Research l Reviews l Theories l Mental Health l Quiz

Introduction to Biostatistics

-statistics are simply a collection of tools that researchers employ to help answer research questions

INTRODUCTION

• Statistics plays a vitally important role in the research.

• Health information is very often explained in statistical terms

• Many decisions in the Health Sciences are created through statistical studies

• It enables you:

• to read and evaluate reports and other literature

• to take independent research investigations

• to describe the data in meaningful terms

DEFINITIONS

1. Statistics: is the study of how to collect, organizes, analyze, and interpret data.

2. Data:  the values recorded in an experiment or observation.

3. Population: refers to any collection of individual items or units that are the subject of investigation.

4. Sample: A small representative sample of a population is called sample.

5. Observation: each unit in the sample provides a record, as a measurement which is called observation.

6. Sampling: getting sample from a population

7. Variable:  the value of an item or individual is called variable

8. Raw Data: Data collected in original form.

9. Frequency: The number of times a certain value or class of values occurs.

10. Tabulation: can be defined as the logical and systematic arrangement of statistical data in rows and columns.

11. Frequency Distribution: The organization of raw data in table form with classes and frequencies.

12. Class Limits: Separate one class in a grouped frequency distribution from another. The limits could actually appear in the data and have gaps between the upper limit of one class and the lower limit of the next.

13. Class Boundaries: Separate one class in a grouped frequency distribution from another.

14. Cumulative Frequency:  The number of values less than the upper class boundary for the current class. This is a running total of the frequencies.

15. Histogram: A graph which displays the data by using vertical bars of various heights to represent frequencies.

16. Frequency Polygon: it is a line graph. The frequency is placed along the vertical axis and the class midpoints are placed along the horizontal axis. These points are connected with lines.

17. Pie Chart: Graphical depiction of data as slices of a pie. The frequency determines the size of the slice. The number of degrees in any slice is the relative frequency times 360 degrees.

18. Central tendency - a typical or representative value for a dataset.

VARIABLES

• The value of an item or individual is called variable.
• Variables are of two types:
• Quantitative: a variable with a numeric value. E.g. age, weight.
• Qualitative: a variable with a category or group value. E.g. Gender (M/F), Religion (H/M/C), Qualification (degree/PG)
• Quantitative variable are two types:
• Discrete /categorical variables
• Continuous variables
• Variables can be
•  Independent
• Are not influenced by other variables.
• Are not influenced by the event, but could influence the event.
• Dependent
•  The variable which is influenced by the others is often referred as dependent variable.

E.g. In an experimental study on relaxation intervention for reducing HTN, blood pressure is the dependent variable and relaxation training, age and gender are independent variable.

SAMPLING

• Sampling is the process of getting a representative fraction of a population.

• Analysis of the sample gives an idea of the population.

• Methods of sampling:

• Random Sampling or Probability sampling
• Simple random sampling
• Stratified random Sampling
• Cluster sampling
• Non-random sampling
• Convenient Sampling
• Purposive Sampling
• Quota Sampling
• In Simple Random sampling, each individual of the population has an equal chance of being included in the sample. Two methods are used in simple random sampling:

• Random Numbers method

• Lottery method

• In stratified random sampling, the population is divide in to groups or strata on the basis of certain characteristics.

• In cluster sampling, the whole population is divided in to a number of relatively small cluster groups. Then some of the clusters are randomly selected.

• Convenience sampling is a type of non-probability sampling which involves the sample being drawn from that part of the population which is selected because it is readily available and convenient.

• Purposive sampling is a type of non-probability sampling in which researcher selects participants based on fulfillment of some criteria. E.g. schizophrenia treatment naive.

SCALES OF MEASUREMENT

• Four measurement scales are used: nominal, ordinal, interval and ratio.

• Each level has its own rules and restrictions.

Nominal Scale of measurement

• Nominal variables include categories of people, events, and other phenomena are named.

• Example: gender, age-class, religion, type of disease, blood groups A, B, AB, and O.

• They are exhaustive in nature, and are mutually exclusive.

• These categories are discrete and non-continuous.

• Statistical operations permissible are: counting of frequency, Percentage, Proportion, mode, and coefficient of contingency.

Ordinal Scale of measurement

• It is second in terms of its refinement as a means of classifying information.

•  It incorporates the functions of nominal scale.

• The ordinal scale is used to arrange (or rank) individuals into a sequence ranging from the highest to lowest.

• Ordinal implies rank-ordered from highest to lowest.

• Grade A+, A, B+, B, C+, C

• 1st , 2nd , 3rd etc

Interval scale of Measurement

• Interval scale refers to the third level of measurement in relation to complexity of statistical techniques used to analyze data.

• It is quantitative in nature

• The individual units are equidistant from one point to the other.

• The interval data does not have an absolute zero.

• E.g. temperature is measured in Celsius or Fahrenheit.

Ratio Scale of Measurement

• Equal distances between the increments

• This scale has an absolute zero.

• Ratio variables exhibit the characteristics of ordinal and interval measurement

• E.g. variable like time, length and weight are ratio scales and also be measured using nominal or ordinal scale.

[The mathematical properties of interval and ratio scales are very similar, so the statistical procedures are common for both the scales.]

 Introduction Definitions Sampling Scales of Measurement Variables Presenting Data Descriptive Statistics Measures of central tendancy Measures of dispersion/variability Normal Distribution and Probability Inferential statistics: Chisquire Test Inferential statistics: t-tests Inferential statistics: correlation tests Inferential statistics:ANOVA and other tests Inferential statistics: Multivariate analysis Quiz and Questions