Summery
This article will explain a clear definition of population structures essential to research and provide some examples of how quantitative research could use these structures. We will learn how to define the population of interest from the collected data. Therefore, this study aims to identify definition of the population structures for research, with their unit of analysis related to dependent and independent variables related to the collected samples.
Unit of analysis & Unit of observation
A unit of analysis in research is the "entity that you wish to say something about at the end of your study, and it is considered the focus of your study." (DeCarlo, 2018) So. The unit of analysis is very much related to our research questions, but the unit of observation depends on the data collection method or the focused group and type of data we collect. There is three common units of analysis are used by researchers, including "Individuals, Groups, Organizations" (DeCarlo, 2018, p. 3)
For example, suppose we would like to collect information about the addicted students to the electronic cigarette vape. In that case, we can gather information from the fun clubs (e.g., dance clubs, party clubs, etc.), or we may choose study clubs (e.g., libraries, group tuition clubs, etc.), or we could focus on sports clubs (e.g., baseball clubs, swimming clubs, etc.). This study identifies and classifies students based on their social membership group to find students as male or female and how students' addictions differ and are similar, based on their attendance group. So, we can categorize students into those clubs they are members of.
As the student's member group shows, students have more opportunities to use vaping in the fun clubs than in the study clubs. In this study, data is collected by survey or direct observation, so the unit of analysis is an individual student, and the unit of observation is also the individual student.
Group's performance on a population of interest
Based on our unit analysis, our research questions with a theoretical study of our student's samples to check their electronic vape addiction are shown in Table 1. We can manipulate independent and dependent variables for our "test-case-and-effect relationship" (Bhandari, 2022, para. 1), where independent variables are the cause, and dependent variables, on the other hand, is the effect. (Bhandari, 2022, p. 1) We can measure independent variables directly from our unit of analysis (students), no matter what kind of membership group they belong to. However, we (researchers) cannot manipulate the dependent variables as the outcome depends on the independent variables. In our case of students, the social group members have different effects on our collected data from students. The number of individuals who use electronic vaping in a party club may be higher than the other membership clubs such as library groups because of the effects of those clubs and characteristics of those gatherings.
How to identify independent and dependent research variables and examples
We identify independent variables in the research question/hypothesis that "manipulates, causes or influences something or a reaction." (Bhandari, 2022) And the dependent variables can be identified through the research question/hypothesis that "sees the result, effect or outcome of changing the independent variable." (Bhandari, 2022)
Here is the experiment where we have one independent and one dependent variable:
Example 1: How does the type of membership group impact the use more electronic vaping?
Independent Variable: Attending to Library group.
Dependent Variable: Time of using vapes.
Example 2: What is the effect of fast food on Cholesterol?
Independent Variable: Using fast food
Dependent Variable: Cholesterol level
Here is the experiment where we have one independent and two dependent variables.
Example 1: Measure how playing videos games can impact teenagers' memory and mood at the same time. So, mood and memory are two dependent variables related to the number of games a teen would play.
Here is the experiment where we have two independent and one dependent variable:
Example 1: Measure how playing videos games can impact teenagers' mood, separated by girls and boys (based on sex), at the same time. So, mood and memory are two dependent variables related to the number of games a teen would play.
Part 2, Variance between groups and within groups
ANOVA (analysis of variance) identifies the ratio of between-group variation to within-group variation when there is a significant difference between the groups. "A one-way ANOVA uses the following null and alternative hypotheses: H0: All group means are equal. HA: At least one group mean is different from the rest." (Z, 2021, p. 1) ANOVA measures in between-group show the total variation between each group, while it shows the total variation in the individual values of each group in within-group variation measurement. In our experiment of measuring the addiction to electronic vaping on students, the between-group measurement is significant in using vaping. For example, students use vaping in fun groups significantly higher than the students in a library group. But, within the individual group, the variance shows that sex is a significant element in measuring vaping addiction.
Calculating Within-Group and Between Group Variation in ANOVA
To measure the variance using our primary model to find students studying model within-group and between-group, we try to calculate three different membership groups that lead to different mean scores in 30 students by randomly them into three groups of ten students. Library group membership includes ten students, Dance club membership with ten students, and Basketball club membership with another ten students, shown in the following table. (Z, 2021)
|
|
Studying score in Library group |
Studying score in Dance club membership |
Studying score in Basketball club membership |
|
|
75 |
78 |
82 |
|
|
77 |
78 |
82 |
|
|
78 |
79 |
84 |
|
|
78 |
81 |
86 |
|
|
79 |
81 |
86 |
|
|
81 |
82 |
87 |
|
|
81 |
83 |
87 |
|
|
83 |
85 |
89 |
|
|
86 |
86 |
90 |
|
|
87 |
88 |
94 |
|
Group Mean |
80.5 |
82.1 |
86.7 |
|
Overall Mean |
83.1 |
|
|
Using abobe date set table, we use Σnj(Xj – X..)2 to calculate Variation Between Group and it will be: 10(80.5-83.1)2 + 10(82.1-83.1)2 + 10(86.7-83.1)2 = 207.2.
For Variation Within
Group, we use Σ(Xij – Xj)2 , and for the three groups it will be:
Group 1: (75-80.5)2 + (77-80.5)2 + (78-80.5)2 + (78-80.5)2 + (79-80.5)2 + (81-80.5)2 + (81-80.5)2 +
(83-80.5)2 + (86-80.5)2 + (87-80.5)2 = 136.5
Group 2: (78-82.1)2 + (78-82.1)2 + (79-82.1)2 + (81-82.1)2 + (81-82.1)2 + (82-82.1)2 + (83-82.1)2 +
(85-82.1)2 + (86-82.1)2 + (88-82.1)2 = 104.9
Group 3: (82-86.7)2 + (82-86.7)2 + (84-86.7)2 + (86-86.7)2 + (86-86.7)2 + (87-86.7)2 + (87-86.7)2 +
(89-86.7)2 + (90-86.7)2 + (94-86.7)2 = 122.1
So, the total variance in Within Group Variation we have: 136.5
+ 104.9 + 122.1 = 363.5
Using ANOVA to test the above dataset we will have the following results: (Z, 2021)
References
Bhandari, P. (2022, February 18). Independent and dependent variables. Scribbr. Retrieved 2022, from https://www.scribbr.com/methodology/independent-and-dependent-variables/
Casteel, A., & Bridier, N. L. (2021). Describing populations and samples in doctoral student research.International Journal of Doctoral Studies, 16, 339-362. https://doi.org/10.28945/4766
DeCarlo, M. (2018a, August 7). 7.3 Unit of analysis and unit of observation – Scientific Inquiry in Social Work. Pressbooks. Retrieved 2022, from https://scientificinquiryinsocialwork.pressbooks.com/chapter/7-3-unit-of-analysis-and-unit-of-observation/
MartÃnez-Mesa, J., González-Chica, D. A., Bastos, J. L., Bonamigo, R. R., & Duquia, R. P. (2014). Sample size: how many participants do I need in my research?. Anais brasileiros de dermatologia, 89(4), 609–615. https://doi.org/10.1590/abd1806-4841.20143705
Zhenghong Tang, Samuel D. Brody, Courtney Quinn, Liang Chang & Ting Wei (2010) Moving from agenda to action: evaluating local climate change action plans, Journal of Environmental Planning and Management, 53:1, 41-62, DOI: 10.1080/09640560903399772
Z. (2021, December 7). Within-Group vs. Between Group Variation in ANOVA. Statology. Retrieved 2022, from https://www.statology.org/within-between-group-variation-anova/
Diagrams, Tables, and Definitions
Table 1. Research questions (a theoretical study) of our student's addiction. (DeCarlo, 2018)
|
Research question |
Unit of analysis |
Data collection |
Unit of observation |
Statement of findings |
|
“Which students are most likely to be addicted to their electronic gadgets?” |
Individuals |
“Survey of students on campus” |
Individuals |
“New Media majors, men, and students with high socioeconomic status are all more likely than other students to become addicted to their electronic gadgets.” |
|
“Do certain types of social clubs have more gadget-addicted members than other sorts of clubs?” |
Groups |
|
Individuals |
“Clubs with a scholarly focus, such as social work club and the math club, have more gadget-addicted members than clubs with a social focus, such as the 100-bottles-of- beer-on-the-wall club and the knitting club.” |
|
“How do different colleges address the problem of electronic gadget addiction?” |
Organizations |
“Content analysis of policies” |
Individuals |
“Campuses without strong computer science programs are more likely than those with such programs to expel students who have been found to have addictions to their electronic gadgets.” |
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