IPR revision (summary)

• True zero represents the complete absence of a measure
• Standard deviation is not a measure of central tendency

Types of Validity

• Face validity assesses if a measure appears to measure what it intends to.
• Construct validity assesses if a measure appears to measure the theoretical construct it's meant to.
• Criterion validity measures how well a measure predicts outcomes against another measure.
• External validity refers to whether the findings can be generalized to other contexts.

Types of Case Studies

• Exploratory case studies explore new research areas or generate hypotheses.
• Descriptive case studies provide detailed accounts of specific cases.
• Explanatory case studies explain the reasons behind particular phenomena.
• Intrinsic case studies focus on specific cases, emphasizing their uniqueness or significance.
• Instrumental case studies provide insight into broader issues.
• Collective case studies involve multiple cases.
• Ethnography includes studies of people in their natural environments.

Types of Thematic Analysis

• Thematic analysis identifies patterns or ideas across multiple data sets.
• Content analysis involves analyzing various sources of data.
• Narrative analysis examines stories from individuals.
• Grounded analysis develops a theory grounded in data collection.
• Discourse analysis is used to study language and communication.
• Interpretative Phenomenological Analysis (IPA) studies lived experiences of individuals.

Process of Conducting a Thematic Analysis

• Familiarize yourself with data.
• Generate initial codes.
• Search for themes.
• Review themes.
• Define and name themes.
• Produce a report.
• Content analysis includes deductive (predefined) or inductive (emerging) categories

Statistical Tests

• Cohen's kappa is a test statistic that considers chance agreement.
• Variance compares participants' original scores to the mean, indicating the overall spread of data points.
  • Find the mean of the data set.
  • For each data point, subtract the mean and square it to obtain squared deviations.
  • Sum the squared deviations and divide by N (for population variance) or N-1 (for sample variance).
• Standard deviation is the square root of the variance, it represents average spread of data points.

Data Types

• Time series is when data are collected at specific time intervals.
• Hierarchical data is organized in a multi-level structure.
• Cross-sectional data is collected at a single point in time.

Graphs

• Histograms are used to visualize distribution and frequency of data.
• Box plots summarize, compare distributions, and identify outliers.
• Violin plots show distribution and variability in more detail.
• Bar charts are used to compare values or densities across categories.
• Bimodal distributions indicate a divide in the data set.

Descriptive Statistics

• Measures of central tendency include mode, median, and mean.
• Measures of dispersion include range, IQR, variance, and standard deviation.

Skewness and Kurtosis

• Kurtosis describes the shape of the distribution and measures the tailedness (peak).
• Positive skew longer tail on right Mean > Median
• Negative Skew longer tail on left Mean Median.

Types of Hypotheses

• Null hypothesis: states there will be no effect or relationship.
• Alternative hypothesis: states there will be an effect or relationship.
  • Alternative hypothesis can be either one-tailed (direction specific) or two-tailed (direction not specified).
• P value: the probability of obtaining the observed data.
• Z score: measures how far a single data point is from the mean, used to compare different data points.

Errors in Hypothesis Testing

• Type 1 error (false positive): rejecting the null hypothesis when it's true.
• Type 2 error (false negative): failing to reject the null hypothesis when it's false.
• Alpha threshold of 0.05 determines the threshold for rejecting the null hypothesis.

Chi-Squared Test

• Assumptions for a Chi-Squared Test (all tests are two-tailed, direction not specified):
  • Nominal or ordinal data.
  • Mutual exclusivity: categories/variables are mutually exclusive and independent.
  • Expected frequency should be more than 5 for each cell to ensure validity.
  • Independence of observations: every observation in the dataset is independent.
  • Random sampling: data comes from a random sample to ensure representativeness and avoid bias.

Goodness of fit test (one variable)

• It includes frequency tables.
• A larger chi-squared value indicates a greater difference between observed and expected frequencies whereas as a smaller chi-squared value indicates a smaller difference between observed and expected frequencies.
• Degrees of freedom (df) define the room of variation allowed.
• Test of association (two variables):
  • Calculate the expected frequencies for each cell.
• Alpha threshold- .05 determines the threshold for rejecting the null hypothesis.
• Power 80 translates to a 20% chance of making a type 2 error.