HCA 383 Biostats Lab Assignment 2 — Due By 11 ✓ Solved

Hca 383 Biostats Lab Assignment 2this Assignment Isdue By 1159 P

HCA 383 Biostats Lab Assignment 2 This assignment is due by 11:59 pm, April 5th, 2018. Important: Please make sure you are using the right dataset in each question. Datasets will be referred to by their names in each question. Wherever necessary, variables associated with any part of the analysis will be mentioned by their names as they appear in the dataset and will be enclosed in parentheses; for example: How many people are Hispanic in the sample ( RACE ). This means you need to perform the relevant procedures using a variable called RACE .

When you produce SPSS output in your response make sure you submit only the relevant part of the output. SPSS produces a lot of information that you do not need to submit. SPSS output can be copied and pasted into a Word document. But remember, submit only the relevant part of the output! Be sure to assign missing values so that you can account for people who responded “don’t know” or refused to answer the question.

Steps are given below.

  1. Use the dataset you created in Assignment 1 to answer the following questions: [Create it again if you did not save it---more practice!] (a) What percentage of the individuals in the sample did not have private or public insurance? ( INS_TYPE ). (2 points)
  2. (b) What is the average age of the individuals in the sample? ( AGE ). (2 points)
  3. (c) What is the total percentage of respondents who were either unsatisfied or very unsatisfied with the services? ( PTN_SAT ). (2 points)
  4. (d) What percentage of the sample did not graduate from high school? ( EDUC ) (2 points)

Collection of patient data

Each time a patient enters a health care facility seeking care for him/herself there is a certain amount of data that is collected from each patient. Some of this data are quantitative data and some data are qualitative data. (a) What are the differences between quantitative data and qualitative data? (4 points)

(b) Give three examples of each type of data that are collected in a health care encounter (that is: when a person goes to see the health provider). (3 points)

(c) State at least THREE different techniques that can be used in the collection of SURVEY data. (3 points)

Use of dataset “webhealth.sav” for analysis

First, follow these steps to assign missing values: Let's use the 'inc' variable as an example:

  1. Go to the Variable View of your dataset
  2. Look for “Missing” column next to the “Values” column
  3. Scroll down until you find the “inc” variable
  4. Click on the “Values” for that variable and review all the values
  5. Identify which numbers should be used as missing values (e.g., “don’t know”, refusal codes)
  6. Click on “None” in the Missing column for that variable and add the identified missing value codes, each in its own box
  7. Click OK

Now, answer the following questions:

  1. (a) What percentage of the respondents were residents of suburban area? (usr). (2 points)
  2. (b) What is the difference between the “Percent” and “Valid Percent” columns on SPSS output? Give a reference for your answer. (3 points)
  3. (c) Find what weekly frequency of using internet or email from home is represented by 21.3% of respondents and 5.4%. (q8a). (3 points)
  4. (d) What percentage of respondents have ever used their cell phones to access health or medical information? (q15). (2 points)
  5. (f) What is the average age of the respondents? What is the range of their age? (age). Be sure to exclude missing data. (2 points)
  6. (g) What is the total percentage of persons who were in school (either full-time or part-time students)? (stud). (2 points)

Sample Paper For Above instruction

Introduction

Understanding basic statistical concepts and the analysis of healthcare datasets are essential skills in health research. This paper demonstrates how to analyze datasets using SPSS, focusing on calculating percentages, averages, and understanding data types and collection techniques within healthcare contexts. Ethical data handling, especially assigning missing values appropriately, is emphasized throughout the analysis to ensure accuracy and validity.

Analysis of Healthcare Data from Assignment 1

Percentage without Insurance

Using the variable INS_TYPE, the analysis reveals the proportion of individuals lacking private or public insurance. The calculation involves coding 'no insurance' responses as missing or specific codes if categorized distinctly. Suppose, out of 200 respondents, 40 reported no insurance. The percentage is calculated as (40/200) x 100 = 20%, indicating a significant segment without insurance coverage, which has implications for health disparities.

Average Age of Participants

By analyzing the AGE variable, the mean age is found by summing all age values (excluding missing data) and dividing by the total responses. For instance, summing ages yields 10,000 years across 500 respondents, averaging 20 years. The range, from minimum (e.g., 18 years) to maximum (e.g., 65 years), helps understand the demographic diversity within the sample.

Responsiveness and Satisfaction

The variable PTN_SAT reflects satisfaction levels towards healthcare services. The combined percentage of respondents indicating 'unsatisfied' or 'very unsatisfied' provides insights into service quality perceptions. If 15% report dissatisfaction, targeted improvements could be deduced.

Education Levels

Using variable EDUC, the percentage of respondents who did not graduate from high school is calculated. For example, if 30% did not reach high school, it underscores educational disparities that may influence health literacy and outcomes.

Collection of Patient Data: Quantitative vs Qualitative

Differences

Quantitative data are numerical and measurable, enabling statistical analysis (e.g., age, blood pressure). Qualitative data are categorical, describing qualities or attributes (e.g., gender, race). Quantitative data facilitate precise calculations, whereas qualitative data aid in understanding categories and groupings.

Examples in Healthcare Encounters

  1. Quantitative: Blood pressure readings, cholesterol levels, BMI.
  2. Qualitative: Patient gender, race/ethnicity, smoking status.

Survey Data Collection Techniques

  1. Structured interviews
  2. Self-administered questionnaires
  3. Online surveys

Analysis of Web Health Dataset

Assigning Missing Values

Following the specified steps, missing values for the inc variable are identified and coded accordingly, ensuring data integrity for subsequent analysis.

Analyzing Residency Status

The percentage of residents living in suburban areas, represented by the variable usr, is calculated. Suppose 150 out of 300 respondents are suburban residents; then, the percentage is (150/300) x 100 = 50%.

Percent vs Valid Percent

According to SPSS documentation (Pallant, 2020), "Percent" displays the proportion based on total cases including missing values, whereas "Valid Percent" excludes missing data, providing an accurate representation of valid responses. This distinction ensures correct interpretation of categorical data.

Internet Usage Frequency

From the variable q8a, the response categories corresponding to 21.3% and 5.4% reflect specific weekly frequencies, such as daily use or infrequent use, illustrating varying levels of internet dependence.

Cell Phone Use for Health Information

The percentage of respondents who have used their cell phones to access health information, derived from variable q15, informs about technological engagement in healthcare.

Age Analysis

The mean age and age range are derived by excluding missing responses, providing demographic context. For example, a mean age of 35 years and age range from 18 to 65 years depict a diverse adult population.

Educational Engagement

The percentage of individuals enrolled in educational programs (stud) helps understand the educational engagement of the sample, which could influence health literacy.

Conclusion

This analysis illustrates fundamental statistical techniques and data handling practices vital in healthcare research. Accurate statistical calculations allow researchers to draw meaningful insights, informing policy and practice. Proper assignment of missing values and understanding data types are crucial steps in ensuring robust analyses.

References

  • Pallant, J. (2020). SPSS Survival Manual. McGraw-Hill Education.
  • Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics. Pearson.
  • George, D., & Mallery, P. (2019). SPSS for Windows Step by Step. Routledge.
  • Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
  • Leech, N. L., Barrett, K. C., & Morgan, G. A. (2018). IBM SPSS for Intermediate Statistics. Routledge.
  • Hatcher, L. (2018). A Step-by-Step Approach to Using SAS for Univariate & Multivariate Statistics. SAS Institute.
  • Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling. Guilford Publications.
  • Floyd, F. J., & Widaman, K. F. (2020). Factor analysis and scale development. In G. R. Hancock & R. O. Mueller (Eds.), The Reviewer’s Guide to Quantitative Methods in the Social Sciences (pp. 110-136). Routledge.
  • American Psychological Association. (2020). Publication Manual of the American Psychological Association (7th ed.).
  • Moore, D. S. (2020). The Basic Practice of Statistics. W. H. Freeman & Company.

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