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).
"Outliers: Missing Pieces." One Read Leaf. WordPress, 19 Nov 2013. Web. 1 Dec 2013. .
The objective of this lab is to become accustomed to using Logger Pro data collection software and the Lab Pro interface. The ideas of averaging constant data and finding the standard deviation will be introduced. This lab will instruct the use of different tools such as the Linear Fit and Statistics tools in Logger Pro. Another objective is to introduce the construction of tables and graphs in Excel. The software will work cohesively to construct organize data that is collected in the lab.
There are two basic psychometric properties, validity and reliability that have been used to evaluate the quality of scale development. Psychometric testing used to evaluate the quality of instrument (Polit& Beck, 2010).
After reading this book, I am touched by the underlying philosophy of statistics. Various theories and models are introduced in this book. During the progress of the development, controversies and confits among these theories are largely attributable to diversity of ideology and doctrine from their establishers. In future, the statistics might evolve into a new era and the vogue methods, like p-value or confidence interval, might be discarded. I am looking forward to witness how statistics make our lives
Then, a scatterplot was formed with the data (Figure 3). It was a crucial graph as it helped determine the outliers in the information (see Appendix D for the outlier chart). Some of these outliers were located in towns with really low population numbers (the average population for an American city or town is around 20000)
Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. A requisite for developing inferential statistics supports general linear models for sampling distribution of the outcome statistic; researchers use the related inferential statistics to determine confidence (Hopkins, Marshall, Batterham, & Hanin, 2009).
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
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.
The first definition of abnormality to be discussed is Deviation from Statistical Norms. This definition is based on the viewpoint that generally abnormal behaviour, such as delusions, are relatively infrequent, and in statistical terms, occurrences of abnormality would be outside of a normal range. Under this definition, the further the behaviour is away from the majority, the more abnormal it is (Helzer, 2002). The main strength of the deviation from statistical norms definition is tha...
Renaud, R. (2014a, April 10). Unit 10 - Understanding Statistical Inferences [PowerPoint slides]. Retrieved from the University of Manitoba EDUA-5800-D01 online course materials.
Overall, the test had an adequate reliability coefficients. It is important to note that the items with the higher alpha scores had more questions, whereas, the items with the lowest alphas scores had fewer questions. Fewer questions my make it more difficult to get higher homogeneity scores (Drummond et al., 2016). So at first consideration, I would say this is a strong test with reliable scales. When assessing validity, I would be inclined to also consider it a strong test. The correlations conveyed a wide array of strength. Yet some of the expected overlap represents adequate validity (Psychnet, 2016). Overall, this could be a good test to use if targeting population similar to the tested population. It was tested on highly academic groups, which may not be representational of the population at large, but may be useful in colligate settings (Psychnet, 2016).
However, both characteristics of reliability and validity are important and can be used in many studies, such as the self-rating and other- ratings of daily behavior. Reliability refers to the internal consistency, inter-rater reliability, test-retest, and standardized scoring. In other words reliability means that study scores have to be constant with repeatability of the findings. Validity also refers to convergent validity, discriminant validity, and predictive validity. Validity refers to the reliability or credibility of the research. If the findings in a study, reliability and validity are valid they must be reliable.
The mean is usually used as a measure of central location. However, the average is extraordinarily sensitive to abnormally large or small observations (Anderson et al., 2011, p.90). When using data with extreme values, the median is desired because its calculation depends less on the broadness of the rang...
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.
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.