Data Analysis Methods Used in Quantitative Research.

Quantitative research is done to find the accurate facts by evaluating the problems like opinion, behaviour by using numerical data. This research is based on theories, hypothesis, collecting, analysing the data to make the research accurate.

4) Discuss the implications of changing the level of significance to a larger value. What mistakes or error could increase if the level of significance in

15 In testing the hypotheses: H0 β1 ’ 0: vs. H1: β 1 ≠ 0 , the following statistics are available: n = 10, b0 = 1.8, b1 = 2.45, and Sb1= 1.20. The value of the test statistic is:

that were not correct and correct them, giving the data from your experiment that supports the corrections.

Given the following information, would your decision be to reject or fail to reject the null hypothesis? Setting the level of significance at .05 for decision making, provide an explanation for your conclusion.

There are multiple values being considered in this study. This has the potential to cause a type 1 error and Bonferroni procedures should be used to reduce the risk of a type 1 error from happening.

As a group we encountered several possible errors throughout the experiment. Blunder error is a common error than can occur.

Answer: D3. The probability of Type I error, , and the probability of Type II error,

Quantitative research methods are objective as it uses measurements and analysis of statistical data to answer the study question. The researchers’ opinions do not affect the outcome of the study, ensuring that the study is unbiased. Another advantage is quantitative research uses numbers and statistics which is understood universally (Houser,2008).

The null hypothesis should be rejected that there is no difference between treatment methods because the calculation show that the treatments are statistically different.

A type I error is where "the null hypothesis is rejected when it should not be rejected" and a type

From the calculation (See Appendix I), we get the 3-sigma control limits for the process, i.e. UCL=0.091, LCL=0.014. These control limits indicate that if the error proportion is within the range of [0.014, 0.091], the process is under control; if not, the process is out of control.

Quantitative research involves collecting data, which can be expressed numerically. The design is well structured with pre-determined outcomes. It frequently involves testing a hypothesis, which then can be analysed from the data deductively using statistical methods. Using numerical data is easier to analysis mathematical, so larger sample sizes can be utilised compared to qualitative research, therefore giving a better representative of the population; along with simplifying the process of making a generalisation. Another advantage is that studies can easily compare to similar findings (Kruger, 2003). The disadvantages are the results are limited and might not provide a proper understanding of the topic. Also, statistics and leading questions can be used to give a false representation of the data when summarising.

A Type I error is the worst type of error a researcher can make while conducting an experiment. A Type I error is the rejection of the null hypothesis when the null hypothesis is actually true. Therefore, a Type I error is a false positive and it indicates that results are significant when they are not. For example, if a researcher states that the amount of antidepressants taken by individuals will decrease the number of suicides, the outcome could be potentially harmful. Individuals would become reliant on the medication and have expectations of the antidepressant lowering their likelihood of committing suicide. Unfortunately, this Type I error could potentially increase suicide. Setting a low alpha level will allow the researcher to avoid

Quantitative research methods are utilized to study the natural phenomena. So, it includes survey methods, formal methods, and numerical methods.