Introduction

• Data- Raw material of statistics. Values that the variables can assume
• Data has two kinds of numbers, measuring and counting.
• Data set- Collection of data values
• Data Value or Datum- Each value in the data set
• Sources of data- Suitable data to serve as the raw material for our investigations
• Sources of data can come from:

  •Routinely kept records

  •Personal interviews

  •Surveys, questionnaires

  •Experiments

  •External sources
• Statistics -Science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data (drawing inferences from a sample of population)
• Biostatistics -When data analyzed are derived from the biological sciences, medicine, and public health
• Purpose of statistics: To investigate and evaluate the nature and meaning of information contained in numbers (data)
• Descriptive Statistics- Consists of the collection, organization, summarization, and presentation of data
• Inferential Statistics -Consists of generalizing from samples to populations, performing estimations, probabilities, hypothesis testing, determining relationships among variables, and making predictions
• Population (collection of entities) -Consists of all subjects (human, animals, machines, places) being studied for which we have an interest at a particular time
• Population of values:

  •Finite –can be measured/counted

  • Infinite –cannot be measured/counted
• Sample -Group of subjects or entities selected from a population
• Variable -A characteristic or attribute that assumes different values
• Kinds of variables:

  • Qualitative
  • Quantitative
  • Random
• Random -occurs by chance; cannot be predicted
• Discrete variables (finite) -Assume values that can be counted (0,1,2,3)
• Continuous variables (infinite) -Can assume an infinite number of values in an interval between any two specific values. Often include fractions and decimals
• Boundaries- given in 1 additional decimal place and always end with the digit 5
• Nominal Level -Names; No rank or order can be placed on the data
• Ordinal Level -Can be placed into categories; can be ordered or ranked. Precise measurements between ranks do not exist
• Interval Level -Precise distance or differences DO exist between units. One property lacking: no true zero value
• Ratio Level -Relationship between two numbers. Have differences between units. Exists a true zero value or true ratio between values
• Systematic samples- Numbering each subject of the population then selecting every kth subject
• Random samples- Selected by using chance methods or random numbers. Computer-generated random numbers, Table of random numbers
• Stratified samples- Obtained by dividing the population into groups (strata) according to some characteristic then sampling from each group randomly
• Cluster Samples- Population is divided into groups called clusters by some means. Researcher randomly selects some of these clusters and uses all members of the selected clusters as subjects
• Convenience sampling –subjects that are convenient
• Sequential sampling –studies one group after another
• Double sampling –employs initial then follow up samples
• Multi-stage sampling –taking samples in stages; using smaller and smaller units at each stage
• Observational studies -Researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these observations
• Experimental studies -Researcher manipulates one of the variables and tries to determine how the manipulation influences other variables
• Quasi-experimental study –researcher manipulates variables without the random assignment of participants
• Independent variables -Also known as explanatory variable. Variable that is manipulated by the researcher
• Dependent variables -Resultant variable or the outcome variable. Variable being studied to see if it has changed significantly due to the manipulation of the IV
• Experimental study groups:

  •Treatment group

  •Control group
• Hawthorne effect -Behavioral change due to awareness of being observed
• Confounding variable -Unforeseen variable affecting the results of the study