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

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

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.

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 1-------- 20.8---------35.49--------50-----------70

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)

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 (1000-5000 TL, 5000-10000TL, 10000-15000TL), 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.

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

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

Main research designs used Observational Correlational Cross-sectional Longitudinal Experimental

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

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

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

Identification of population crucial Populations Identification of population crucial

Sample Sample 1 Population Sample 2 Sample 3

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

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,

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

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

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

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)

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

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?

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

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)

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.

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

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

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

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

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)

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