Independent Samples T-Test Quiz

Introduction to Quantitative Research Methods and Data Analysis

• Data literacy in psychology is the ability to read, understand, argue with, and make decisions based on data.
• Data informs our understanding of behaviours, mental processes, and treatment effectiveness.
• Data literacy allows for critical assessment of research findings and their implications.
• Reading data involves understanding graphs, tables, and statistical results.
• Critical thinking involves assessing the quality of research and identifying biases or limitations.
• Decision-making uses data to guide choices in personal and professional life.
• Communicating insights involves presenting data-driven conclusions clearly to others.

Basic Concepts

• Research questions/hypotheses are statements or questions researchers want to know about a topic.
• Literature reviews critically examine existing research, theories, and concepts relevant to a research topic.
• Research design outlines how a researcher will conduct a study, including the selection of a research sample and design of data collection tools.
• Data collection involves gathering data from participants to answer the research question(s).
• Data analysis manages, analyses, and interprets the collected data.
• Writing up reports and disseminates research findings.

Quantitative Data and Populations/Samples

• Quantitative data relates to numbers and is used in evidence-based practice and policy-making.
• Population refers to all the units available for research.
• A sample is a subset of the population selected for the study.
• A subject is a basic unit from which data is collected.
• Samples should be representative of the population to allow for generalizable results.
• Ensuring equal likelihood of individuals being in a sample is referred to as “randomness”.

Sampling Techniques

• Non-probability sampling techniques are judgment, convenience, quota, and snowball sampling.
• Probability sampling includes random, stratified, cluster, and systematic sampling.
• External validity refers to the extent to which research findings can be generalized to other groups and settings.
• Population validity refers to the extent to which the sample is representative of the population.
• Ecological validity relates to the extent to which the experiment setting represents the real world.

Types of Variables

• Variables are characteristics that can take different values.
• Categorical variables (Nominal, Ordinal) are qualitative measures.
• Numerical variables (Continuous, Discrete) are quantitative measures.
• Nominal variables are labels without magnitude.
• Ordinal variables can be ordered but have unequal intervals.
• Interval variables are numerical, have meaningful differences but no true zero point.
• Ratio variables have equal intervals and a true zero point.

Levels of Measurement

• Variables can be measured at different levels (nominal, ordinal, interval, ratio.)
• Continuous variables can take any value within a range.
• Discrete variables take on distinct countable values.

Operational Definition

• Operational definitions provide specific meaning to concepts/variables/constructs in research.
• Defining variables explicitly in terms of observable activities to obtain quantified measures.

Variable Associations

• Questions about one variable at a time. (e.g. average age of participants).
• Questions about associations. (e.g. do attitudes on drug use vary by age?)
• Two variables are associated if knowing the value of one predicts the value of another
• Independent Variables are directly manipulated.
• Dependent Variables are measured.

Descriptive and Inferential Statistics

• Descriptive statistics summarise data (e.g., mean, median, mode, standard deviation).
• Inferential statistics make inferences about a population based on sample data. (e.g. t-tests, ANOVA)

Measures of Central Tendency

• Mode = most frequent value
• Median = middle value of the ordered data
• Mean = average value

Measures of Variability

• Range = difference between highest and lowest values
• Interquartile range = difference between the first and third quartiles.
• Variance = average squared deviation from the mean.
• Standard Deviation = square root of the variance

The Normal Distribution

• Symmetrical, bell-shaped curve.
• Describes the distribution of many variables in the population

Distribution Characteristics

• Skewness = asymmetry around the mean.
• Kurtosis = degree to which data cluster around the middle of the distribution.

Variability

• Variability measures the spread of scores around the mean.
• Measures of variability include range, interquartile range, standard deviation, and variance

Sampling Distributions and the Standard Error

• Population distribution = entire group of interest.
• Sampling distribution = distribution of possible sample means.
• standard error = the standard deviation of the sampling distribution of sample means.

Confidence Intervals

• A range of values likely to contain the true population mean with a specified probability.
• Defined using the standard error, critical value(z) from normal distribution and sample size
• Measures confidence that the sample mean is close to the true population mean

Hypothesis Testing and P-values

• Hypothesis = a claim about population parameters.
• Null hypothesis (Ho) = the assumed opposite of what research predicts (no difference)
• Alternative hypothesis (H1) = the prediction/claim of research (shows a difference)

Statistical Tests

• T-tests = compare means/test for differences between samples
  • one-sample t-test: compare sample mean to a standard population mean
  • paired t-test: measure of difference to compare in related groups (e.g. before & after)
  • independent t-test: compare means/test for difference in independent samples (e.g. men vs women)
• Analysis of Variance (ANOVA) = used to compare means in more than 2 groups

Parametric vs Non-Parametric Tests

• Parametric tests = make assumptions about data distribution (e.g. normal distribution).
• Non-parametric tests = do not make assumptions regarding data distributions (e.g. Wilcoxon, Mann-Whitney U).

Multiple Comparisons

• Multiple comparisons occur when a study contains several tests.
• Controlling error when many comparisons are done to correct for the likelihood of errors.