1. What Is Quantitative Data Analysis?: Engr. Tyrone John B. Diaz Res290-ol Chapter 14 - Ecq

Engr. Tyrone John B. Diaz RES290-OL Chapter 14 - ECQ

1. What is Quantitative Data Analysis?

Since quantitative data is numerical in nature, researchers can use software in order to analyze these data. Some of the most common data analysis software are Statistical Package for Social Sciences (SPSS), John’s Macintosh Program (JMP), Stata, Statistical Analysis System (SAS), R, and MATLAB.

2. “What are the measures of central tendency?”

1. Mean – the mean of a set of data is basically just the arithmetic average of the given data. The mean or average can be calculated by getting the summation of all the observations and dividing it by the total number of observations. The mean is mostly used as a measure of central tendency of interval variables since it needs to have a specific interval among the values for it to provide a meaningful result. 2. Median – the median of a set of data, put in simple terms is just the value at the very middle of a range of values. According to Dr. Dante L. Silva (2020), this type of measure of central tendency “can be used with ordinal, ratio or interval measurements and assumptions are needed not to be done is called median. Also, resistant measure is another term for the median in which, it is not affected by changes”. 3. Mode – the mode of a set of data is the value with the most amount of observations in the given of data. This type of measure of central tendency is mostly used with nominal variables since it does not need any numerical calculations.

3. Differentiate the between variables to ordinal variable and nominal variables.

Nominal variable – as the name suggests, nominal variables just pertains to a name or category. The values of this type of variable does not have a ranking or order. Nominal variable is generally referred to as the lowest level of measurement. Examples of this type of variable are sex (male or female), ethnicity (Filipino, Indonesian, American, Korean, etc.), marital status (single, married, divorced, widowed), or religion (Christianity, Buddhism, Hinduism, Islam, Judaism, etc.). These variables are categories that does not have any clear ranking among its values. Ordinal variable – ordinal variable, just like nominal variable pertains to a name or category, however, ordinal variable, as the name suggests, have an order or ranking among the values of the variable. Example of this type of variable are academic ranking (instructor, assistant professor, associate professor, professor) and order of precedence such as in a government (president, vice-president, secretary, president of the senate, etc.).

Engr. Tyrone John B. Diaz RES290-OL Chapter 14 - ECQ

4. Define what is mean.

The mean of a set of data is basically just the arithmetic average of the given data. The mean or average can be calculated by getting the summation of all the observations and dividing it by the total number of observations. The mean is mostly used as a measure of central tendency of interval variables since it needs to have a specific interval among the values for it to provide a meaningful result.

5. What is the difference between first quartile and the third quartile to eliminate the outliers?

According to Dr. Dante L. Silva (2020), interquartile range “focuses on the difference between first quartile and the third quartile to eliminate the outliers. Interquartile is as a strong measure of sample dispersion, and it is considered the middle value of the upper and lower halves of the data”. 6. What are ANOVA and MANOVA differences?

ANOVA stands for analysis of variance while MANOVA stands for multiple analysis of variance. According to Dr. Dante L. Silva (2020), “One-way ANOVA tests the means of unlike groups if they are common or not. Two-way ANOVA is applied when the groups that undergo in some series of tests has two distinctive feature characteristics instead of only one. While MANOVA, can be applied in multiple analysis of variance”.

7. Define Simple Linear Regression.

According to Dr. Dante L. Silva (2020), simple linear regression “determines the range in which the variable that is dependent is being predicted by the variable that is independent and inform how well do line matches the data. It will seek to look the best “fit” in the middle of two or more variables”.

8. State why graphing is necessary.

Using graphs in a research study is necessary because of the reason that it gives not only the researcher but also the readers of the research study a visual representation of data.

Engr. Tyrone John B. Diaz RES290-OL Chapter 14 - ECQ

Using graphs is an easy way of presenting data that is also easier for the readers to comprehend since visual representation is generally easier and faster to understand compared to a textual representation of data.

9. State the types of graphs.

The following are just some examples of types of graphs that can be used in a research study. 1. Line graph – a line graph uses a line that interconnects one value in a given set of data to its subsequent value. 2. Bar graph – this type of graph uses individual bars for each value in a data set that represents their respective magnitudes. 3. Scattergram – this type of graph uses individual points that represents the values in a given data set. These points are scattered on the graph, hence the name scattergram.

10. What are the four stages in data analysis?

The four stages of data analysis are description, followed by interpretation, after that is conclusions, and lastly, theorization and starts again with description. These four stages of data alaysis form a cyclical process. According to Dr. Dante L. Silva (2020), “During the description stage, the researcher provides a descriptive analysis of all the data gathered for their research. After giving a thorough description of all data, the researcher gives his take on the meaning each data gives, this is the interpretation stage. From the interpretation stage, the researcher then draws up some kind of conclusion. This conclusion may of major or minor importance to the data. Usually conclusions drawn from this are minor, which would add up and lead to the major conclusion, which is found in the final chapter of the research project. Lastly, the theorization stage. This stage is where the researcher compares the findings of their data analysis to similar literature found in the literature review. This might coincide with the findings of other researchers or it may not. The purpose of this is to contribute knowledge to the area of research. For example, the data gathered clearly shows that customers aren’t happy after eating at McDonald’s (description). The researcher then interprets these as due to the services the crew is giving them (interpretation). The researcher then draws a conclusion which states that the crew should improve on the services they are giving to the customers (conclusion). The researcher should then check about similar literature found in the literature review whether or not the same conclusion has been drawn by other researchers about different restaurants (theorization). The conclusion may be minor as other conclusions may be drawn up such as food quality, cleanliness of place etc. The process then goes back to the 1st stage and another conclusion will be drawn until such time that a major conclusion will be formulated for the entire research”.

Engr. Tyrone John B. Diaz RES290-OL Chapter 14 - ECQ

11. What are the four stages of the process of data analysis?

The four stages of data analysis are description, followed by interpretation, after that is conclusions, and lastly, theorization and starts again with description. These four stages of data alaysis form a cyclical process. According to Dr. Dante L. Silva (2020), “During the description stage, the researcher provides a descriptive analysis of all the data gathered for their research. After giving a thorough description of all data, the researcher gives his take on the meaning each data gives, this is the interpretation stage. From the interpretation stage, the researcher then draws up some kind of conclusion. This conclusion may of major or minor importance to the data. Usually conclusions drawn from this are minor, which would add up and lead to the major conclusion, which is found in the final chapter of the research project. Lastly, the theorization stage. This stage is where the researcher compares the findings of their data analysis to similar literature found in the literature review. This might coincide with the findings of other researchers or it may not. The purpose of this is to contribute knowledge to the area of research. For example, the data gathered clearly shows that customers aren’t happy after eating at McDonald’s (description). The researcher then interprets these as due to the services the crew is giving them (interpretation). The researcher then draws a conclusion which states that the crew should improve on the services they are giving to the customers (conclusion). The researcher should then check about similar literature found in the literature review whether or not the same conclusion has been drawn by other researchers about different restaurants (theorization). The conclusion may be minor as other conclusions may be drawn up such as food quality, cleanliness of place etc. The process then goes back to the 1st stage and another conclusion will be drawn until such time that a major conclusion will be formulated for the entire research”.