Module 01 - Home Page

Types of Research, Levels of Measurements

Analysis of Descriptive Statistics

Chi-Square test and interpretation

Hypotheses Writing  

Key Terms to Understand

• Parametric and Nonparametric variables

• Types of Variables  -- Nominal, Dichotomous, Categorical, Continuous or Scaled, Factor, Dependent Variable, Independent Variable, Confounding, Extraneous

• Independence

• Statistical Significance

Module Objectives

By the end of this module, the student shall be able to satisfy the following outcomes expectations:

1.  Explain and discuss the value of descriptive studies in medical and health science research. (Case Assignment)

2.  Discuss the characteristics of descriptive research study designs and assess its application to research in the health sciences. (Case Assignment)

3.  Distinguish and contrast the different levels of measurement in health science research studies and the understand how it is related to the interpretation of study results. (Case Assignment, TD and SLP)

4.  Interpret, present and construct proper conclusions for Descriptive analyses and Chi-Square Tests utilizing SPSS. (Case Assignment)

Activities

The activities listed here include get acquainted and "housekeeping assignments" that will only occur in our first week as we get our class operational. Your timely responses are important to the well-being of all. Thank you.

A. Prepare a self-introduction for your classmates. Post your self-introduction to the Threaded Discussion in CourseNet.  

B. The  synchronous (Horizon Live) conferences for this course are usually held once during the second week of each session module. Conference participation is included among the grading components. I know students are located throughout the United States and some are international. Please send me an email stating the evenings you are unavailable to attend the Horizon Live conferences. Should you expect difficulties attending the synchronous (Horizon Live) conferences, please contact me as soon as possible. I will consider each student's situation and, hopefully, provide a reasonable accommodation for everyone.  There may be two Horizon Live conferences for each module. You are only required to attend one of the conferences. If you are unable to attend any of the conferences then you can review the synchronous (Horizon Live) conferences through the Horizon Live archives. 

In addition, I will host at least two HL Conferences on using SPSS. In these sessions I will walk the class through data entry, and the steps required to do some of the required analysis for this course.

SPSS  --  Starting to Use it for this course --  Click here for exercise on SPSS.

Carefully review the following information and gain a thorough understanding of it!

(Note:  Many of the readings in this course are intended to help you develop researcher's perspective and help to prepare you for the oral qualifying examination. We want you to develop a "prepared mind"!   For instance, it is very important that you understand "the purpose and the value of a well developed bivariate analysis." (James H. Watt, and Sjef van den Berg, 2002)

What Researchers Seek (In general):

1. TO DESCRIBE

e.g.  The objective of this analysis is to describe the distribution of statistics examination scores for student in DHS608.

There is one variable (examination scores), hence this is a univariate analysis.

2. TO COMPARE

e.g. The objective of this analysis is to determine if there is a significant difference between males and females  with respect to statistics examination scores for students in DHS608.

There are two variables (gender and examination scores), and these are linked, therefore this is a bivariate analysis.

3. TO EXPLORE RELATIONSHIP

e.g. The objective of this analysis is to determine if there is a significant relationship between mid-term and final statistics examinations scores for students in DHS608. Usually, the follow-up objective is to determine the extent to which final exam scores can be predicted form mid-term exam scores.

There are two variables (mid-term exam scores & final exam scores) and these are linked, hence a bivariate analysis.  

For this assignment, the objectives should conform to #1  with appropriate measures (see below).

WHICH MEASURE(S) SHOULD I USE FOR DESCRIBING THE DATA?

Descriptive statistics should be the first step of statistical analysis, in order to reduce the “raw data” to a small number of “representative” figures. Datasets are summarized in order to describe two important features of the data distributions: the spread of scores and where within that range most of the data fall, e.g. the average of the scores. Measures of central tendency are used to assess the 'middle' or 'average' value in a distribution and measures of dispersion in order to estimate the amount of variability contained within the data and thus the degree to which the average is a typical value.

• With nominal variables (unordered categories: e.g. ethnic groups), use the MODE (with frequencies and percentages for each alternative).
• With ordinal variables (ordered categories: e.g. height reported as tall, medium, short), the median and mode are appropriate.
• For true numerical variables, all the common measures of central tendency (mean, median, mode) and dispersion (standard deviation, variance and range) are appropriate. If the distribution of the variables is skewed, emphasize the median in your report. NOTE: It is very useful to include the confidence interval for the mean (usually 95% confidence level). This indicates the range of values within which you are 95% confident that the true population mean will fall. Remember that there will always be “standard error” in statistics, therefore, it is not exact to describe the entire population with only one value (hence a range or values or confidence interval). This is the basic concept of INFERENTIAL STATISTICS, which will be further explored, in subsequent modules.
• Always check your data for the presence of outliers: these are extreme values (a valid but atypical value, or resulting from coding or data entry errors) which could lead to unreliable results if not addressed.

NOTE

When describing the data, you must interpret the numerical outcomes (measures of central tendency, dispersion and skewness) in conjunction with appropriate graphs. The numerical measures together with the graphs will allow you to confirm/discern the following features/characteristics:

1.      Normal

2.      Positively skewed

3.      Negatively skewed

4.      Bi-modal

5.      Multi-modal

Hypotheses Writing

For this course you are required to write a research question AND a hypothesis for each statistical test performed for this course.  There is a very sound reason for this requirement.  It will clarify your statistical analysis and help you to better understand your data set. Please review the following reference on hypotheses testing:  Chapter 12 - Testing Hypotheses found at http://www.llc.rpi.edu/web/ResearchMethodsForCommunicationScience/ch12.pdf.  Although, this Chapter is about hypothesis testing, it is directly related to statistical tests since statistical tests selected are selected based on the suitability of the test to answer the hypothesis.  In turn, the hypotheses should answer the research question. Hypotheses is plural since there are times when several hypotheses are required to answer the research question.  You will soon understand the value of learning to do this as it brings much more meaning to your data analysis.

For more information on Hypotheses see Background Page, Module Two.

Please continue this module by reviewing and studying the materials presented in the Module Background page .