Several things can be done to the raw data in order to see what they can say about the hypotheses (Neuman, 2003). An inspection of the raw data can be done by using the descriptive statistics to find obvious coding errors. The minimum and maximum values for each variable must fall within the admissible range. Pairwise correlations depict that all relationships must be in the expected direction. Meanwhile, listwise deletion of missing values indicates that the data can be used for analysis.
An outlier is an observation that is unusually small or large. Outliers assist researchers in detecting coding errors. According to Bagozzi and Baumgartner (1994), outliers are not recommended to be routinely excluded from further analysis. Data collected were analyzed by using three approaches:
1. Cronbach’s alpha (a) was used to test the reliability. Cronbach’s alpha indicates how well the items in a set are positively correlated to one another. This is to make sure that the scales are free of random or unstable errors and produce consistent results over time (Cooper & Schindler, 1998);
2. Descriptive statistics where the researcher used mean, standard deviation and variance to get an idea on how the respondents reacted to the items in the questionnaire. The major concern of descriptive statistics is to present information in a convenient, usable and understandable form (Runyon & Audry, 1980).
Descriptive summary, including frequency and descriptive, was used to screen the data set. Among basic statistics to use were mean, median, mode, sum, variance, range, minimum, maximum, skewness and kurtosis.
3. Inferential statistics concerned with generalizing from a sample to make estimates and inferences about a wider population (Neuman, 2003...
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....e. more than 30 (Hair et al., 2006). Sekaran (2003) suggests the approximation to normality of the observed variables could be investigated by inspecting the data through histograms, stem-and leaf displays, probit plots and by computing univariate and multivariate measures of skewness and kurtosis. Histograms, stem-and-leaf and probit plots indicate the symmetric distribution of variables or sets of variables.
Tabachnick and Fidell (1996) suggest the value of skewness and kurtosis is equal to zero if the distribution of a variable is normal. Chou and Bentler (1995) emphases the absolute values of univariate skewness indices greater than 3 can be described as extremely skewed. Meanwhile, a threshold value of kurtosis greater than 10 can be considered problematic and value greater than 20 can be considered as having serious problems (Hoyle, 1995; Kline, 1998).
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.
Renaud, R. (2014a, April 10). Unit 10 - Understanding Statistical Inferences [PowerPoint slides]. Retrieved from the University of Manitoba EDUA-5800-D01 online course materials.
Best, Joel. Stat-Spotting: A Field Guide to Identifying Dubious Data. California: University of California Press, 2008.
Many statistical ideas were mentioned in the Barron’s guide. In the topic called Graphing Display the Barron’s guide discusses the different types of graphs, measures of center and spread, including outliers, modes, and shape. Summarizing Distributions mentions different ways of measuring the center, spread, and position, including z-scores, percentile rankings, and the Innerquartile Range, and its role in finding outliers. Comparing Distributions discusses the different types of graphical displays and the situations in which each type is most useful or appropriate. The section on Exploring Bivariate Data explains scatter plots in depth, discussing residuals, influential points and transformations, and other topics specific to scatter plots. Conditional relative frequencies and association, and marginal frequencies for two-way tables were explained in the section entitled Exploring Categorical Data. Overview of Methods of Data Collection explained the difference between censuses, surveys, experiments, and observational studies. Surveys are discussed more in depth in Planning and Conducting Surveys, including characteristics of a well-designed and well-conducted survey, and sources of bias. Planning and Conducting Experiments explains experiments in depth; going over confounding, control groups, placebo effects, and blinding, as well as randomization. Basic rules for probability are discussed in Probability as Relative Frequency, including the law of large numbers, addition rule, and multiplication rule. Other topics discussed in this section include the different types of probability calculations. Combining Independent Random Variables discusses manners in which two variables can be compared to each other and things to be wary of while doing so.
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 study follows the descriptive analytical method. It begins by an introduction forming a background to the study; followed by a summary of the plot, a literature review, a discussion and a conclusion.
iNZight’s ‘Visual Inferencing Tool’ will be what I use to display using the data. It will present this as a box and whisker graph. I will then analysis the data distribution discussing skews, inter-quartile range, range, shape etc. I will make a first judgement based on what I see being presented. From there I will create a difference between medians bootstrapping confidence interval, this is so I can produce reliable intervals that will potentially provide evidence for my question. iNZight will also be used for all statistics and
Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages.
The extent to which a distribution of values deviates from symmetry around the mean is the skewness. A value of zero means the distribution is symmetric, while a positive skewness indicates a greater number of smaller values, and a negative value indicates a greater number of larger values (Grad pad, 2013). Values for acceptability for psychometric purposes (+/-1 to +/-2) are the same as with kurtosis.
As the number of the sample is established, it is expected that the degrees of freedom of the research will be between 38 and 48. The alpha level for the research is 0.5. Taking into consideration the established degrees of freedom and the alpha level of 0.5, the critical value is expected to be either 2.042 or 2.000 (Gerstman, 2016). The confidence level that the null hypothesis is correct will be at 95%. The collected data will be analyzed with the use of Microsoft Excel.
The first table was titled Other Measures. It provided information on the sample size, minimum, maximum, first quartile, third quartile, given percentage, and value of percentile. These values are used to compute range and interquartile range in the measures of dispersion. The last table shows the mean plus or minus 1, 2, or 3 times the standard deviation and offers details on how many values fall within the ranges created by those calculations.
Descriptive statistics is the term that summarizes the data in an effective and meaningful way. Basically the descriptive statistic describes the data in a simple way however, it is not more effective to draw conclusion about the hypothesis under study. It is descriptive statistic that made me able to represent the data in more arranged form and made me able to study and understands the graphical or tabulated description of data. It also made me able to evaluate the data which is an effective approach to reach at a particular decision about the data Schau, (2003). The course enable me to apply the knowledge related to descriptive statistics to evaluate the financial statements of the company in order to reach at some decision regarding the way payrolls are generated.
In evaluating statistical data one thing to consider is the measure that is used. By understanding the different statistical measurement tools and how they differ from one another, it is possible to judge whether a statistical graph can be accepted at face value. A good example is using the mean to depict averages. This was demonstrated by using the mean as a measure of determining the distribution of incomes. The mean income depicted was, $70,000 per year. At face value, it looks as though the sample population enjoys a rather high income. However, upon seeing individual salaries, it becomes obvious that only a few salaries are responsible for the high average income as depicted by the mean. The majority of the salaries were well under the $70,000 average. Therefore, the mean distributed income of $70,000 was at best misleading. By also looking at the median and mode measures of the income distributions, one has a clearer picture of the actual income distributions. Because this data contained extreme values, a standard deviation curve would have given better representation of salary distribution and would have highlighted the salaries at the high level and how they skewed the mean value.
Whether or not people notice the importance of statistics, people is using them in their everyday life. Statistics have been more and more important for different cohorts of people from a farmer to an academician and a politician. For example, Cambodian famers produce an average of three tons or rice per hectare, about eighty per cent of Cambodian population is a farmer, at least two million people support party A, and so on. According to the University of Melbourne, statistics are about to make conclusive estimates about the present or to predict the future (The University of Melbourne, 2009). Because of their significance, statistics are used for different purposes. Statistics are not always trustable, yet they depend on their reliable factors such as sample, data collection methods and sources of data. This essay will discuss how people can use statistics to present facts or to delude others. Then, it will discuss some of the criteria for a reliable statistic interpretation.
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.