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Important Research Concepts
Research design largely affects the effectiveness of the work and the overall credibility of study results. When evaluating a research paper, it is important to pay attention to multiple elements of research construction including analysis and data collection tools, validity measures, theoretical backgrounds, etc. However, research questions and research variables are two of the most important concepts as they largely define the nature of the study and its orientation.
Research questions always guide the course of study (Creswell, 2009). There are three major types of research questions: relational, casual, and descriptive. By identifying the type of formulated questions, we can understand the purposes and aims of work. For instance, the objective of a study that employs relational research questions is the conception of answers meant to describe whether and how the analyzed phenomena or events are connected (Trochim, 2002). The causal questions help to establish if one or more variables provokes the evaluated outcomes (i.e., affects outcome variables) (Trochim, 2002). And lastly, descriptive or exploratory study questions allow us to obtain a deeper and more comprehensive understanding of phenomena, provide better definitions, or evidence needed to measure variables with greater accuracy and validity (Trochim, 2002).
Research questions are interrelated with the identification of research variables – the concepts having varying quality or quantity in a study (Kothari, 2005). There are a lot of types of study variables (continuous, discrete, control, criterion, etc.), but the independent and dependent ones are used most frequently in the experimental works. Dependent variables denote a presumed effect in a study, while the independent ones mark the supposed causes (Types of variables, n.d.). By analyzing these research concepts, scholars may obtain a more profound understanding of particular research works and implement the findings in practice or further studies in a more efficient way.
Descriptive Statistics and Statistical Inference
In modern research, two basic types of statistical methods are commonly distinguished: descriptive statistics and statistical inference. Descriptive statistics are usually implemented for the simple generalization of data obtained in the context of a particular research (Descriptive and inferential statistics, n.d.). But inferential statistics allow distributing the sampling data to the overall general population (Descriptive and inferential statistics, n.d.). In this way, it is possible to say that these two types of statistics are characterized by the different capacities to generalization.
The basic descriptive statistical methods mentioned in the posts are measures of central tendency and measures of variation, but such a tool as percentages is important as well. It can be used to bring the frequency distribution under a studied variable to the base of 100 or 1 (Holcomb, 1998). Comparing to the raw frequency distribution, the data represented in the form of percentage largely facilitates the analysis of results and, in this way, favorable impacts the overall course of research.
All these statistical tools allow researchers to summarize the data available in a sample. For instance, measures of central tendency (mode, median, and arithmetic mean) provide information regarding the typical and central value of the distribution. Mode identifies the most common value, median – the average value, and arithmetic mean – the most expected value (Holcomb, 1998). Overall, the central tendency measures are the statistical indicators of typical properties associated with empirical data. They can answer such questions as “what is the average level of student’s intelligence?,” “what is the typical index value of nurses’ responsibilities?,” and alike. The implementation of the central tendency measures is necessary but not sufficient for high-quality research. To improve the quality of work, it is also important to use the measures of variability.
Overall, measures of variability denote the degree of heterogeneity of distribution. The authors of the posts describe such measures of variability as standard deviation and dispersion but do not mention such tools as a range. Range (R) can be regarded as the most simple test method in terms of both received information and calculation – it equals the difference between the largest and the smallest distribution values (Lane, n.d.). However, comparing to dispersion and deviant distribution which are the most commonly implemented variability measures, the range is rarely used in research.
Descriptive and inferential statistical methods applied in quantitative research help scholars to obtain the numerical data, increase the overall objectivity of conclusions, and achieve a higher level of accuracy (Creswell, 2009). The discussed tools and methods may substantially support researchers in identifying links and relations between the variables. However, the use of statistics does not eliminate the risk of data biasing. Thus, it is essential to conduct research and select study constructs in the agreement with all statistical and scientific rules. To do so, a researcher needs to measure construct and internal validity before conducting the main research (Shuttleworth, n.d.). For example, it is possible to complete a small pre-test to understand whether the designed research program will measure the intended attributes, or establish the temporal precedence of estimated cause and effect through evaluation of previous studies (Shuttleworth, n.d.). In this way, researchers may significantly reduce the risks of error occurs when handling statistical data and achieve a high level of result credibility.
Reflective Essay
The knowledge obtained through the investigation of research methodology largely assists in writing their own research papers. The session helped me to develop and enhance my skills in research design and selection of appropriate study methods. By performing various activities aimed at the evaluation of research works and concepts, I have significantly improved critical thinking skills and achieved a greater level of understanding of research methodology. Critical thinking is essential for creating an adequate semantic and logical content of a research paper. Moreover, it implies the ability to evaluate and analyze the arguments and claims to ensure they are formed consistently with scientific principles. Therefore, due to the progress I have achieved during the session, I feel more confident that I can make my own high-quality research study.
It is possible to say that I have understood all topics introduced during the course because the instructor was very supportive and quick in answering my questions and dispelling all doubts that I had. I think I performed all activities well and tried my best to achieve excellent outcomes. Overall, I think I did a good job in all assignments, but my oral communication skills need some more improvement because I was not completely satisfied with the sound effects in the narrated PowerPoint presentation activity. However, I will achieve better results in this area by practicing more.
In my opinion, I have attained the expected course outcomes. All the completed assignments have contributed to my academic growth and knowledge development. I think the skills acquired during the course will be very helpful in the achievement of my professional objectives as well.
References
Creswell, J. (2009). Research design: Qualitative, quantitative and mixed methods approaches. Thousand oaks, CA: Sage.
Descriptive and inferential statistics. (n.d.). Web.
Holcomb, Z. C. (1998). Fundamentals of descriptive statistics. Los Angeles, CA: Pyrczak Publishing.
Kothari, C.R. (2005), Research methodology – Methods and techniques. New Delhi: Wiley Eastern Limited.
Lane, D. M. (n.d.). Measures of variability. Web.
Shuttleworth. (n.d.).Internal validity. Web.
Trochim, W. (2002). Research methods knowledge base. Web.
Types of variables. (n.d.). Web.
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