Quantitative Results Show
Statistical Analysis An analysis of variance (ANOVA) is an appropriate statistical analysis when assessing for differences between groups on a continuous measurement (Tabachnick & Fidell, 2013). Depending on the goal of the research, there are several types of ANOVAs that can be utilized. Between-Subjects ANOVA: One of the most common forms of an ANOVA is a between-subjects ANOVA. This type of analysis is applied when examining for differences between independent groups on a continuous level variable. Within this “branch” of ANOVA, there are one-way ANOVAs and factorial ANOVAs. Discover How We Assist to Edit Your Dissertation ChaptersAligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.
A one-way ANOVA is used when assessing for differences in one continuous variable between ONE grouping variable. For example, a one-way ANOVA would be appropriate if the goal of research is to assess for differences in job satisfaction levels between ethnicities. In this example, there is only one dependent variable (job satisfaction) and ONE independent variable (ethnicity). A factorial ANOVA is a general term applied when examining multiple independent variables. For example, a factorial ANOVA would be appropriate if the goal of a study was to examine for differences in job satisfaction levels by ethnicity and education level. In this example, there is only one dependent variable (job satisfaction) and TWO independent variables (ethnicity and education level). A factorial ANOVA can be applied when there are two or more independent variables. Within-Subjects ANOVA: A within-subjects ANOVA is appropriate when examining for differences in a continuous level variable over time. A within-subjects ANOVA is also called a repeated measures ANOVA. This type of test is frequently used when using a pretest and posttest design, but is not limited to only two time periods. The repeated measures ANOVA can be used when examining for differences over two or more time periods. For example, this analysis would be appropriate if the researcher seeks to explore for differences in job satisfaction levels, measured at three points in time (pretest, posttest, 2-month follow up). Mixed-Model ANOVA: A mixed model ANOVA, sometimes called a within-between ANOVA, is appropriate when examining for differences in a continuous level variable by group and time. This type of ANOVA is frequently applied when using a quasi-experimental or true experimental design. This analysis would be applicable if the purpose of the research is to examine for potential differences in a continuous level variable between a treatment and control group, and over time (pretest and posttest). ANCOVA: An analysis of covariance (ANCOVA) is appropriate when examining for differences in a continuous dependent variable between groups, while controlling for the effect of additional variables. The “C” in ANCOVA denotes that a covariate is being inputted into the model, and this covariate examination can be applied to a between-subjects design, a within-subjects design, or a mixed-model design. ANCOVAs are frequently used in experimental studies when the researcher wants to account for the effects of an antecedent (control) variable. MANOVA: Finally, a multivariate analysis of variance (MANOVA) is an extension on the ANOVA, and is appropriate when examining for differences in multiple continuous level variables between groups. For example, a MANOVA would be applicable if assessing for differences between ethnicities in job satisfaction AND intrinsic motivation levels of participants. In this example, job satisfaction and intrinsic motivation are the continuous level dependent variables. The MANOVA can be conducted with multiple independent variables, and can also include covariates (i.e., MANCOVA). References All experiments examine some kind of variable(s). A variable is not only something that we measure, but also something that we can manipulate and something we can control for. To understand the characteristics of variables and how we use them in research, this guide is divided into three main sections. First, we illustrate the role of dependent and independent variables. Second, we discuss the difference between experimental and non-experimental research. Finally, we explain how variables can be characterised as either categorical or continuous. Dependent and Independent VariablesAn independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called an outcome variable. Imagine that a tutor asks 100 students to complete a maths test. The tutor wants to know why some students perform better than others. Whilst the tutor does not know the answer to this, she thinks that it might be because of two reasons: (1) some students spend more time revising for their test; and (2) some students are naturally more intelligent than others. As such, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. The dependent and independent variables for the study are: Dependent Variable: Test Mark (measured from 0 to 100) Independent Variables: Revision time (measured in hours) Intelligence (measured using IQ score) The dependent variable is simply that, a variable that is dependent on an independent variable(s). For example, in our case the test mark that a student achieves is dependent on revision time and intelligence. Whilst revision time and intelligence (the independent variables) may (or may not) cause a change in the test mark (the dependent variable), the reverse is implausible; in other words, whilst the number of hours a student spends revising and the higher a student's IQ score may (or may not) change the test mark that a student achieves, a change in a student's test mark has no bearing on whether a student revises more or is more intelligent (this simply doesn't make sense). Therefore, the aim of the tutor's investigation is to examine whether these independent variables - revision time and IQ - result in a change in the dependent variable, the students' test scores. However, it is also worth noting that whilst this is the main aim of the experiment, the tutor may also be interested to know if the independent variables - revision time and IQ - are also connected in some way. In the section on experimental and non-experimental research that follows, we find out a little more about the nature of independent and dependent variables. Experimental and Non-Experimental Research
Categorical and Continuous VariablesCategorical variables are also known as discrete or qualitative variables. Categorical variables can be further categorized as either nominal, ordinal or dichotomous.
Continuous variables are also known as quantitative variables. Continuous variables can be further categorized as either interval or ratio variables.
Ambiguities in classifying a type of variableIn some cases, the measurement scale for data is ordinal, but the variable is treated as continuous. For example, a Likert scale that contains five values - strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree - is ordinal. However, where a Likert scale contains seven or more value - strongly agree, moderately agree, agree, neither agree nor disagree, disagree, moderately disagree, and strongly disagree - the underlying scale is sometimes treated as continuous (although where you should do this is a cause of great dispute). It is worth noting that how we categorise variables is somewhat of a choice. Whilst we categorised gender as a dichotomous variable (you are either male or female), social scientists may disagree with this, arguing that gender is a more complex variable involving more than two distinctions, but also including measurement levels like genderqueer, intersex and transgender. At the same time, some researchers would argue that a Likert scale, even with seven values, should never be treated as a continuous variable. What type of test do you use when your dependent and independent variable are both categorical?If the dependent variable is normally distributed and you have a categorical independent variable is paired then you use a PAIRED T TEST.
What test is run for categorical and continuous variables?When your experiment is trying to draw a comparison or find the difference between one categorical (with more than two categories) and another continuous variable, then you use the ANOVA (Analysis of Variance) test.
Is Anova for continuous or categorical variables?Data Level and Assumptions
In ANOVA, the dependent variable must be a continuous (interval or ratio) level of measurement. The independent variables in ANOVA must be categorical (nominal or ordinal) variables.
What test is used for categorical to categorical analysis?The Pearson's χ2 test (after Karl Pearson, 1900) is the most commonly used test for the difference in distribution of categorical variables between two or more independent groups.
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