Statistical Variables about experiments and science

Statistical Variables

1. Statistics... «Discipline that includes techniques and methods for data collection, classification, analysis and interpretation of results» «A mathematical technique used to make the collected data meaningful» «Methods and techniques used to answer the research question or test research hypotheses»

2. Why do we learn statistics? To interpret the data obtained through a research using the appropriate method and technique. In statistics, one concept depends on the others.

3. Basics Research: Activities aimed at understanding human behavior and increasing knowledge on this subject Research begins with the research question. Statistical techniques are used when searching for an answer to this question.

4. Basics Population: «all units within the scope of the study with the same characteristics» The feature under consideration determines the size of the universe. For the purpose of research, a province, Turkey, may be a village. The population can be limited or unlimited. For example: If a research is conducted on gifted children, the population in this study is all gifted children in the country where the study was conducted.

5. The population is a community of individuals who fall into the field of observation. Population

6. Basics Parameter: "The values of the properties associated with the population" The arithmetic mean of the population (μ) The variance of the population (σ2), While doing research, it is not possible to obtain data from the entire population due to issues such as cost and time constraints. Instead, data are collected from smaller groups (samples) representing the population.

7. Basics Sample: Smaller groups selected from a population with sampling methods and having the same characteristics as the population can be defined. In other words, it is a small set chosen from a certain population according to certain rules and accepted as the ability to represent the population from which it is selected. The expression of the characteristics of the sample with numerical values is called statistics. Sample mean (Avg) Sample standard deviation (SS)

8. Population (N) Population Sample 1 Sample 2 Sample 3

9. Statistical methods are divided into two: descriptive and extractive statistics. Descriptive statistics The statistical methods and techniques used for collecting, describing and presenting numerical data serve to determine the characteristics of the group studied Inferential statistics Statistical methods and techniques are used to make accurate predictions about the universe from the data obtained from the sample.

10. Descriptive statistics Frequency, Percent, Measures of Variability and Correlation etc . Inferential statistics T- test, analysis of variance, chi-square etc.

11. Operationalisation: defining your measurement Being precise about what you are trying to measure. Determining what method you will use to measure it. Defining the set of allowable values that the measurement can take. 12

12. Variable VARIABLE is a characteristic or attribute that can assume different values age, gender, level of social class religious affiliation, marital status, type of department, town that you live in, score on an intelligence test speed on a reaction time test, number of people absent from work on a given day, type of psychotherapy received by patients, type of mental disorder number of siblings level of stress

13. Example Does the amount of coffee consumed have an effect on the test score? Variables: amount of coffee, exam score

14. Data The subject (participant) values related to the variable are called. Persons or units in the population or sample are called subjects (participants).

15. Qualitative and quantitative variables Quantitative variable: If the property of a person or object is explained in terms of quantity, this variable is quantitative. Eg: academic achievement score, number of books in the library, IQ score Qualitative variable: If the property of a person or object is divided into classes, this variable is qualitative. Ex: language, skin color, academic title, gender

16. VARIABLES QUANTITATIVE VARIABLES (NUMERIC) Ex: age, number of students, height, weight DISCRETE VRB Discrete variables can only take on certain discrete values in a range (countable) # of children in a family CONTINUOUS VRB Continuous variables can take on absolutely any value within a given range. Height, weight, temperature QUALITATIVE VARIABLES (CATEGORICAL /NOMINAL) Categorical variables are those in which we simply allocate people to categories. Ex: gender, department, rel. affiliation, marital status don’t take average examine frequency Quantitative & Continuous Quantitative & Discrete Qualitative & Discrete

17. Variables Sürekli Değişken: iki değer arasında sonsuz sayıda değerler alabilen değişkenlerdir. Örn., Ağırlık ve uzunluk 1 20.8 35.49 50 70 Kesikli (Discrete) Değişken: Herhangi iki değer arasında, başka bir değer alamayan, sınırlı sayıda değer alabilen değişkenlerdir. Örn., sergilenebilen semptom sayısı Kategorik Değişken: Sınırlı sayıda değerler alabilen veya alt birimlere bölünemeyen değişkenlere denir. Örn., Cinsiyet (sadece kız ve erkek değerlerini alabilir), medeni durum, etnik köken İki değerli değişken (Dichotomous) : Değişken sadece iki değer alabilirse (biyolojik cinsiyet gibi)

18. Variables CONTINUOUS DISCRETE CATEGORICAL Temperature # of symptoms of a disease Gender Speed # of cars Occupation Intelligence Game score Fav. color Anxiety # of congress you attained The amount of violance in TV # of children

19. Variables How long has content related to violence been shown on TV? Number of TV violence incidents in a week continuous discrete

20. Levels of Measurement Nominal scale is a naming scale, where variables are simply “named” or labeled, with no specific order. Ordinal scale has all its variables in a specific order, beyond just naming them. Interval scale offers labels, order, as well as, a specific interval between each of its variable options. Ratio scale bears all the characteristics of an interval scale, in addition to that, it can also accommodate the value of “zero” on any of its variables. 23

21. Levels of Measurement 1. Nominal Scale (Categorical) For these kinds of variables it doesn't make any sense to say that one of them is "bigger" or "better" than any other one, and it absolutely doesn't make any sense to average them. 24

22. 2. Ordinal Scale An ordinal scale variable is one in which there is a natural, meaningful way to order the different possibilities, but you can't do anything else. 25

23. 3. Interval Scale Interval Scale is defined as a numerical scale where the order of the variables is known as well as the difference between these variables. Variables that have familiar, constant, and computable differences are classified using the Interval scale. 26 ** ‘Interval’ indicates ‘distance between two entities

24. 4. Ratio Scale Ratio Scale is defined as a variable measurement scale that not only produces the order of variables but also makes the difference between variables known along with information on the value of true zero 27

25. Measurement The variable manipulated by the experimenter is called the independent variable (IV): that is, its value is not dependent upon (is independent of) the other variables being investigated. ??? whether dog walking influences the number of social encounters. Dependent variable (DV): The variable that assumed to be dependent upon the value of the IV. The purpose of the experiment is to establish or dismiss such dependence . ??? whether dog walking influences the number of social encounters.

26. Hypothesis A research hypothesis is our prediction of how specific variables might be related to one another or how groups of participants might be different from each other.

27. The ‘role’ of Variables: Predictors and Outcomes "to be explained" variable Y "doing the explaining" as X 1 , X 2 , etc. The independent variable (IV) is the variable that you use to do the explaining (i.e., X ) and the dependent variable (DV) is the variable being explained (i.e., Y ). Y depends on X use X (the predictors) to make guesses about Y (the outcomes) 30

28. example: The effect of coffee consumption on exam scores İçilen kahve miktarı Experimental group 1: 1 cup of coffee Experimental group 2: 2 cup of coffee Control group: no coffee Hypothesis: As the students drink more coffee, their exam scores increase. IV DV performance Cannot be manipulated Only measured

29. IV & DV The effect of the sound in the environment on test anxiety The effect of exam anxiety on exam score The effect of self- compassion on life satisfaction