Discuss about the Research and Communication for Valid Ways of Representing Data.
The method used in the question is not only appropriate. The data collected can be represented by the use of central tendency and the distribution tables and illustrations (Palinkas, Horwitz, Green, Wisdom, Duan, & Hoagwood, 2015). The application of central tendency involves calculating the mean, mode, and the median. Upon analyzing the available data mean is the most suitable to represent this data. The right approach will be summing up all the measurements and diving it by the number of measurements. That is (10 + 50 + 51 + 128 + 80)/5 = 63.8. Alternatively, considering the number of weights used in the study arriving at the summary score using mode can also be appropriate. Again, this data can be illustrated using pie charts, bar, and line graphs, and scatter diagrams (Slutsky, 2014).
Similarly, the researcher may use a liker scale to represent data. It is simple to use, time saving and multi-dimensional (answers the question giving five to seven options). However, they focus on two response sides like agree or disagree. They as well fail to measure respondents’ attitudes. The respondent is restricted on the given choices. This means that the researcher will use the summary score to represent the data. For this question, a bar and line graphs, histogram, frequency table and a pie chart would apply.
Types of Quantitative Data
The respondents will give their responses stating whether the color is yellow, black, red, green and blue. The suitable method for color is the nominal way. Just as the name suggests, respondents give the names of the variables. They could be names of persons, animals or colors. The researcher will then tally which color has a higher frequency.
They show a certain order of performance in a particular course. Ordinal scales are used to show the most significant variables all the way to the least important (Cliff, 2014). Utilizing that rule, course grades can be classified by the use of the ordinal method. Where, C may be considered as excellent, D for good and perhaps P for poor.
Overall Course Score
Milfont, & Fischer, (2015) narrates that ratio scales give ultimate order for surveys. If a student scores thirty percent that would be a ratio but in a percentage form. It can be concluded that course scores which are in most cases in whole numbers can be presented by the use of ratios. The scores are expressed in to percentages or to the total number of marks allocated for the task.
The Kelvin thermometer is a device used to measure temperature by use of degree Celsius labeled on intervals of 10 degrees (Weng, & Luiten, 2015). Therefore, the best method to apply when collecting data from the instrument is by the use of intervals. For example, the data obtained could be between thirty to forty degrees.
Study of Lecture Attendance
Dependent and Independent Variables
This paper holds that the number of students is the dependent variable while the time allocated for the lectures is an independent variable. Fixed variables do not change and include number of days, time in hours and also ages (Creswell, 2013). The dependent variables are influenced by the fixed ones. For this case, students won’t attend the 8:30 lectures fully due to the times allocated. However, the will all attend the afternoon ones due to favorable time allocated to the classes.
These are variables which do not affect either the dependent or the independent variables. Their occurrence has no impact on the other variables. In our case, the lecturers’ attendance is one of them. Whether the lecturer comes or fails to come, most students will still miss the morning and evening classes. Once they know there won’t be a class, they won’t come.
Secondly is the area of residence of the students. Those living far may always fail to attend the morning and leave early, therefore, missing the evening classes. That interferes with the validity of the results.
Type of Quantitative Study
The type used is the descriptive non-experimental. The methods state the researcher cannot alter or manipulate the variables, he or she has to interact and observe the variables. Therefore, the hypothesis arrived at the end of the study is based on the observations made from the variables by the researcher.
The design is good for testing hypothesis. That is a proposal, a theorem or a proposal about an explanation but it is yet to be justified. At the end, the researcher concludes whether it is alternative or null. The alternative analysis entails what the researcher hopes to prove it is true. On the other hand, null hypothesis specifies that there are no observable effects in the experiment. Researchers attempt to seek evidence against the established hypothesis test. This study is non-experimental showing that the researcher has no control over the variable. Therefore, the hypothesis generated at the end will be based on the researcher’s observation but not the statistical data.
Cliff, N. (2014). Ordinal methods for behavioral data analysis. Psychology Press, 12-17
Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods Approaches. Sage publications. Pp. 4-12
Milfont, T. L., & Fischer, R. (2015). Testing measurement invariance across groups: Applications in cross-cultural research. International Journal of psychological research, 3(1), 111-130.
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015).
Purposeful sampling for qualitative data collection and analysis in mixed method Implementation research. Administration and Policy in Mental Health and Mental Health
Services Research, 42(5), 533-544.
Slutsky, D. J. (2014). The Effective Use of Graphs. Journal of wrist surgery, 3(02), 067-068.
Weng, W., & Luiten, A. N. (2015). Ultra-sensitive thermometer based on a compact optical Resonator. Temperature, 2(1), 36-37.