1. Research Methods in Psychology PSY 311
Week 2: Basics of quantitative RM
Dr Sabahat Çiğdem Bağcı

2. Session Overview Theory, hypothesis Variable types
Basic concepts in quantitative RM

3. Operational definitions
Explain what you mean in your hypothesis.
How will the variables be measured in “real life” terms.
How you operationalize the variables will tell us if the study is valid and reliable.

4. Continious vs Categorical variables
Categorical variables are also known as discrete or qualitative variables. The variable is measured by distinct categories. E.g., gender, nationality, marital status,...
Dichotomous variable: When the variable has two categories, e.g. Gender
Continuous variables are also known as quantitative variables. The variable can take many different values

5. Types of Data / Variables
Categorical Variables
Nominal (Categorical)
E.g. Marital status, hair color, Smoking (Yes/no), racial categories
Ordinal: You have categories, but also you can see the ‘order’ of variables.
E.g. Socio-economic status (low, medium, high), educational level (primary school, highschool, university)

6. Types of Data / Variables
Continious Variables
Interval: The interval between groups should be equal
Similar to ordinal but different;. E.g., educational level is ordinal; primary school-highschool is different to the space between highschool-university
Annual income ( TL, TL, TL), temperature (the difference between 60 and 50 degrees is a measurable 10 degrees, as is the difference between 80 and 70 degrees) are interval
Ratio: problem with interval; no ‘zero’, there is no thing like ‘no temperature’. Ratio variables have absolute zero values.
Ratio variables can be meaningfully added, subtracted, multiplied, divided (ratios). E.g. Weight: 20 kg is twice as 10 kg.

7. Variables Variables are things we measure that ‘vary’
Independent Variable
A factor that is manipulated by the researcher
The causal part of the relationship
Dependent Variable
A variable that the experimenter measures to assess the impact of a variation in an independent variable
The dependent outcome that is observed from the manipulation of the independent variable

8. Variables Effect of smoking on health status
IV: smoking
DV: health status
Effect of stress on job performance
Effect of work load on marital satisfaction
Caution in causality! (different types of tests have different assumptions about causality)
Not all IV’s cause DV’s in every research design

9. Main research designs used
Observational
Correlational
Cross-sectional
Longitudinal
Experimental

10. Cross-sectional design
All data collected at one time-point
e.g. An employee attitude survey
Advantages
Quick and easy
Good for exploratory work
Disadvantages
Limited to inference of correlation; unable to make causal inferences
Potential for measurement bias if subjective data

11. Longitudinal design
Data collected from same participants at multiple time points
Advantages
Able to examine the effect of a change in one variable on a change in another; so can (begin to) make causal inferences
Disadvantages
More difficult and resource intensive
Introduction of other biases / cannot rule out “3rd variable” role

12. Experimental design ‘Intervention’ studies (with control groups)
e.g. Evaluation of the effect of a staff training exercise on sales performance
Advantages
One of best designs to model causation
Disadvantages
Arificial, low ecological validity

13. Identification of population crucial
Populations
Identification of population crucial

14. Sample
Sample 1
Population
Sample 2
Sample 3

15. Sampling How can you get participants?
Random sampling: everyone in the population has an equal chance of being your participant (e.g., lottery)
Non-probability sampling
Snawball: ask your friend to give it to his/her friend
Convenience (opportunity): anyone you can find

16. Sampling Stratified random sampling
Identify stratas (bölge) and do random sampling in them separately
E.g., you want a nationally representative sample in turkey
Random sampling in regions of Marmara, Karadeniz, Akdeniz,

17. Ability to generalize findings
“Survey of women agreed that their skin felt noticeably younger..” (283; 81%)
Representative
Sample
Ability to generalize findings

18. Research Methods in Psychology PSY 311
Week 2: Basics of quantitative RM
Dr Sabahat Çiğdem Bağcı

19. Theory & hypothesis testing
Theories  hypotheses
Predictions about behavior
Research Question vs. Hypothesis
Need plan (research design) for conducting specific activities (research method) among a sample (sampling

20. Hypotheses Statements about the objective world around us
Men earn more than women
Women are more intelligent than men
As study hours increases, exam grades will get better...
Null hypothesis(H0) vs Alternative hypothesis(H1)
(H0): Men do NOT earn more than women (men and women earn equally)
(H1): Men earn more than women (men and women earn differentially)

21. Hypothesis testing The aim of psychological research
Not to prove what you predicted is correct
But to reject the null hypothesis
Science should be falsifiable (Kuhn, 1970)
So if you find a difference or an effect
You reject null hypothesis and retain alternative hypothesis

22. Hypothesis testing
(H0) = No difference
(H1) = There is a difference
You conduct a research to examine whether adolescents have higher self-esteem than younger children. What would be H0 and H1?

23. Statistical significance
Is there sufficient evidence to reject the null hypothesis?
Probability = Chance = Likelihood
You decide on your level of alpha = > it is usually .05
IF you want to be more conservative you can choose alpha as .01, or even .001.
p = expression of probability

24. Statistical significance
Psychological research – subject to probabilities
You cannot ‘prove’ something, you need to have some probabilities
a p-value of 0.05 or below indicates that a finding is significant (i.e. it is the threshold)

25. p value
p value < .05 – the likelihood that you found a relationship or difference (as you initially observed) by random chance is smaller than .05%. So you can be (at least) 95% sure that what you found is not because of random chance.

26. Possible errors in hypothesis testing
Type I error
When the null hypothesis is true (there is no difference) and you reject it (you say there is a difference), you make a type I error. The probability of making a type I error is α (alpha), which is the level of significance you set for your hypothesis test. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis.
Type I error: Gerçekte var olmayan bir bulgu için bulgu var demek

27. Possible errors in hypothesis testing
Type II error
When the null hypothesis is false (there is a difference) and you fail to reject it (you say there is no effect), you make a type II error. The probability of making a type II error is β, which depends on the power of the test.
Type II error: Gerçekte var olan bir bulguyu kaçırmak ve yok demek

28. Type I – Type II errors Type I error
You want to test the effectiveness of a drug on participants. If you assume that there was a significant effect of the drug (the alternative hyp is true, so there is a difference), while in reality there was no effect (in fact the null hyp is true). What type of error is this?
Type I error

29. Type I – Type II errors Type II error
You want to test if there is a performance difference between men and women. You assume that there is no difference between men and women, while in reality there is such a difference. What type of error is this?
Type II error

30. Inferential tests Investigation of more than one variable
Typically inferential tests examine the significance of differences and associations
... i.e. they produce p-values
Many inferential tests exist
Choice of test dependent upon whether you are examining associations or differences and on the type of data you have (i.e. Categorical / continuous)

31. SPSS Data entry Variable view – data view
Enter data – coding categorical variables