occur. Most notably, they occur when samples are randomly assigned. In the process, the experiment that is being undertaken can be considered a true design (Robson, 1993, p. 34). There are threats to internal validity meaning that they should be controlled by true experiments. True experiment controls such threats by ensuring that the control group is not exposed in any way. In this case, key aspects that relate to the program in question should not exit. This means that they should not move away from the comparison group that was supposed to be evaluated.
Threats are controlled by limiting the narrow range through the evaluation process that true experiments are supposed to address. In addition, there are also cases where a researcher might try to influence results. This is threat that has been controlled by defining dependent variables effectively. True experiments differ with experimental designs in various ways. This is based on different aspects that can be evaluated in relation to research. True experiments therefore differ with experimental designs based on how the population is designed. Differences can also be seen from how the outcome is evaluated and discussed. True experiments always isolate causes while experimental designs tend to come up with repeated measures.
What are quasi-experimental designs? Why are they important? How are they different from experimental designs?
Quasi-experimental designs revolve around selecting groups which enables a given variable to be tested. This is normally done without undergoing any pre-selection processes that might be random in nature. In a broad perspective, quasi-experimental designs are used in the evaluation of different educational programs. This is mostly the case, when a random assignment can not be practical in any way. In fact, they can be effectively described as posttest only. These designs have also been explained based on the fact that they do not have any control over the allocation of different treatments (Campbel, 2002, p. 32). It can be boldly said that quasi-experimental designs can be frequently implemented. In the process, group differences can later on be evaluated in relation to specific aspects that were supposed to be looked at. Quasi-experimental designs are very important because they can be used when randomization is not possible.
These designs are also important because they are easy to set up than others like true experimental designs. They minimize threats meaning that there will be no problems with the natural environment. This therefore reduces different threats to external validity that might be encountered. Generalizations about population can be easily made by looking at different settings and subjects which is important in design. This method is very important because it ultimately assists in longitudinal research by enhancing efficiency. Longitudinal research takes a lot of time in different environments thereby justifying the use of quasi-experimental designs (Robson, 1993, p. 68). Quasi-experimental designs are different from experimental designs because they are used when randomization is impracticable. In fact, they are easier to set up which is not the case with experimental designs.
Bradley, N. (2007). Marketing Research: Tools and Techniques. Oxford: Oxford University Press.
Campbell, T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. New York: Houghton Mifflin Company.
Robson, C. (1993). Real-world research: A resource for social scientists and practitioner researchers. Malden: Blackwell Publishing.