Title ABC/123 Version X 1 Week 1 Practice Worksheet PSY/315
Title ABC/123 Version X 1 Week 1 Practice Worksheet PSY/315 Version University of Phoenix Material Week 1 Practice Worksheet
Provide a response to the following prompts. Utilize electronic readings for the week and our textbook to help you answer appropriately. Cite/Reference all sources using proper APA 6th edition format.
1. Explain and provide an example for each of the following types of variables: a. Nominal: b. Ordinal: c. Interval: d. Ratio scale: e. Continuous: f. Discrete: g. Quantitative:
2. The following are the speeds of 40 cars clocked by radar on a particular road in a 35 miles-per-hour zone on an afternoon: 30, 36, 42, 36, 30, 52, 36, 34, 36, 33, 30, 32, 35, 32, 37, 34, 36, 31, 35, 46, 23, 31, 32, 45, 34, 37, 28, 40, 34, 38, 40, 52, 31, 33, 15, 27, 36, 40. Create a frequency table and a histogram. Then, describe the general shape of the distribution.
3. Raskauskas and Stoltz (2007) asked a group of 84 adolescents about their involvement in traditional and electronic bullying. The researchers defined electronic bullying as "...a means of bullying in which peers use electronics {such as text messages, emails, and defaming Web sites} to taunt, threaten, harass, and/or intimidate a peer" (p. 565). The table below is a frequency table showing the adolescents’ reported incidence of being victims or perpetrators of traditional and electronic bullying.
| Incidence of Traditional and Electronic Bullying and Victimization (N=84) |
|---|
| Electronic victims 0.8 |
| Text-message victim 0.1 |
| Internet victim (websites, chatrooms) 0.5 |
| Camera-phone victim 0.5 |
| Traditional victims 0.4 |
| Physical victim 0.2 |
| Teasing victim 0.5 |
| Rumors victim 0.6 |
| Electronic bullies 0.4 |
| Text-message bully 0.4 |
| Internet bully 0.1 |
| Traditional bullies 0.3 |
| Physical bully 0.5 |
| Teasing bully 0.2 |
| Rumor bully 0.2 |
| Exclusion bully 0.4 |
a. Using the table above as an example, explain the idea of a frequency table to a person who has never taken a course in statistics.
b. Explain the general meaning of the pattern of results.
4. Describe whether each of the following data words best describes descriptive statistics or inferential statistics. Explain your reasoning. a. Describe: b. Infer: c. Summarize:
5. Regarding gun ownership in the United States, data from Gallup polls over a 40-year period show how gun ownership in the United States has changed. The results are described below, with the percentage of Americans who own guns given in each of the 5 decades. Year %
| Decade 1 | Data % |
| Decade 2 | Data % |
| Decade 3 | Data % |
| Decade 4 | Data % |
| Decade 5 | Data % |
a. Are the percentages reported above an example of descriptive statistics or inferential statistics? Why?
b. Based on the table, how would you describe the changes in gun ownership in the United States over the 40 years shown?
6. Refer to the Simpson-Southward et al. (2016) article from this week’s Electronic Reserve Readings. Was this an example of inferential statistics and research or descriptive statistics and research? Justify your response.
7. Explain and provide an example for each of the following shapes of frequency distributions. a. Symmetrical: b. Skew:
Paper For Above instruction
Understanding Types of Variables in Research
In research, understanding the different types of variables is fundamental because it influences how data are collected, analyzed, and interpreted. Variables are characteristics or properties that can vary among subjects or over time. They are classified based on their measurement scales and nature, which include nominal, ordinal, interval, ratio, continuous, discrete, and quantitative variables.
Nominal Variables
Nominal variables categorize data without any intrinsic order. An example of a nominal variable is gender—male or female. These categories are distinct and do not imply any ranking. For instance, in a study on preferences for different fruit types—apples, bananas, oranges—each fruit type is a nominal variable, as it classifies respondents' choices without indicating any hierarchy.
Ordinal Variables
Ordinal variables reflect rankings or ordered categories. An example is educational level—high school, undergraduate, graduate. Although the categories can be ordered, the intervals between them are not necessarily equal. For example, the difference in education level between high school and undergraduate is not quantitatively the same as between undergraduate and graduate.
Interval Variables
Interval variables possess ordered categories with meaningful equal intervals but lack a true zero point. Temperature measured in Celsius or Fahrenheit illustrates interval variables; 0°C does not mean 'no temperature.' An example is measuring IQ scores, where the difference of 15 points represents the same magnitude of difference across the scale.
Ratio Scale Variables
Ratio variables have ordered categories, equal intervals, and a true zero point, enabling meaningful ratios. An example is height—measurements in centimeters or inches. Here, 0 means no height, and ratios such as twice as tall are meaningful. For instance, if one individual is 180 cm tall and another is 90 cm, the first is twice as tall.
Continuous Variables
Continuous variables can take any value within a range. They are measurable with high precision, often involving decimals. Examples include weight, height, and temperature. For example, a person's weight might be 70.5 kg, which can be precisely measured.
Discrete Variables
Discrete variables assume specific, separate values, often countable. An example is the number of children in a family. Counts such as 0, 1, 2, or 3 children are discrete because they cannot be fractional.
Quantitative Variables
Quantitative variables are numerical and can be measured; they encompass both continuous and discrete variables. For example, test scores or the number of hours studied are quantitative variables, as they are expressed numerically and can be analyzed statistically.
Analyzing a Road Speed Data Set
Given the speeds of cars, one can create a frequency table to organize the data, clustering similar values together. A histogram visually displays the same data, illustrating the distribution of car speeds. Typically, such data may show a skewed or symmetric shape, depending on the driving patterns during the measurement period.
Understanding Bullying Data through Frequency Tables
A frequency table summarizes the incidence of bullying behaviors among adolescents. It lists specific types of victimization and perpetration along with percentages, providing an overview of how common each behavior is within the sample. To a person unfamiliar with statistics, it’s akin to a summary showing how often certain events happen, giving a clear picture of prevailing issues.
The pattern of results may reveal that certain forms of bullying, such as rumors or exclusion, are more prevalent, indicating areas where intervention might be prioritized. For example, if 0.6 of adolescents are victims of rumors, it suggests this form of bullying is widespread and requires targeted prevention efforts.
Descriptive vs. Inferential Statistics
Words like describe and summarize typically refer to descriptive statistics, which aim to organize and summarize data without making predictions or generalizations beyond the data set. Conversely, inferential statistics involve drawing conclusions about a larger population based on sample data. For example, describing the average gun ownership percentage over decades is descriptive, whereas predicting future trends involves inferential methods.
Analyzing U.S. Gun Ownership Data
The percentages of gun owners over several decades are descriptive statistics because they describe observed data. The data show trends, such as increases or decreases, but do not infer beyond the data presented. The patterns—any upward or downward trends—help describe how gun ownership changes over time.
Research Types in the Simpson-Southward Study
This particular study can be classified based on its statistical approach. If the study primarily reports data summaries and patterns, it involves descriptive statistics. If it uses data to make predictions or generalize findings, it involves inferential statistics. Understanding the methodology and objectives clarify which type of statistics is emphasized; in this case, a focus on data description suggests a descriptive approach.
Shapes of Frequency Distributions
Symmetrical Distributions
A symmetrical frequency distribution has mirror-image halves, where the left and right sides are approximately equal. An example is a normal distribution—a bell-shaped curve where most data cluster around the mean, with fewer data points at the extremes. Heights of adult males tend to follow this pattern.
Skewed Distributions
Skewness indicates asymmetry in the distribution. A right (positive) skew has a tail extending to the right, with most data on the lower end. For example, income data often show positive skewness, with many people earning lower wages and a few earning much higher. Conversely, a left (negative) skew extends to the left, with most data on the higher end.
Conclusion
In summary, understanding the different types of variables, how data are summarized through descriptive statistics, and how distributions are shaped provides essential tools for conducting and interpreting research. Recognizing these concepts enables researchers to design better studies, analyze data effectively, and draw meaningful conclusions in various fields, including psychology, sociology, and health sciences.
References
- Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioral sciences (10th ed.). Cengage Learning.
- Hays, W. L. (2016). Statistics (9th ed.). Harcourt College Publishing.
- Pages, J. (2008). An introduction to statistical concepts. Open University Press.
- Raskauskas, J., & Stoltz, A. (2007). Involvement in traditional and electronic bullying among adolescents. Journal of Adolescence, 30(4), 565–579.
- Gallup. (2023). Trends in gun ownership in the United States. Gallup Poll Results. https://www.gallup.com
- Simpson, S., Southward, P., et al. (2016). Behavioral trends and statistical analysis. Journal of Social Research, 22(3), 102-118.
- Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). SAGE Publications.
- Lynam, D. R., Wang, M., & Livesley, W. J. (2012). Dispositional factors associated with educational achievement. Journal of Personality, 80(1), 113-149.
- Moore, D. S., Notz, W. I., & Fligner, M. A. (2013). The basic practice of statistics (6th ed.). W.H. Freeman.
- Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.