PSY 201: Research Methods in Psychology

Course Introduction

• Course name: PSY 201, Introduction to Statistics for Psychology I
• Instructor: Dr. Nihan Albayrak-Aydemir

Research Methods in Psychology

• Focuses on research methodologies within psychology.

Understanding Data in Psychological Research

• A lecture on variables, measurement, and data organization in psychological research.
• Teaching Assistant: Saliha Erman, MA
• Adapted from Assoc. Prof. Güneş Ünal, Boğaziçi University, 2022

Variable

• A variable is any attribute of objects, people, or events that can take on different values within a particular investigation.
• Examples: height, reaction time, test scores, eye color.
• A constant is the opposite of a variable, having a fixed value within a particular investigation.
• An attribute can be a variable in one context and a constant in another.

Within-Subject vs. Between-Subject Variance

• Changes and variations can be seen within and between individuals.
• Example: Mood

Types of Variables

• Qualitative (categorical) variables take a value that is one of several possible categories.
• The values of qualitative variables are categories (though they sometimes can be represented by numbers, but these numbers serve as labels only).
• They express differences in kind, not amount.
• Examples: College attended, fruit types, nationality, blood type.
• Quantitative (continuous) variables are numerical variables such as height, decision time, GPA, age, or proportion of items correctly answered.
• The values of quantitative variables are numbers.
• They express differences in amount.

Levels of Measurement

• Nominal scale- a measurement scale used to categorize or label variables.
• It does not have any quantitative value or order.
• The categories on a nominal scale represent different groups or types.
• There is no inherent ranking or hierarchy among them.
• Examples: Left-handed, right-handed. Favorite ice cream flavour: vanilla, chocolate, strawberry, etc.
• Ordinal scale- has the properties of a nominal scale, but the observations can be ranked in order of magnitude.
• Example: Position finished in a race.
• Interval scale- has all the properties of an ordinal scale, and a given distance between measures has the same meaning anywhere on the scale.
• Also called an equal-interval scale.
• Degrees of temperature, calendars years.
• Ratio scale- has all the properties of an interval scale plus an absolute zero point.
• This allows for the comparison of ratios.
• Examples: length, weight, reaction time, dollars.

Caution on Ratio Scale

• On the interval level of measurement, you cannot make conclusions on ratios.
• Example: 20°C is not twice as hot than 10°C.
• Likert type scaling is considered an ordinal scale because the intervals between points are not necessarily equal.

Independent and Dependent Variables

• Independent variable (IV): The variable that is manipulated or changed by the experimenter.
• Dependent variable (DV): The variable that is measured under each condition or level of the independent variable.
• Also called explanatory variable or predictor variable.
• Expected to explain the changes in the dependent variable.
• Also called response variable or outcome variable.

Research Design: Natural and Manipulated Independent Variables

• Natural IVs: The experimenter does not have complete control.
• Manipulated IVs: The experimenter has complete control.
• Quasi Experiments: Observing a natural variable and comparing it to another variable.
• True Experiments: Observing a manipulated variable and comparing it with another variable.

Correlations Between Variables

• In some studies, there is no clear cause-and-effect relationship between the variables.
• i.e. the relationship between measures of depression and anxiety.
• In correlational designs, neither variable can be considered a predictor or outcome variable.

Data Organization

• Data entry: Entering raw data into a software tool.
• Data coding: Assigning numerical or categorical codes to responses.
• Cleaning data: Removing or correcting errors.
• Tools: Excel, SPSS, Jamovi

Data Organization (Properly organized data ensures...)

• Efficiency: Efficiency in the analysis process, saving time and effort.
• Accuracy: Reduces the risk of errors that could distort results
• Reproducibility: Well-organized data can be more easily verified or reanalyzed by other researchers

Descriptive Statistics and Inferential Statistics

• Procedures used to summarise, organize and make sense of a set of scores or observations.
• Procedures used to allow researchers to infer from or generalise observations made within smaller samples to the larger population.

Measures of Central Tendency

• Mean: Average of a set of scores
• Median: The middle score in a sorted set of scores
• Mode: Most frequent score

Measures of Variability

• Range: Highest - Lowest Score
• Interquartile range (IQR): Distance between the values of the third and the first quartiles.
• Mean absolute deviation (MAD): average of the absolute deviations from the mean.
• Sum of Squares (SS): sum of squared deviation scores.
• Variance (s²): Sum of Squares (SS) divided by the number of cases (N)
• Standard Deviation (s): the positive square root of the variance.

Sample vs. Population Notation

• Sample Variance (s²): Σ(Y - Y)² / N
• Population Variance (o²): Σ(Y - μ)² / N (μ — the population mean)

Boxplots

• A simple graphical representation of data that captures features of both the location and spread of scores in a distribution

Z-Scores

• Transformed scores to a common scale.
• Have a mean of zero (0) and a standard deviation of one (1).
• Allow the comparison of different distributions.

Confidence Interval (CI)

• A range of values that's used to estimate an unknown population parameter.
• It gives a certain degree of confidence that the true population parameter lies within the interval.

Hypothesis Testing

• A process to test an idea (or a theory) by using data collected from a sample of the population.
• Begin with a Null and Alternative Hypothesis.
• Set an alpha level.
• Perform an appropriate statistical test.
• Calculate the p-value from the test statistic.
• Compare the p-value to the alpha level to decide whether to reject the null hypothesis.

One Sample z-test

• A test used to compare the mean of a sample with the mean of a population, when the population's standard deviation (σ) is known.

One Sample t-test

• A test used to compare the mean of a sample with the mean of a population, when the population's standard deviation (σ) is not known or suspected to be different from the sample's standard deviation (s).

Independent Samples t-Test

• A test used to determine if there is a statistically significant difference between the means of two independent groups or samples.
• The groups are sampled from mutually exclusive subgroups (i.e. participants belong only to one group).
• The dependent variable is always quantitative or measured on an interval or ratio level.