The similarities and differences between descriptive and inferential statistics
Researchers in the field of psychology use two branches of statistics which are descriptive and inferential statistics in order to make some conclusions about the data and present the description of the sample’s peculiarities or their analysis. The similarities between descriptive and inferential statistics are in the fact that these two types of statistics operate the definite data which are the results of the observations or experiments conducted within the definite sample.
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The types of statistics are used to present the data in the certain form for the audience, for instance, as graphs and tables. The differences between descriptive and inferential statistics are in the character of working with the data. Descriptive statistics is used to summarize the data and organize some quantitative information. It is necessary to refer to descriptive statistics for providing the simple explanations of the facts. Inferential statistics is used for interpreting the data and making the definite conclusions about the hypotheses and the learnt information. It is significant for making the generalizations depending on a sample and for determining the possibilities for the future development of the processes (Coolican, 2009).
The similarities and differences between single-case and small-N research designs
Single-case and small-N research designs have a lot of similarities which are based on the number of the participants and the character of the observations. Thus, single-case research design is used for observing the performance of an individual in order to examine his or her behavioral peculiarities. The individual performance is determined in opposition to the possible observations of the group performance.
However, if single-case research designs are developed for observing individuals, small-N research designs are often provided for a small number of participants. In this case, the accents are also made on the individual performance of the participants without references to the group performance. Single-case research designs are important for examining the individual case in the clinical psychology. Small-N research designs are necessary for presenting a lot of observations on few subjects of the investigation, and the results of the research are used in the practical work with these subjects (Haslam & McGarty, 2003).
True experiments and threats to internal validity
True experiments are characterized by the fact that all the factors which can influence the observed phenomenon or the process are controlled. There are usually two groups of the participants which form the treatment and control groups. The participants of the groups are assigned randomly. It is typical for true experiments to manipulate an independent variable with treatment and with references to the comparison condition (Shaughnessy, Zechmeister, & Zechmeister, 2009, p. 376).
The threats to internal validity are history, maturation, subject mortality, testing, instrumentation, selection, and they are controlled by true experiments with the help of possible reducing the alternatives with references to the control groups and the high level of controlling all the variables (Shaughnessy, Zechmeister, & Zechmeister, 2009, p. 376). Experimental design can be discussed as the general notion for determining both the true and quasi experiments which are different in the degree of controlling and random assignment.
Quasi-experimental designs and their importance
Quasi-experimental designs have many similarities with true experiments, but they are different in the degree of controlling and possibilities of the random assignment. Quasi-experiments are important because they can be used when it is impossible for researchers to use the random assignment and provide the high level of control. Thus, quasi-experiments present the researchers a definite degree of flexibility. The similarities with the other experimental designs are in accentuating the outcome of the research, using two groups of participants. The differences are in the significance of threats to internal validity and in the degree of control (Jackson, 2011).
Coolican, H. (2009). Research methods and statistics in psychology. USA: Hodder Arnold Publishers.
Haslam, S. A. & McGarty, C. (2003). Research methods and statistics in psychology. USA: Sage Publications Ltd.
Jackson, S. L. (2011). Research methods and statistics: A critical thinking approach. USA: Wadsworth Publishing.
Shaughnessy, J. J., Zechmeister, E. B., & Zechmeister, J. S. (2009). Research methods in psychology. New York: McGraw Hill.