This Assignment Consists Of Three Parts: For Parts 1 And 2

This assignment consists of three parts: For Parts 1 and 2: Go to the NCU

This assignment consists of three parts: For Parts 1 and 2: Go to the NCU library from your home page. Examine the guide to primary and secondary sources to see what is available. This can be found under LibGuides → Research Process → Primary and Secondary Resources. Then go back to the library home page and either conduct a search using the Roadrunner Search or go to the Statista database in the A-Z Databases list to locate data to answer the following questions. The library's guide on Statistics may also be helpful.

Part 1: Internet Data Usage and Age Over Time

Does there appear to be a relationship between adult internet usage and age between 2000 and 2018?

Part 2: Data Breaches in Health Care Over Time

Has the incidence of data breaches in the health/medical sector increased in the last five years?

Part 3: Archived Dataset

Identify one dataset (also referred to as a database) publicly available for research. A list of possible sources is included in this week’s resources or choose one that may be appropriate for your research study topic. You do not need to open the dataset that includes raw numbers.

To operationalize the dataset, gather the following information about the dataset. Dataset information is most likely contained in a document separate from the dataset and may be identified as a database dictionary, codebook, or program record layout. For the assignment response, include the following information using the headings and format outlined here:

Dataset Name

Enter text on this line.

Dataset Source

Enter text on this line.

Dataset Location

Enter text on this line. (include link if available)

Dataset Overview

Include three to five sentences to provide context for the Reader. This may include industry, focus, and original purpose for collecting the data.

Dataset Timeframe

Enter text on this line.

Four Variables

For each of the four variables, separately list the following information, using separate headings for each variable. If the information is not available, then indicate ‘not available.’ Follow the template below for each of the variables/constructs.

Variable Name [include dataset abbreviation if appropriate]

Definition of the Variable

Enter text here.

Source of Data

(This might be self-reported by business, self-reported by survey, observation, etc.)

Scoring of the Variable

Enter text here. For variables, this might be age in years, number of defects per quarter, group membership, etc.

For constructs, this might be computation of multiple questions on a survey or multiple variables that make up the construct.

Level of Measurement

Enter text here. Below is a reminder of the measurement levels you learned in Statistics 1. Do not include the measurement level definitions in your assignment response.

  • Nominal: Nominal data are measured at the discrete level depicting independent categories with no underlying order. Examples: sex, race, organizational department membership.
  • Ordinal: Ordinal data are measured at the discrete level with categories implying a hierarchy. Examples: education level, age groups, Likert scales.
  • Interval: Interval data are continuous with equal intervals and no absolute zero point. Examples: time, temperature, test scores.
  • Ratio: Ratio data are continuous with equal intervals and an absolute zero point. Examples: age, income, defects per lot.

Score Range and Interpretation

Enter text here. Include the total possible range of scores for the variable and how to interpret the range. The score range and interpretation might be ‘Six-point Likert scale with ‘1’ meaning “Not Satisfied at All,” and ‘6’ meaning “Completely Satisfied,” or a four-point ordinal scale with ‘1’ meaning no use, ‘2’ little use, ‘3’ moderate use, and ‘4’ a lot of use. For nominal variables, identify the coding scheme (e.g., 1 = male; 2 = female).

Reflection

Enter text here. Describe advantages, disadvantages, challenges, and benefits that you feel may should be considered if you were to use an archived dataset for your dissertation research study.

Length: Your paper should be at least 5, but may be as long as 10 pages, if the table and/or figures are included. This does not include the title and reference page. You are encouraged to make effective use of tables and/or figures in your presentation. References: Include a minimum of three (3) scholarly sources. Your presentation should demonstrate thoughtful consideration of the ideas and concepts presented in the course and provide new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral University's Academic Integrity Policy.

Paper For Above instruction

In the rapidly evolving landscape of digital technology, understanding patterns of internet usage and data security concerns is essential for contemporary research and policy development. This comprehensive analysis synthesizes data trends related to adult internet usage and age, data breaches within healthcare sectors, and explores the utilization of archived datasets for research purposes, emphasizing their significance and methodological considerations.

Part 1: Internet Data Usage and Age Over Time

Analyzing data from 2000 to 2018 reveals notable shifts in adult internet usage across different age groups. The period encompasses the advent and broad adoption of high-speed internet, smartphones, and social media, all of which have contributed to changing usage patterns. Studies indicate that younger adults, particularly those between 18-29, have historically demonstrated higher internet usage rates, which could be attributed to greater familiarity with technology and mobile device accessibility (Smith, 2017). However, over time, usage among older populations has increased significantly, especially with the proliferation of user-friendly devices and targeted communication strategies (Pew Research Center, 2018). The data suggests a potential inverse relationship between age and internet usage, with younger cohorts leading usage rates but older demographics closing the gap gradually. This trend underscores the importance of considering age as a factor influencing digital engagement and highlights shifts in accessibility and literacy across generations.

Part 2: Data Breaches in Healthcare Over Time

Examining recent patterns of data breaches in the healthcare sector over the last five years reveals a concerning upward trend. According to recent reports (Protenus, 2022), the number of healthcare data breaches has increased dramatically, with notable incidents involving ransomware attacks, phishing, and insider threats. The healthcare industry’s increasing reliance on electronic health records (EHRs) and interconnected systems has expanded the attack surface for malicious actors (U.S. Department of Health & Human Services, 2021). Furthermore, breaches can lead to significant financial losses, compromised patient privacy, and erosion of trust. The surge in breaches aligns with broader cybersecurity vulnerabilities faced by organizations managing sensitive health data, driven by evolving tactics of cybercriminals and inadequate security measures (Kshetri & Voas, 2020). Analyzing these trends emphasizes the critical need for increased cybersecurity investments, robust policies, and continuous monitoring in healthcare environments to mitigate risks associated with data breaches.

Part 3: Archived Dataset and Its Operationalization

For this part, I have chosen a publicly accessible dataset from the National Health and Nutrition Examination Survey (NHANES), which provides comprehensive health-related information collected periodically. The dataset's name is “NHANES 2017–2018,” sourced by the Centers for Disease Control and Prevention (CDC). The dataset is publicly available via the NIH Data Sharing Repository. NHANES aims to evaluate the health and nutritional status of adults and children in the United States, and its data collection includes demographics, health behaviors, laboratory results, and health conditions (CDC, 2020).

The dataset timeframe covers the years 2017 to 2018, aligning with the administration period for which data is available. The core variables I examined include age, gender, BMI, and hypertension status, each operationalized as follows:

Variable 1: Age [NHANES Age]

  • Definition: The age of the participant at the time of data collection, measured in years.
  • Source of Data: Self-reported by participants during interviews.
  • Scoring: Continuous variable ranging from 0 to 120.
  • Level of Measurement: Ratio
  • Score Range and Interpretation: 0–120; higher values indicate older age, with no ceiling effects.
  • Reflection: Using age as a continuous ratio variable offers detailed analysis but requires careful consideration of outliers and data quality. Challenges include handling missing age data and ensuring accurate reporting.

Variable 2: Gender [NHANES Gender]

  • Definition: Participant's biological sex, categorized as male or female.
  • Source of Data: Self-reported during interviews.
  • Scoring: Nominal; coded as 1 = Male, 2 = Female.
  • Level of Measurement: Nominal
  • Score Range and Interpretation: 1–2; categories represent gender identity, with no inherent order.
  • Reflection: Utilizing nominal coding simplifies analysis but may overlook gender diversity considerations present in modern datasets. Limitations include binary classification and cultural variations in reporting gender.

Variable 3: BMI [NHANES BMI]

  • Definition: Body mass index calculated from height and weight, indicates nutritional status.
  • Source of Data: Laboratory measurements during physical examinations.
  • Scoring: Continuous variable measured in kg/m².
  • Level of Measurement: Ratio
  • Score Range and Interpretation: Typically ranges from under 15 to over 60; higher BMI values signal overweight or obesity statuses.
  • Reflection: As a ratio variable, BMI facilitates detailed health risk analysis, but potential issues include measurement errors in physical assessment and unit conversions.

Variable 4: Hypertension Status [NHANES Hypertension]

  • Definition: Whether a participant has been diagnosed with hypertension or exhibits elevated blood pressure during measurement.
  • Source of Data: Self-report and physical measurement.
  • Scoring: Nominal; coded as 0 = No, 1 = Yes.
  • Level of Measurement: Nominal
  • Score Range and Interpretation: 0–1; indicates absence or presence of hypertension.
  • Reflection: Binary coding simplifies analysis but limits nuance, such as blood pressure severity or medication adherence. Incorporating continuous blood pressure readings might enrich insights.

Conclusion

Utilizing archived datasets like NHANES offers invaluable opportunities for health research, enabling detailed analyses of demographic and health variables over time. While such datasets have numerous advantages, including representativeness and comprehensive data collection, challenges like data missingness, measurement errors, and binary limitations must be carefully managed. Overall, thoughtful operationalization of variables is essential for robust, meaningful research outcomes.

References

  • Centers for Disease Control and Prevention (CDC). (2020). National Health and Nutrition Examination Survey. https://www.cdc.gov/nchs/nhanes/index.htm
  • Kshetri, N., & Voas, J. (2020). Ransomware attacks on healthcare organizations. Computer, 53(12), 78-85.
  • Pew Research Center. (2018). Internet and technology use among older adults. https://www.pewinternet.org/2018/05/17/technology-use-among-seniors/
  • Protenus. (2022). 2022 Healthcare Data Breach Report. https://www.protenus.com/insights/reports/healthcare-data-breach-report
  • Smith, A. (2017). Older adults and internet usage. Pew Research Center. https://www.pewresearch.org/internet/2017/05/17/older-adults-and-internet-use/
  • U.S. Department of Health & Human Services. (2021). Health Sector Cybersecurity Coordination Center. https://www.hhs.gov/sites/default/files/hhs-cyber-rare.pdf
  • National Institute of Health (NIH). (2021). NIH Data Sharing Repository. https://datasharing.nih.gov
  • Wang, Y., McPherson, R., & Harris, T. (2020). Impact of cybersecurity threats in healthcare. Journal of Medical Internet Research, 22(2), e15512.
  • World Health Organization (WHO). (2018). The digital revolution in health care. WHO Bulletin, 96(7), 450-451.
  • Yuan, J., & Weng, J. (2019). Operational challenges of archived health datasets. Data & Knowledge Engineering, 115, 123-135.

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