Statistics-MAEd-Module-1-FINAL PRESENTATION

STATTISTICS

1. STATISTICS WITH COMPUTER APPLICATION Prepared by: Neil Arden B. Sotto

2. Module 1 Basic Concepts Definition of Statistics Role of Statistics in Research Sources of Data Types of Data Methods of Collecting and Presenting Data Module 1 – overview SOTTO, N.A. (2024)

3. Definition of Statistics

4. Definition of Statistics The term STATISTICS is used in either singular or plural sense. In its singular sense, Statistics refers to the principles and methods of handling or processing data. These methods range from the most basic (mean, median, and mode) to those extremely complicated mathematical procedure (T-tests, Chi Square, ANOVA, etc.) Module 1 – definition of statistics SOTTO, N.A. (2024)

5. Module 1 – definition of statistics SOTTO, N.A. (2024)

6. Definition of Statistics The term STATISTICS is used in either singular or plural sense. In its plural sense, statistics refer to a body of numerical facts of any kind (i.e. athletic stats, vital stats, etc.) Module 1 – definition of statistics SOTTO, N.A. (2024)

7. Module 1 – definition of statistics SOTTO, N.A. (2024)

8. Definition of Statistics STATISTICS deals with collecting, presenting, analyzing, and interpreting numerical or quantitative data. Module 1 – definition of statistics SOTTO, N.A. (2024)

9. Role of Statistics in Research

10. Role of Statistics in Research It is used by researchers in many fields to collect, organize, summarize, analyze, interpret, and present data in a meaningful and convenient way. It will enable researchers to give exact descriptions of the collected data in his/her research It enables researchers to develop accurate and reasonable inferences from the relevant data he/she collected. Module 1 – role of statistics in research SOTTO, N.A. (2024)

11. Role of Statistics in Research Knowledge on statistics would enable the researchers and consumers of research to evaluate the credibility and usefulness of information derived from the data, for them to make appropriate decisions or actions based on the data collected Results acquired from research are meaningless raw data unless analyzed with the appropriate statistical tool. Therefore, determining statistics in research is of utmost necessity to justify research findings. Module 1 – role of statistics in research SOTTO, N.A. (2024)

12. Sources of Data

13. Sources of Data Primary Data – firsthand data collected from the source (informants, respondents, or records) by the researcher himself Secondary Data – data taken from published material or compiled by the researcher, organization, research institution, and other agencies. Sometimes referred to as “archival data”. Module 1 – sources of data SOTTO, N.A. (2024)

14. Types of Data

15. Main Types of Data Qualitative Data – is a categorical measurement expressed not in terms of numbers, but rather by verbal description. In statistics, it is often used interchangeably with “categorical” data. Quantitative Data – is a numerical measurement acquired through counting or measuring. Module 1 – types of data SOTTO, N.A. (2024)

16. Types of Quantitative / Numerical Data Discrete – can only take particular value e.g. number of students in a class, number of languages a person speaks, number of family members in a household, number of voters in a barangay; you can’t have a fraction of this data. (ex. 34, 10) Continuous – not restricted and can occupy any value over a continuous range such as weight, height, length, temperature (ex. 42.8 kg, 6.72 meters, 32.5°C) Module 1 – types of data SOTTO, N.A. (2024)

17. Types of Quantitative / Numerical Data (based on the Level of Measurement) Levels of Measurement, also called scales of measurement, tell us how precisely variables or data are recorded. There are 4 Levels of Measurement: Nominal – numerical assignment as identifiers only Ordinal – categorized and ranked Interval – difference is evident and no true zero point Ratio – has a true zero point Module 1 – types of data SOTTO, N.A. (2024)

18. Types of Quantitative / Numerical Data (based on the Level of Measurement) Nominal – categorical data in which numbers are simply used as identifiers, a number assigned for classification or identification purposes only and does not have quantitative meaning. Example: 1 – male 2 – female *sex, marital status, religious affiliation, race Module 1 – types of data SOTTO, N.A. (2024)

19. Types of Quantitative / Numerical Data (based on the Level of Measurement) Ordinal – ranked data used to classify and order classes. Example: Likert – type questions answerable by (5) – Very Highly Satisfied (4) – Highly Satisfied (3) – Moderately Satisfied (2) – Dissatisfied (1) – Very Dissatisfied Module 1 – types of data SOTTO, N.A. (2024)

20. Types of Quantitative / Numerical Data (based on the Level of Measurement) Interval – is a measurement where the difference between two values is meaningful. Interval scales are numeric scales in which we know not only the order, but also the exact differences between the values. In particular, interval data has no true zero point. Example: temperature (°C & °F), IQ, calendar years Module 1 – types of data SOTTO, N.A. (2024)

21. Types of Quantitative / Numerical Data (based on the Level of Measurement) Ratio – the highest level of measurement, has “true zero” and therefore provide absolute magnitude of attribute. True zero means that a value of zero signifies total absence of the variable of interest. Example: height, weight, length, area, speed, age, time Module 1 – types of data SOTTO, N.A. (2024)

22. Module 1 – types of data SOTTO, N.A. (2024)

23. Methods of Collecting Data

24. Methods of Collecting Data Interview – a person to person exchange of information. Uses interview schedule or guide questionnaire. Questionnaire – a set of prepared questions to be answered by the respondents. Experiment – conduct of an activity to obtain results Observation – recording of naturally occurring observable data. Module 1 – methods of collecting data SOTTO, N.A. (2024)

25. Methods of Presenting Data

26. Methods of Presenting Data Textual - narrative form Tabular - arranging or summarizing data in statistical tables Graphical – use of graphs such as pie, bar, line, pictograph and other graphical illustrations. Module 1 – methods of presenting data SOTTO, N.A. (2024)

27. Module 1 – methods of presenting data SOTTO, N.A. (2024)

28. REFERENCES: SPSS Tutorial (The Basics, Data, Descriptive Statistics, Chi-square and T-tests, Correlation and Regression, One-way ANOVA and Factorial ANOVA), www.Psych.utoronto.ca/courses/c1/spss/toc.htm Raynald’s SPSS Tools, SPSS Tutorials, www.SPSStools.net/spss.htm SOTTO, N.A. (2024) Module 1 – REFERENCES

29. REFERENCES: SPSS Tutorial 1-You Tube, Qassim Medical College www.youtube.com/watch?v=ZsjQht9TaOk Dr. Asma Ali, Hands-on Tutorial on IBM SPSS Software, www.youtube.com/playlist?list Amherst College, SPSS Tutorial, ANOVA with Repeated Measures, Basic SPSS Instructions on Chi-Square Goodness of Fit and Test of Independence , www.amherst.edu SOTTO, N.A. (2024) Module 1 – REFERENCES

30. REFERENCES: Laerd Statistics: SPSS Tutorial and Statistical Guides, for Statistics Courses, Dissertations/theses and Research Projects. https://statistics.laerd.com/ SPSS Tutorial – Harvard-MIT Data Center-Harvard University, www.hmdc.harvard.edu SPSS Online Training Workshop- Mathematics Department Calcnet.mth.cmich.edu/org/ spss /toc.htm SOTTO, N.A. (2024) Module 1 – REFERENCES