3.9 DATA ANALYSIS METHODS
3.9.1 NORMALITY TEST
Saunders, Lewis and Thornhill (2007) explains Kolmogorov-Smirnov test as a statistical test used to find out the probability that an observed set of values for each category of a variable differs from a specified distribution. In this study, one-sample Kolmogorov-Smirnov test was used to check whether the collected are distributed normally or not.
Table 3.6 One Sample Kolmogorov-Smirnov Test
Variables Kolmogorov-Smirnov Z P-value
Behavioural Intention to Adopt Mobile Commerce 1.321 0.0530
Perceived Usefulness 1.813 0.063
Perceived Ease of Use 1.895 0.092
Trust
1.029 0.062
Technological Self-Efficacy 1.042 0.072
Accessibility
1.958 0.091
Terminology Clarity 1.320 0.061
Response Time 1.507 0.071
Output Quality 1.284 0.074
The results of the one-sample Kolmogorov-Smirnov test reveal that the proportions follow a normal distribution. The above table shows that normality is met for all the variables as the P values are
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Burns and Grove (2011) add that this test provides an examination of frequencies for two nominal scaled variables in a cross-tabulated form to determine whether the variables have a non-monotonic relationship. The Chi-square test (2) examines the relationships between two variables at nominal and discrete level. The test compares the actual frequencies with the expected results or how strongly they match or differ from the expected distribution and whether two variables are independent or not. In this study, most of the questions were nominal and discrete hence the test was used for interpretation of data (Burns and Grove, 2011). Chi-square test (2) is used to test the statistical relationship between two discrete variables using a set of frequencies (Carver and Nash,
For this statistical inference, the question was whether the means were truly different or could they have been samples from the same population. To do draw a conclusion, we must first assume normal distribution. We must also set the null hypothesis to m1 - m2 = 0. And per this assignment we must set the a-level at .05 and the hypothesis alternative to m1 - m2 ¹ 0; thus requiring a two-tailed test.
Inferential Statistics has two approaches for making inferences about parameters. The first approach is the parametric method. The parametric method either knows or assumes that the data comes from a known type of probability distribution. There are many well-known distributions that parametric methods can be used, such as the Normal distribution, Chi-Square distribution, and the Student T distribution. If the underlying distribution is known, then the data can be tested accordingly. However, most data does not have a known underlying distribution. In order to test the data parametrically, there must be certain assumptions made. Some assumptions are all populations must be normal or at least same distribution, and all populations must have the same error variance. If these assumptions are correct, the parametric test will yield more accurate and precise estimates of the parameters being tested. If these assumptions are incorrect, the test will have a very low statistical power. This will reduce the probability of rejecting the null hypothesis when the alternative hypothesis is true. So what happens with the data is definitely known not to fit any distribution? This is when nonparametric methods are used.
the observed test statistic is the Z value on the that leads to a probability of 83/100 or .83
In Flannery O’Connor’s short story, A Good Man is Hard to Find, a family gets in a car accident on a deserted dirt road. Unluckily for them, they are found by a group of three escaped convicts, led by a man who calls himself The Misfit. These convicts systematically execute the family in twos as the Misfit talks with the grandmother. While the catalyst for this execution is the grandmother’s verbal recognition of The Misfit as an escaped criminal, it is clear that he commits his crimes for deeper reasons. The Misfit is angry on a fundamental level, and acting out on this anger is the closest he can come to feeling pleasure in this life.
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
The alpha value used was .05. The test showed that there were 62.5% of the cells had an expected count less than 5. Since the percentage was larger than 20%, likelihood ratio showed a value of 22.937, a degree of freedom of 9 and a p-value of .006. The p-value of .006 and which is smaller than .05 required the null hypothesis was rejected. We were able to determine if there was a statistical significance between ethnicity and the belief that the Bakersfield Police treats all resident fairly regardless of race. This was achieved by using the contingency coefficient sine the graph was 4x4. The contingency coefficient had a value of .406 and a p-value of .020. The p-value revealed that there was a weak statistical
Collected data were subjected to analysis of variance using the SAS (9.1, SAS institute, 2004) statistical software package. Statistical assessments of differences between mean values were performed by the LSD test at P = 0.05.
In this lab we apply the technique known as a two point discrimination test. This test will allow us to determine which regions of the skin are best able to discriminate between two simultaneous sensory impulses. According to (Haggard et al. 2007), tactile discrimination depends on the size of the receptive fields located on the somatosensory neurons. However receptive fields for other types of sensations are located elsewhere. For vision we find that the receptive fields are located inside the visual cortex, and for hearing we find receptive fields in the auditory cortex. The ability for the body to discriminate two points depends on how well that area of the body is innervated with neurons; and thus conferring to the size of the receptive fields (Haggard et al. 2007). It is important to note that the size of the receptive field generally decreases in correlation to higher innervations. As was seen in the retinal receptive fields, the peripheries of tissue had contained larger receptive fields (Hartline, 1940). In our test we hypothesized that the finger region will be able to discriminate better than the forearm. This means that they will be much more innervated with neurons than the forearm, and likewise contain smaller receptive fields. This also means that convergence is closer to a 1:1 ratio, and is less the case the farther from the fingers we go. We also think that the amount of convergence is varied with each individual. We will test to see if two people will have different interpretations of these results.
...ne’s level of interest. The independent variables are the three different groups that are being studied. The ratings given by the participants will represent the dependent variables. The alpha is set as 0.05. According to SPSS, the results show that this study has a significance level of 0.000, which is less than 0.05. Because of this difference, it is appropriate to accept the research hypothesis and to reject the null hypothesis.
The Beck Anxiety Inventory was designed by Aaron T. Beck and is self report scale that consists of 21 items. The items are short and straightforward, making it easy to read and comprehend. All items are related to anxiety and describe a symptom of anxiety that is rate on a four point likert scale according to severity. The answers range from 0-3 and the responses range from “not at all” to “severely; I could barely stand it” and all items are added for a total score. The instructions on the test ask for the respondent to “indicate how much you have been bothered by each symptom during the past week, including today, by placing an X in the corresponding space in the column next to each symptom” (Dowd, 2008). The assessment is intended for adolescents and adults and can be administered individually or in a group setting. An additional copy of the inventory test is also available in Spanish. It was originally created from a sample of 810 outpatients of that were predominately affected by mood and anxiety disorders and research on the original development is described as informative and thorough.
The Benedict's Test is used to test the presence of simple sugars in a sample. If sugars are present, a color change will occur from blue to red. However, although the Benedict's test shows the presence of sugars, it cannot accurately determine the concentration of sugar in a sample solution. In our method, we added specific concentrations of glucose to the Benedict's test to use as a chart to estimate the glucose concentration of an unknown solution X. Although this gives a rough estimate of the concentration, it is very inaccurate. For example, the mystery solution X was a pale orange color, which was between the colors in my first and second test tube.
Rooks, Noliwe M. "Why It's Time to Get Rid of Standardize Tests." Time N.p., n.d. 11 Oct 2012. Web. 15 May 2014..
The ratio of 1.7 for the last two years indicates consistency, although a lower number is preferred. As a company produces high value product, this could be a satisfactory ratio. By comparing it to 2011 when a ratio was 2.9, in the last two years a ratio improved
The histogram is presented in the right form, using data from reliable sources and a methodology that appears to be correct (4 marks)
The Stanford-Binet Intelligence Scale is a standardized test that assesses intelligence and cognitive abilities. Intelligence is "a concept intended to explain why some people perform better than others on cognitive tasks. Intelligence is defined as "the mental abilities needed to select, adapt to, and shape environments. It involves the abilities to profit from experience, solve problems, reason, and successfully meet challenges and achievement goals.