# RES 710 University of Phoenix Statistics Discussion

Please review attachment

https://methods.sagepub.com/which-stats-test

Part 1: Discussion Question

An important component to quantitative research is choosing the correct statistical test. The

University Library offers several Sage Research Methods resources to help you in your endeavors.

Using the “Sage Tools – Which Stats Test?” link from the University Library and your chosen

variables, complete the “Which Stats Test?” survey to decide which statistical tests could be most

beneficial to understanding the relationship between your variables.

Write a 250- to 300-word response to the following:

•

•

Discuss the process of choosing a statistical test using the tools from Sage and your Salkind

& Frey (2019) text.

Explain what impact variable types have on statistical tests.

Part 2: Student Responses

Student response 1:Review the classmates’ posts and respond to at least one in a

minimum of 150 words. Explain why you agree or disagree. Then, share an example from

your professional experience to support your assertions.

The levels of measurement show the method for classifying a specification. Each represents a

characteristic that differs by whether it is a degree of something and how specialists interpret

the magnitude. The nominal level indicates one where no feature is higher or lower than

another, like a gender, where men and women are distinct types of people, and none is more

advantageous. The ordinal one also states non-numerical characteristics but mentions ranks;

an example might be the Likert scale, where there are the lowest and highest positions. The

other two levels of measurement are where changes are quantifiable (Norman, 2010).

The interval level of measurement identifies degree standards where a change of one unit

signifies the same in all, regardless of the stated number. Nevertheless, unlike the ratio, it does

not have an absolute bottom level. An example is a date, where a difference of one day is

always equal to 24 hours, but the year 0 does not denominate an absence of an aspect.

Researchers can utilize such numbers for additions and subtractions. However, they cannot

utilize such variables as multipliers because scholars solely find differences (Allanson & Notar,

2020).

The ratio level of measurement compares a quantity with a size worth one unit. Time length is

an example of a continous variable that enters into this category. When an entity tells how long

something took, it divides the amount by a standard such as the second, a day, or one year,

among many others. A discrite variable that enters into this level of measurement might be the

population size, as zero inhabitants signify there are no items in thst location. Unlike the

interval level, it can be multiplied or divided, and specalists can note a real absence.

References:

Allanson, P. E., & Notar, C. E. (2020). Statistics as measurement: 4 scales/levels of

measurement. Education Quarterly Reviews, 3(3)

Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in

Health Sciences Education : Theory and Practice, 15(5)

Student response 2:Review the classmates’ posts and respond to at least one in a

minimum of 150 words. Explain why you agree or disagree. Then, share an example from

your professional experience to support your assertions.

The process of choosing a statistical test can be difficult, and there are many factors to

consider. The type of data, the research question, and the type of analysis are all important

factors. In, general, quantitative research is concerned with relationships between variables,

and the type of statistical test used will be determined by the type of variables involved (Salkind

& Frey, 2019).

There are two main types of variables: categorical and continuous. Categorical variables are

those that can be divided into groups, and they are often used to represent characteristics like

gender or ethnicity. Continuous variables are those that can take on any value within a range,

and they are often used to represent things like age or income.

The type of statistical test used will be determined by the type of variables involved. For

example, if the research question is concerned with the relationship between two categorical

variables, then the chi-squared test would be used. If the research question is concerned with

the relationship between a categorical variable and a continuous variable, then the t-test would

be used.

The independent variables chosen are race (nominal) and healthcare accessibility (ordinal). The

dependent variable is the mortality rate (ratio). The suggested test is ordinal regression. Ordinal

regression is a member of the family of regression analysis. As a predictive analysis, ordinal

regression describes data and explains the relationship between one dependent variable and

two or more independent variables.

The impact of variable types on statistical tests is that the type of statistical test used will be

determined by the type of variables involved. Categorical variables are often used to represent

things like age and income. The type of statistical test used will be determined by the type of

research question being asked.

Reference:

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2021). Social statistics for a diverse

society (9th ed.). Sage Publications.

Salkind, N. J., & Frey, B. B. (2019). Statistics for people who (think they) hate statistics: Using

Microsoft Excel. Sage publications.

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