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Date Graded: May 16, 2019

Quantitative Research Design

BTM-7303 Assignment # 8

Elton Norman

Dr. Antoinette Kohlman

12 May 2019

Research Problem

The research on the relationship between substance abuse and school dropout cases can be examined using a quantitative methodology. A substantial number of researches on dropout rates touch on the actual percentages of the students who drop out of school due to substance abuse. However, limited research has been done on the level of education which has witnessed the highest dropout rates and the drug which is mostly associated with these cases. Comment by Antoinette Kohlman: Do you mean research studies? I do not know what you mean by researches? Comment by Antoinette Kohlman: What impact does this lack of information have on schools, communities, or families? Would studying this situation create new knowledge that will enhance practice or further theoretical development? In your next paper, I would enhance the problem statement with this type of information.

Purpose of the Research

The purpose of the research is to establish the level where most of the students drop out of learning institutions due to substance abuse. It is also geared towards establishing the type of drug which contributes to most of these cases.

Research Questions

At what level of education do most of the youths drop out of school? Comment by Antoinette Kohlman: I think there is a gap here. I would add more key questions. For example: >> Among those who drop out of school, what percentage leaves due to illegal drug usage? >> Among those who dropped out of school due to illegal drug usage, what was the most common illegal drug used? How might the above research questions be translated into hypotheses? Hypothesis Examples: Illegal drug usage does not have a statistically significant effect on school dropout rates. Illegal drug usage has a statistically significant effect on school dropout rates. Comment by Antoinette Kohlman: This first question is a good starting point because you are acknowledging there are many reasons that contribute to high school dropouts. You then immediately pinpoint your interests in drug usage however I think more nuanced questions can be added.

What type of drug is associated with the highest dropout rates?

Hypotheses

Most of the school dropout rates due to substance abuse are witnessed in high school. Comment by Antoinette Kohlman: There are handouts that explain how to craft hypotheses in the Dissertation Center. Click the following link to access NCU’s Developing a Hypothesis Handout. The element that is missing from your hypotheses is “statistical significance.” Please see my hypothesis examples above! Here is an excerpt that you can use to self-evaluate your hypotheses: Nature of Hypothesis 1. It can be tested –verifiable or falsifiable 2. Hypotheses are not moral or ethical questions 3. It is neither too specific nor to general 4. It is a prediction of consequences 5. It is considered valuable even if proven false

Alcohol abuse contributes to the highest rate of school dropout rates in high school.

Use of Surveys

Surveys make up one of the excellent ways of gathering data during quantitative research and involve gathering answers from the chosen sample which represents the population being studied. It includes the use of questionnaires, mobile surveys, paper surveys, face-to-face interviews, and telephone surveys. In this research, the use of questionnaires is viable since it will help reach a large number of respondents for a short period.

Advantages of Surveys

One of the advantages of surveys is that they are inexpensive. In most cases, surveys utilize questionnaires whereby the respondents are issued with questions which they are supposed to fill. In this case, a quantitative survey involving the use of surveys can be carried out with a minimum budget and still produce a top-notch survey with valid results. Comment by Antoinette Kohlman: I agree. Please cite at least one source!

The use of surveys in research leads to extensive research. It should be noted that most of the research is used to describe particular aspects of a certain population. In this case, the research carried out must involve a large population so that the results from the sample population infer to the whole population under study. Such results can only be achieved when a method which can reach a large population for a short period is used. In this case, the use of surveys in research gives the researchers an opportunity to conduct the research using a large sample. Comment by Antoinette Kohlman: How so? How does a survey lead to “extensive research?” Comment by Antoinette Kohlman: I do not understand what you mean. Doesn’t most research target specific populations? Comment by Antoinette Kohlman: So you mean the results can be generalized?

Disadvantages of Surveys

The use of surveys has disadvantages which include higher chances of bias. It is evident that the researchers are involved in choosing the respondents. In this case, they can select a group of respondents who are inclined to their hypothesis. The fact that samples are used to infer to a large population requires the use of a large sample with respondents who bear different lines of thought with the researchers. In this scenario, a poorly selected sample can lead to unreliable results which are not a representative of the larger population(Mitchell, 2010). Comment by Antoinette Kohlman: Please be specific and name the type or types of biases. Comment by Antoinette Kohlman: Comment by Antoinette Kohlman: What do you mean? I do not understand how this might occur. Please say more. Comment by Antoinette Kohlman: I do not understand what you mean by a “different line(s) of thought.”

Although the researchers can select a sample population without bias, the lack of knowledge in the techniques used in sampling can lead to errors. Sampling method involves calculations and statistical analysis which require a researcher with substantial knowledge in sampling techniques. Failure to possess such skills can lead to sampling errors resulting in misleading research (Mitchell, 2010).

Use of Quasi-experiment

In this experiment, the use of experiments is limited as the respondents are already out of the learning institutions. For the study, the respondents will be subjected to a quasi-experiment whereby they will only give details about the level of education they dropped out of school and the substance which they can attribute to the same. The use of quasi-experiments is popular in research as it enables the researcher to control the experiment and eliminates random assignment which depends on chances that do not offer a guarantee of the equivalency of the groups at the baseline. Comment by Antoinette Kohlman: This would mean you might have to do either a longitudinal study or use a pre-test, post-test design. Comment by Antoinette Kohlman: What exactly is a quasi-experiment? Please explain or define this term and cite your source. Thank You.

Advantages of Quasi-experiments

The use of quasi-experiments in the research gives the researcher an opportunity to conduct the survey without subjecting the respondents to random assignments. Such assignments on substance abuse are unethical to carry out since the survey involves human respondents. The results arrived at in the survey will then be used to infer to the whole population since a large number of respondents will ensure the survey is extensive. Comment by Antoinette Kohlman: Some of this information seems inaccurate/incorrect however I would need to know which sources you used. Cites are needed.

Quasi-experiments give the researcher the freedom to manipulate the respondents to gather substantial data for the study. In normal scenarios, the researchers can only gather limited information about the level at which most of the dropout rates are witnessed. With quasi-experiments, the researcher can twist the questions to fit the study such as indicating most of the drugs most abused for the respondents to choose.

Disadvantages of Quasi-experiments

Although quasi-experiments put the researcher in a position to manipulate the research, they lack randomness which leads to weaker evidence. Randomness is vital in research as it leads to results which infer to the whole population. Failure to include randomness may obtain results that favor the hypothesis and which are not a representative of the whole population.

The use of quasi-experiments leads to unequal groups which jeopardize the internal validity of the research. During surveys, the internal validity aids in obtaining the approximate truth concerning causal relationships. Lack of internal validity infers that the experimenter lacks control for the variables which contribute to the results, leading to unreliable data (Polit & Beck, 2010).

Analysis of Potential Data

After the experiments, the potential data is analyzed using statistical tools such as the SPSS and SAS. At first, the central tendencies for the acquired data will be obtained. The measures of central tendency in the experiment will include median, mode and the mean. It will be followed by the variability measurements; an action will determine the distribution of the score and how the scores vary. In this scenario, the variability measurements taken will include standard deviation, average deviation, and the range.

Factors affecting Data Analysis

The analysis of the data is affected by the level of the skills exhibited by the researcher. Although the correct data can be arrived at from the questionnaires, poor analysis skills can lead to inaccurate data which does not infer to the population under the study. As such, the researcher must be conversant with the statistical tools to draw reliable conclusions from the survey.

The extent of the analysis is another factor which affects the data analysis. During the survey, the researcher must establish the level of analysis and apply the suitable statistical tools which do not compromise the data integrity. In this case, they must apply multiple tools to analyze the collected data to establish the patterns of behavior and test the hypothesis to get the correct data which represents the population (Ramachandran &Tsokos, 2009).

Type 1 and Type 2 Errors

Type 1 and type 2 errors are the examples of errors which can occur in the survey. Type 1 errors occur when the researcher rejects the null hypothesis when it is true. The researcher concludes that there is the existence of differences between the groups when it is not present in reality. On the other hand, the type 2 errors infer that the researcher fails to reject a false null hypothesis. The researcher’s conclusion communicates that there is no difference between the groups although it exists. The presence of these errors in the survey leads to false results as the researcher does not make the correct inferences from the experiments. In such scenarios, the survey is termed as unreliable as it contains misleading information (Gravetter&Wallnau, 2007).

Statistical Power

Statistical power refers to the probability that the study will reveal the differences if they exist. A study bears the possibility of differences in the groups being studied and the failure to detect such differences will lead to research will false results. As such, the statistical tests must have the capacity to detect the differences and reject the false null hypothesis. A low statistical power infers that the tests may not identify the differences even when they are present. Its presence increases the probability of type 2 errors whereby the false null hypothesis is not rejected (Wimmer& Dominick, 2011). Comment by Antoinette Kohlman: Elton, if you are required to compare groups … which groups would you compare? Go back to your initial research questions. Could you compare dropout rates based on gender or ethnicity? Could you hypothesize that males dropout od school due to illegal drug usage at a higher rate when compared to females? Does this make sense?

Avoiding Low Statistical Power

There are numerous actions which are adopted to ensure the statistical tools have a higher statistical power. One of the actions is to use a greater sample size since it offers detailed information concerning the population being studied. Another means of increasing the statistical power is incorporating a higher level of significance which increases the chances of rejecting the null hypothesis.

References

Gravetter, F. J., &Wallnau, L. B. (2007). Statistics for the behavioral sciences. Belmont: Wadsworth.

Mitchell, M. L. (2010). Research design explained. -7th ed. (9780495602217). Belmont: Wadsworth.

Polit, D. F., & Beck, C. T. (2010). Essentials of nursing research: Appraising evidence for nursing practice. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.

Ramachandran, K. M., &Tsokos, C. P. (2009). Mathematical statistics with applications. London: Elsevier Academic Press.

Wimmer, R. D., & Dominick, J. R. (2011). Mass media research: An introduction. Boston, Mass: Cengage- Wadsworth.