Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Descriptive & inferential statistics presentation 2
Similar to Descriptive & inferential statistics presentation 2 (20)
Recently uploaded
Recently uploaded (20)
Descriptive & inferential statistics presentation 2
- 1. Descriptive & Inferential Statistics Knowledge Management Angela Davidson, Tamara Schantz PSY/315 February 11, 2016 William Arden
- 2. Agenda • Describe the function of statistics in Knowledge Management through governance • Define Descriptive Statistics • Define Inferential Statistics • Review example of descriptive & inferential statistics in Knowledge Management
- 3. Function of Statistics in Knowledge Management • To measure and evaluate the effectiveness and efficiency of Knowledge Management strategy implementation. • To reveal the value from, or the return on investment of Knowledge Management strategy development and implementation.
- 4. Descriptive Statistics A statistical technique that produces a number or figure that summarizes or describes a set of data. The basic idea is that a descriptive statistic summarizes a set of data with one number or graph ("University Of Northern Iowa", 2015). • Measures of central tendency • Mean • Median • Mode • Measure distribution • Identify range • Identify Variables
- 5. Descriptive Statistics in Knowledge Management • Risk management • Measure, Evaluate, and Review • Evaluate the success and obstacles
- 6. Inferential Statistics A method that takes chance factors into account when samples are used to reach conclusions (or make inferences about) populations ("University Of Northern Iowa", 2015). • Population • Measure mean and variable • Observes data collected • Summarizes variability • Hypothesis • Predictions/Judgements about population
- 7. Inferential Statistics in Knowledge Management • Decisions • Predictions • Assumptions • Forecasts
- 8. Relationship Between Descriptive & Inferential Statistics
- 9. Relationship Between Descriptive & Inferential Statistics
- 10. Conclusion • Knowledge management uses descriptive statistics to evaluate a wide range of data such as interpersonal, social-technical, and technical modes developed from organizations. Evaluation considers both successes and obstacles to the implementation of strategy (Zynigier, S. 2011). Qualitative and quantitative measurements were used to establish the size, quality, quantity, and perceived satisfaction (Zynigier, S. 2011). • Knowledge management uses inferential statistics to continue to change and evolve, organizations, corporations, businesses, and agencies of authority make decisions or predictions for groups or department as a result of the data collected.
- 11. References Privitera, G. J. (2016). Essential statistics for the behavior sciences. Retrieved from https://phoenix.vitalsource.com/#/books/9781483353012/cfi/6/28!/4/2/4@0:0. University of Northern Iowa. (2015). Retrieved from http://www.uni.edu/~hitlan/techniques.htm Zyngier, S. (2011). Knowledge Management: Realizing Value through Governance. International Journal of Knowledge Management (IJKM), 1(7), 35-54. doi:10.4018/jkm.2011010103
Editor's Notes
- Statistics is the method to evaluate and measure the success of strategies used to achieve the goal. In the article Knowledge Management: Realizing Value through Governance the use of statistics provided a means to measure the effectiveness of systems in place regarding the way in which the company governs the program. Collecting and analyzing data from a population provides the company with a measurable result including the many variables. Statistics is able to show the value of Knowledge Management implemented in a company and which type of governance produces results or does not.
- The data collected from the population was categorized by the type and the size of the company. There was no evidence found that associated the size or type of company influenced the benefit. Table 1. represents the type of companies the data was collected from, qualitive. Table 2. classifies the results by the number of employees within the company, quantitive. This shows majority of the data collected is from larger companies, therefore it can be regarded as skewed towards large organizations.