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.
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
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 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).
(Holmes-Smith, Coote, & Cunningham, 2004) On specification the model is then tested for plausibility based on the sample data that comprise all observed variables in the model. The main task in model testing is to determine the goodness-of-fit between the hypothesized model and the sample data. Fit Indices There is abundance of fit indices and wide variety of disparity in agreement on which indices to report and also the cut-offs for various indices, (Hooper, Coughlan, & Mullen, 2008) because dif... ... middle of paper ... ...are the options to verify the dimensionality of the measurement or to verify the model fit. The modification of the model is aided by modification indices (MIs) sometimes in conjunction with parameter estimates statistics. (Lei & Wu, 2007) These indices were examined during evaluation of model fit to get the direction of modification, for example whether freeing or incorporating parameters either between or among unobserved variables is required in obtaining better model fit.
However, quantitative practitioners affirm the cause or effect interplay between data and construct for validation of investigation by applying test procedures or processes (Golafshani, 2003, p. 599). As a result, with regard to validity, researchers conclude that, it is whether measurements of the mean are accurate or they are measuring the intended features. Accuracy of the mean helps in relating the cause-and-effect relationship present in internal validity. The above definition is associated with quantitative research methodology. It summarizes that validity to be the extent in which instruments measure the exact thing it purports to measure.
Moreover; I will examine true experiments and examine how they control threats to internal validity. In addition, I will examine how true experiments are different from experimental designs. Finally, in this paper, I will discuss quasi-experiments by explaining their importance and how they differ from experimental designs. According to Shaughnessy, Zechmeister, and Zechmeister (2009), data analysis and statistics play a major role in the analysis and the interpretation of experimental findings. Descriptive statistics and inferential statistics are both used to describe the results of an experiment.
1. Introduction Design variables are important to be conducted the appropriate experiment analyzing and getting the accurate values for integer, discrete, zero-one (binary), and continuous variables. The researchers should classify design factors before the experiment is conducted. In literature, there are several factors such as quantitative, qualitative, discrete, continuous, zero-one (binary), non-zero-one (non-binary), controlled and uncontrolled variables (Sanchez & Wan, 2009). Quantitative variables get numerical values.
In order to predict and gauge the consumers responses to a questionnaire correctly the questionnaire must be assembled with the appropriate guidelines to attain the desired statistical results. Works Cited Lane, D. (2003). Levels of Measurement. Retrieved from http://cnx.org/content/m10809/latest/ Stat Trek. (2011).
It has objective stances, logic, and numbers focusing on unchanging data and details (Babbie, E.R., 2010). For example, a quantitative method would ask how many people are participating in a program, what are the characteristics of people in a program, and how do the people in the program perform (Leedy, P. & Ormrod, J., 2009). Using a quantitative research method has several advantages for testing the hypothesis. The aim of quantitative research is to classify features, count the features, and construct statistical models to explain what was observed (McNabb, D.E., 2008). Typically, quantitative methodologies uses already tested and validated theories about how and why an event occurs.