PPT-1_INTRODUCTION-TO-PSYCHOLOGICAL-STATISTICS.pptx

Introduction to Psychological Statistics

1. Introduction to psychological statistics

2. OBJECTIVES FOR TODAY’s DISCUSSION Define what is statistics? Understanding the Importance of Statistics in Psychology Discuss and Understand the general categories od statistics Define population, sample, parameter, and statistic Understand the different scale of measurement Understand types of probability sampling (random

3. What comes to mind when you hear the word…

4. HOW TO SURVIVE THE COURSE? Keep your attention on the concepts. 2. Be sure you know each concept before you go to the next 3. Study, especially, intensely in the first half of the of the course

5. INTELLIGENCE LEVEL OF UMTC College STUDENTS 92% of UMTC College Students are highly intellectual with an average IQ of 130 base on Standford Binet Test Results

6. WHY STUDY STATISTICS ? 1. Understanding statistics is crucial to being able to read psychology research articles 2. Understanding statistics is crucial to doing research yourself. 3. Understanding statistics develops your analytic and critical thinking. Reference: Aron , A., Coups E., & Aron , E. (2013). Statistics for psychology (6 th ed.). Pearson Education

7. PSYCHOLOGY AND STATISTICS What is psychology? Scientific study of human behaviour and mental processes Describe, Predict, Explain, and Control

8. GOALS OF PSYCHOLOGY (Describe) Academic Motivation in the midst of Pandemic Results shows that the academic motivation among BS Psychology students has an average of 1.2 in a scale of 1-5 with 1 as the least motivates and 5 as the most motivated.

9. GOALS OF PSYCHOLOGY (Predict) Academic Motivation What could be the factors that predicts academic motivation? Type of teacher Social Isolation Mode of Instruction HYPOTHESIS

10. The research now proves that social isolation is inversely correlated with academic motivation. He can now proceed to conducting an experiment explaining the causal relationship between the variables. GOALS OF PSYCHOLOGY (Explain) IV D V Social Isolation Academic Motivation

11. If the causal relationship between social isolation and academic procrastination are established, one can now create an intervention program to control one’s academic procrastination. GOALS OF PSYCHOLOGY (Control)

12. What is statistics?

13. A branch of mathematics that deals with the organization, summarization, analysis, and interpretation of group of numbers. A set of mathematical procedures for organizing, summarizing, and interpreting information. What is statistics?

14. What is statistics?

15. The set of all individuals of interest in a particular study POPULATION

16. Parameter A value, usually a numerical value, that describes a population Ex. Census

17. Sample A set of individuals selected from a population, usually intended to represent the population in a research study.

18. STATISTIC A value, usually a numerical value, that describes a sample.

19. Relationship Population (All individuals of interest) Sample (Selected from a population) Sample selected to become respondents

20. VARIABLE A characteristic or condition that changes or has different values for different individuals

21. VaLUES Possible number or category that a score can have Academic Procrastination Scale (1-5) Stress Level (0-20)

22. Data and DATA SET DATA Are measurements or observations Datum (singular)- a single measurement or observation and is commonly called a score or raw score DATA SET Collection of measurements or observations Excel files from different courses ng nagpapaanalyze

23. EXAMPLE

24. Branches of statistics

25. Try to Analyze this one

26. Sampling error The naturally occurring discrepancy, or error, that exists between a sample statistic and the corresponding population parameter.

27. SAMPLE SITUATION Experimental Design: Type of Study Materials and Exam Scores Sample A: Printed Materials Score: 25, 26, 28, 27, 21, 27. 30, 28, 24, 19, 23, 26, 29, 26, 22 Sample A: Computer Screen 20 , 23, 25, 22, 18, 22, 17, 28, 19, 24, 27, 23, 21, 22, 19

28. SAMPLE SITUATION Descriptive Statistics Sample A: Printed Materials Average Score: 26 Sample A: Computer Screen Average Score: 22

29. SAMPLE SITUATION Inferential Statistics Test of Significance Use of Independent Samples T-Test Inferential Statistics helps us to decide what is the accurate interpretation to give for a sample statistic

30. LEARNING CHECK! Ms. Rae wanted to conduct a study regarding the relationship of pressure and risk-taking tendencies among UM College students. A group of 467 students volunteered to to participate as respondents in the study. What do we call these 467 students? A statistic A variable A parameter A sample

31. LEARNING CHECK! Ms. Rae wanted to conduct a study regarding the relationship of pressure and risk-taking tendencies among UM College students. A group of 467 students volunteered to to participate as respondents in the study. What do we call these 467 students? A statistic A variable A parameter A sample

32. LEARNING CHECK! Ms. Sarah conducts a survey regarding the preference of the 13K College students of UM for mode of instruction. From the 13K, 65.2 percent prefer online class while 34.8 percent prefers face-to-face classes. These percentages represents ___________. A statistic A variable A parameter A sample

33. LEVELS OF MEASUREMENT

34. LEVELS OF MEASUREMENTS NOMINAL (WEAKEST) applies to data that consists of names, labels, or categories. There are no implied criteria by which the data can be ordered from smallest to largest

35. LEVELS OF MEASUREMENTS ORDINAL Applies to data that can be arranged in order. However, differences between data values either cannot be determined or meaningless.

36. LEVELS OF MEASUREMENTS INTERVAL Applies to data that can be arranged in order. In addition, differences between data are meaningful.

37. LEVELS OF MEASUREMENTS RATIO A pplies to data that can be arranged in order. In addition, both differences between data values and ratios of data values are meaningful. Data at ratio level have a true zero . Examples: Income, height, weight, annual sales, market share, product defect rates, time to repurchase, unemployment rate, and crime rate are examples of ratio data.

38. LEVELS OF MEASUREMENTS

39. LEVELS OF MEASUREMENT

40. LEARNING CHECK Identify my specific level of measurement are the following examples: Anxiety Levels (Scale 0-20) Likert Scale Hair Length (Either Long or Short) Temperature Educational Status (Literate vs. Illiterate)

41. RANDOM SAMPLES

42. SAMPLING Sampling Technique- Sampling is an essential part of any research project. The right sampling method can make or break the validity of your research, and it’s essential to choose the right method for your specific question .

43. PROBABILITY SAMPLING (RANDOM SAMPLES) Simple Random Sampling (Snowball sampling) - all members of the population have an equal chance of being selected and the selection is done randomly.

44. PROBABILITY SAMPLING (RANDOM SAMPLES) Stratified Random Sampling - With stratified random sampling, you would draw a sample from each of these groups (or strata) separately. This allows you to make sure that every subgroup is properly represented, which leads to more accurate results than simple random sampling.

45. Systematic Random Sampling - Each member of the population is assigned a number, and then selected at regular intervals to form a sample. (Systematic sampling is also known as interval sampling.) Or, to put it another way, every “nth” individual in the population is selected to be part of the sample.

46. Cluster sampling - Divide the entire population into pre-existing segments or clusters. The clusters. The clusters are often geographic. Make a random selection of clusters. Include every member of each selected cluster in the sample.

47. QUESTIONS AND CLARIFICATIONS

48. REFERENCES Brase - Brase , (2018 ). Understandable Statistics Concepts and Method s, 12 th edition, Cengage Learning, Boston, MA, USA. McCombes , S. (2019). Sampling Methods | Types, Techniques & Example. Retrieved from: https://www.scribbr.com/methodology/sampling-methods / Flores, J. L., Hernandez, M. A., Leyva , E. W., Cacciata , M., Tuazon , J., & Evangelista, L. (2018). Prevalence and correlates of depression, anxiety, and distress among filipinos from low-income communities in the philippines . The Philippine journal of nursing, 88(2), 8–13.