Chapter 2: Methods & Staticstics in I/O psychology Flashcards
Science is
An approach that involves the understanding, prediction and control of some phenomenon of interst.
Psychology is
Understanding, prediction and control of human behavior
W&O-psychology in work-related context
Emperical evidence
A data-driven aproach to test theories and relationships
Characteristics of “Good”Science, are;
- Logical approach, based on theories from which testable hypotheses can be derived.
- Knowledge based on data.
- Open, transparent, publicly available.
- The process of falsifiability: the inheren possiblitiy that a hypothesis can be proved false.
- Research should be independent and objective concerning their research results.
Characteristics of “good” theories, are;
- Are seen as important by the scientific community.
- Peer-reviewed.
- Emperically tested.
- Are verifiable and replicable.
Theory is
A set of interrelated concepts, definitions, and propositions that are advanced to explain or predict phenomena.
What is the Emperical cycle?
From observation(s), you induce you specific to a general rule, which is a theory or model. Based on that, you can make a deduction going from general to specific, by making a prediction. Testing the preduction, will translate to results. Based on the evaluation, you will make a decision.
What are some decisions you make when desigining a research?
- Labatory or fied?
- Participants?
- Conditions and how to assign people to the different experimental conditions?
- What are the relevant variables?
- How to measure the variables?
What are the different types of research designs in WO-psychology?
- Experimental.
- Quasi-experimetal
- Non-experimental
Experimental
- The Golden standard; allows you to oversee the causal relationships.
- Random assignment of conditions (example: intervention yes/no).
Experiments are
- Are studies in which the researcher manipulates a variable in order to observe how it affects another variable that is being studied.
- Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particula factor is manipulated.
Quasi-experimental:
- Assignment to conditions but not random.
Non-experimental
- No assignment to conditions.
- No intervention.
field research (survey) is a non-experimental design.
What is the difference between random and non-random sampeling?
There are systemetic factors that you maybe oversee, within the non random assignment of participants. With random assignment you try to hold everything continieus.
What are different types of data collection?
- Qualitative research
- Quantitative research
- Triangulation
Qualitative research
- For example observation, interviews, case- and diary studies.
- Results are descriptive
Descriptive statistics refer to a set of methods used to summarize and describe the main features of a dataset, such as its central tendency, variability, and distrivution.
Quantitative research
- For example test, questionnaires, fysiological measurement.
- Results are numerical
A data type expressed in numbers, rather than natural language description.
Triangulation
Converging evidence; testing the same hypothesis by using multiple, different methods.
What is the different between independent and dependent variables in an experiment?
- The independent variable is the variable that is varied or manipulated by the researcher.
- The dependent varialbe is the response that is measured.
Generability
Capability of being generated.
External validity
The degree that the results of one study can be applied to different…
- GROUPS
- Situations/tasks
- time
- organizations
What are the different data-analysis?
- Descriptive statistics; central tendency, variability/spread, distirubtion
- Inferential statistics
Descriptive statistics
Statistics that summarize, organize, and describe a sample of data.
Inferential statistics
Statistics used to aid the researcher in testing hypotheses and making inferences from the sampel to population.
Measures of centrale tendency;
- Mean: Average (sum of all scores divided by the sample size).
- Mode: Most common or frequent score in the distribution.
- Median: Middle score, value separating the higher half from the lower half.
111112223334445; mean=2.47. mode=1, median= 2 (whent it was 12345678, the mean is 4.5)
Variability/Spread
The extent to which scores in a distirubiton vary; variance, standard deviation.
SD provides information on the proportin of observations above or below certain values.
Distribution
Skewness;
- Positive (right).
- Negative (left)
No skewness
Distirubiton has bell curve shape, and the mean, median& mode are identical.
Positive skewness
Mean, median, and mode are not identical. Most observation to the left of the mean.
Normal Distribution
Is the assumption for most statistical technipuqes. Characteristics are:
- Symmetrical.
- Mode=Median=Mean
- About 2/3 of all socres fall between -1SD and +1SD
Inferential statistics
Statistics used to test hypotheses and making inferences from the sample to a larger population.
- Differences between groups.
- Strength of association between (two or more) variables.
Statistical significance
- Indicates the probability of getting the observed data, assuming the null-hypothesis is true ~Daniel Lakens.
- Null-hypothesis states that there is no effect.
Statistical power
- The likelihood of finding a statistically significance difference.
- Power decreases with smaller samples
Correlation
Relationship between variables
Inidicates the magnitude and direction of an relationship between two variables, about a linear relationship
Can vary between -1 and +1
Correlation NOT Causality
Based on the correlations, you generally can’t conclude that there is a causal relationship between the variables.
What are two imporatnat criteria when evaluating measurement instruments?
- Reliability: consistency or stability of a measure.
- Validity: Does the instrument actualy measure what is the supposed to measure?
Types of reliability
- Test-retest reliability: measures the consistency of results when you repeat the same test on the same sample at a different point of time.
- Equivalent test reliability: The extent to which measurements on two or more forms of a test is consistent.
- Inter-rater: measurement of the extent to which data collectors (raters) assign the same score to the same variable.
- Internal consistency: A measure of how well a test addresses different constructs and delivers reliable scores.
Types of validity
- Content-related validity: Evaluates how well and instrument (like a test) covers all relevant parts of the construct it aims to measure.
- Criterion-related validity: Evaluates how accurately a test measures the outcome it was designed to measure. Predictive (future) and Concurrent (current).
- Construct-related validity; The extent to which you test or measure accurately assesses what it’s supposed to.
