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.
The primary factors that are important in conducting statistical test are variables (categorical or quantitative) and the number of (IVs) independent variables and (DVs) dependant variables. To facilitate the identification process the chapter provides two decision- making tools so that it is easier to make a decision. The chapter presents the decision making tools and gives an overview of the statistical techniques addressed in this text as well as basic univariate test, all of which will be organized by the four types of research questions: degree of relationship, significance of group differences, prediction of group membership, and structure. Statistical test that analyze the degree of relationship include bivariate correlation and regression, multiple regression and path analysis. Research questions addressing degree of relationship all have quantitative variables.
Regression is a hypothetical model of the relationship between the two variables. The similarities between them are, they both are statistical analyses used to identify the relationship between the predictor and the outcome variable. What is a line of best fit, what does it tell us, and how is it developed? The line of best fit is a linear line that minimizes error within a data set. It helps to
The self-administered questionnaire is a technique used to engage in the needed data collection. Zikmund (2004) stat... ... middle of paper ... .... Multiple regression is a technique that allows additional factors to enter the analysis separately so that the effect of each can be estimated. Malhorta (2004) explained that multiple regression analysis is a way to describe the relationship between a dependent variable and several independent variables. In the multiple regression, one uses additional independent variables that help better explain or predict the dependent variable (Y).
Numerical summaries which can either measure the central tendency of a given set of data or which describe the spread of a given data. They use ... ... middle of paper ... ...he data. It will be also paramount to investigate the results using evaluative means like the ANOVA test, which uses variance and makes sure that averages exists within every variable test group. There is also need to set a regression, which is a general statistical tool, which sees how variables are interconnected. Finally, there is the need to analyze the qualitative element of the analysis (Chance et al, 2005).
Initially we test that two variables are associated or not. If two variables are associated then we find strength of this association by appropriate statistic. Cross tabulations can be produced by a range of statistical packages, including some that are specialized for the task. By using the cross tabulation technique, in this section we will analyze the association between variables. We will also test the correlation among them.
The Chi-Square is used in two circumstances as below: i) When the researcher want to estimate how closely the observed distribution matches the proportions that is expected. This is called ‘goodness of fit’ test. ii) When the researcher wishes to estimate whether random variables used are independent. Assumptions of the Chi-Square Test: i) To use Chi-Square test for independence, the two variables that are used must be of categorical data i.e. the data ought to be measured at nominal or ordinal levels.
• Define descriptive and inference statistic. What is/are the differences? Descriptive statistics refers to the collection, presentation, description, analysis and interpretation of a collection of data, essentially is to summarize these with one or two pieces of information (descriptive measures) that characterize all of them. The descriptive statistics is the method of obtaining a data set conclusions about themselves and do not exceed the knowledge provided by them. It can also be used to summarize or describe any outfit whether it is a population or a sample, as in the preliminary stage of statistical inference the elements of a sample known.
Statistical Method Classic Assumption Test When a research uses multiple regressions as the statistical tool, that research directly use several assumption (Lind and Marchal and Whaten, 2008). Thus, writer will use classic assumption tests to prove that these assumptions are correct. Normality Test Writer will conduct normality test in this research. The purpose of this normality test is to measure the distribution of the residual. Distribution of the residual in multiple regressions should follow normal distribution (Lind and Marchal and Whaten, 2008).
A negative value indicates a distribution which is more peaked than normal, and a positive kurtosis indicates a shape flatter than normal. An extreme positive kurtosis indicates a distribution where more of the values are located in the tails of the distribution rather than around the mean (Grad pad, 2013). A kurtosis value of +/-1 is considered very good for most psychometric uses, but +/-2 is also usually acceptable (Grad pad, 2013). The above graph shows GPA with a kurtosis of -.811; awhile the final kurtosis is -33.2. The extent to which a distribution of values deviates from symmetry around the mean is the skewness.