1 / 18

Understanding Statistics: Descriptive and Inferential Data Analysis

Learn the basics of descriptive and inferential statistics, explore variables, data reduction, and measures of association. Discover the levels of measurement and how they impact statistical analysis.

georgeswanson
Download Presentation

Understanding Statistics: Descriptive and Inferential Data Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Statistics:Descriptive and Inferential

  2. Data is numerical

  3. Statistics • A set of mathematical techniques used by social scientists to organize and manipulate data for the purpose of answering questions and testing theories

  4. Variable • Any trait that can change values from case to case

  5. Independent Variable Dependent Variable

  6. Descriptive Statistics • When the researcher needs to summarize or describe the distribution of a single variable • When the researcher wishes to understand the relationship between two or more variables

  7. Data Reduction • Process of allowing a few numbers to summarize many numbers • Is the basic goal of single-variable (or univariate) descriptive statistical procedures

  8. Measures of Association • Understand the relationship between two or more variables • Allow to quantify the strength and direction of a relationship

  9. Inferential Statistics • Wish to generalize findings from a sample to a population • Population – total collection of all cases that the researcher wishes to understand better • Involves using information from samples (carefully chosen subsets of the defined populations) to make inferences about populations

  10. Discrete Variable • It has a basic unit of measurement that cannot be subdivided • Eg. # of people per household

  11. Continuous Variable • If the measurement can be subdivided infinitely • Eg. time • Always approximating and rounding off the scores • Reported as discrete

  12. Level of Measurement • Nominal • Ordinal • Interval-ratio (I/R)

  13. Nominal • Classification into categories is the only measurement procedure permitted • Not numerical • Compared to each other only in terms of the number of cases classified in them

  14. Nominal • Categories not higher or lower along some numerical scale • Eg. gender, religious affiliation • Mutually exclusive and exhaustive • Categories are relatively homogeneous

  15. Ordinal • Classify into categories • Allow categories to be ranked • High to low, more or less than another • Eg. SES: upper class, middle class, working class, lower class

  16. Ordinal • Represents only a position • The distance between the scores cannot be described in precise terms • Averages cannot be used

  17. Interval-Ratio (I/R) • They are measured in units that have equal intevals • Eg. age, # of siblings • Have a true zero • Zero indicates the absence or complete lack of whatever is being measured • Eg. education, income

  18. Levels of measurement is the first guideline to use in selecting a statistic

More Related