PSYC-523 Statistics and Research Methods Flashcards
ANOVA
analysis of variance; Statistical test used to determine whether significant differences exist between 2 or more conditions
statistical technique used to compare more than two random samples or groups at a time; determines whether there is a significant difference between groups but does not tell where that difference lies - must do further tests to determine; goes around need for doing multiple t-tests which would increase error significantly
Clinical vs. Statistical significance
Clinical significance: is the obtained result important or meaningful?; Evidence Based Treatment; Will it be meaninigful in the real world? Looks from a therapeutic standpoint, looks at bigger picture. Clinical significance looks at symptom levels, remissions, client functioning, and quality of life. A high clinical significance suggests the post treatment symptom scores are lower than the pre treatment symptom scores
Statistical significance: The degree to which a result is not reasonably attributable to chance
is the obtained result likely to be attributable to chance factors?; Empirically Supported Treatment; Looks from an experimental standpoint, data driven
usually based on p
Construct validity
measures qualities or constructs; degree to which a test measures what it claims to be measuring; assessed by evidence of it being convergent (correlated highly with tests measuring the same thing) or divergent (not correlated with tests measuring different constructs)
ex. if a researcher develops a new questionnaire to evaluate respondents’ levels of aggression, the construct validity of the instrument would be the extent to which it actually assesses aggression as opposed to assertiveness, social dominance, and so forth.
Content validity
The degree to which the test measures content appropriate to the subject
the extent to which the items on a test are fairly representative of the entire domain the test seeks to measure.
For example, if a test is designed to survey math skills at a third-grade level, content validity indicates how well it represents the range of arithmetic operations possible at that level.
Correlation vs. Causation
Correlation refers to the relationship between two variables (not causation)
Correlation: refers to whether 2 variables are related or the extent to which change in 1 variable corresponds with change in the 2nd variable. this also can include whether association is greater than expected by chance strength of association.
These changes may be due to a 3rd variable or external influences on the other 2 variables. uses data/variables that currently exist - NOT manipulated;
used to determine:
-whether 2 variables co-vary-association - weak, moderate, strong
Causation: relationship between cause and effect; causality usually determined via controlled study - when you can isolate variables you want to examine and control for extraneous variables.
Correlation does not imply causation
Correlational research
Correlational research refers to a non-experimental research method which studies the relationship between two variables with the help of statistical analysis. Does not in any way establish causal factors
The goal is to describe the strength of the relationship between two or more events or characteristics. the more strongly the two events are correlated the more effectively we can predict one event from the other. Yields a correlation coefficient to show the relationship statistically; from -1 to +1. negative indicates inverse relationship. The closer the coefficient is to 1, the stronger the relationship is.
Ex: intelligence and height - people with higher IQs are taller; diet and weight
Cross-sectional design
Research design that examines a group of similar people who differ in one key characteristic
a research design in which individuals, typically of different ages or developmental levels, are compared at a single point in time. An example is a study that involves a direct comparison of 5-year-olds with 8-year-olds.
Study several groups of differing ages (or other variable of interest), but similar characteristics (SES, gender, ethnicity, education) at one point in time; very common; Groups can be compared across a variety of dependent variables.
Advantages are the researcher does not have to wait for the individuals to grow up or become older, large amounts of data in a short amount of time, and are inexpensive.
Drawbacks include not giving information about how individuals change or about the stability of their characteristics. cannot disentangle cohort and developmental changes. less complete picture of individual development. used to gather information only - cannot infer causation (because it is just a snapshot); quasi-experimental design (participants are not selected randomly - selected based on age)
Ex: look at developmental differences between 5, 10, and 15-year-olds
Dependent t-test
The dependent t-test (also called the paired t-test or paired-samples t-test)
compares the means of two related groups to determine whether there is a statistically significant difference between these means.
statistical analysis used when we want to know whether there is a difference between populations when the data are “linked” or “dependent”; also called the paired t-test or paired-samples t-test;
compares the means of two related groups/samples to detect whether there are any statistically significant differences between these means
Descriptive vs. Inferential
Descriptive statistics describe what is going on in a population or data set. Inferential statistics, by contrast, allow scientists to take findings from a sample group and generalize them to a larger population.
Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.
Descriptive: statistics that describe; do not explain why; can describe relationships, but not causality
Ex: Are women waiting until later to get married? Has the average ago of marriage for women increased?
Mean, median, mode, range, variance…
Inferential:
examine the relationships between variables within a sample and then make generalizations or predictions about how those variables will relate to a larger population.
When conducting research using inferential statistics, scientists conduct a test of significance to determine whether they can generalize their results to a larger population. Common tests of significance include the chi-square and t-test. These tell scientists the probability that the results of their analysis of the sample are representative of the population as a whole.
include linear regression analyses, logistic regression analyses, ANOVA, correlation analyses
Double-blind study
A type of clinical trial in which neither the participants nor the researcher knows which treatment or intervention participants are receiving until the clinical trial is over.
Guards against experimenter bias and placebo effects
Ecological validity
The extent to which a study is realistic or representative of real life
a measure of how test performance predicts behaviours in real-world settings.; generalized to real-life settings/situations; more control in experiment, typically less ecological validity - conditions are different than those found in real-life setting
Effect size
The magnitude of a relationship between variables
Effect size is a quantitative measure of the magnitude of the experimental effect. The larger the effect size the stronger the relationship between two variables.
For example, we might want to know the effect of a therapy on treating depression. The effect size value will show us if the therapy as had a small, medium or large effect on depression.
Experimental research
Research designed to discover causal relationships between various factors (manipulation)
Experimental research is a study that strictly adheres to a scientific research design. It includes a hypothesis, a variable that can be manipulated by the researcher, and variables that can be measured, calculated and compared. Most importantly, experimental research is completed in a controlled environment. The researcher collects data and results will either support or reject the hypothesis.
Includes independent and dependent variables, pretesting and post testing, and experimental and control groups.
Hypothesis
A testable prediction
A precise, testable statement of what the researchers predict will be the outcome of the study. In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review.
Independent t-test
Statistical procedure which determines differences between the means of independent variables
statistical technique that involves selecting two random samples; compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different
an inferential statistical test that determines whether there is a statistically significant difference between the means in two unrelated groups.
Internal consistency
A measure of reliability; the degree to which a test yields similar scores across its different parts, such as on odd versus even items
The higher the internal consistency, the more confident you can be that your survey is reliable.
measures whether several items that propose to measure the same general construct produce similar scores; usually measured with Cronbach’s alpha
Internal validity
The degree to which the effects observed in an experiment are due to the relationship between variables (not outside factors)
Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors. In other words, can you reasonably draw a causal link between your treatment and the response in an experiment?
whether an experimental treatment was the only cause of changes in a dependent variable;
control for confounding variables can increase internal validity - random selection of participants
Interrater reliability
The extent to which different raters agree on observations
used to assess the degree to which different raters/observers give consistent estimates of the same phenomenon;
the extent to which two or more raters (or observers, coders, examiners) agree.
Measures of central tendency
provides statistical description of the center of the distribution; three measures of central tendency are mean, median, and mode. Each of these measures describes a different indication of the typical or central value in the distribution.
Measures of variability
The ways of measuring score distribution (range, variance, standard deviation)
provide descriptive information about the dispersion of scores within data. Measures of variability provide summary statistics to understand the variety of scores in relation to the midpoint of the data.
spread of the distribution - standard deviation, range, and variance; gives information on data set to perform statistical analysis
Nominal/Ordinal/Interval/Ratio measurements
Nominal: scale in which labels are assigned for identification but cannot be counted or categorical data where there may be more than two categories
Ex: male/female; Republican, Democrat, Independent
Ordinal: data (numbers) that indicate order only, but may not indicate what measurement was used to determine the order or the magnitude of the differences within the order
Ex. How satisfied are you with our services?
1- Very Unsatisfied 2- Unsatisfied 3- Neural 4- Satisfied 5- Very Satisfied
Interval: true score data where you know the score a person made and you can tell the actual distance between individuals based on their respective scores, but the measure used to generate the score has not true zero
Ex: most psychological measures, IQ, SAT, GRE
Ratio: same as interval but also with an absolute zero
Ex. height, weight
Normal curve
aka normal distribution; refers to “bell-shaped” curve formed on histogram when data has a normal distribution; symmetry; most data focused toward mean/average with less toward extremes; random sampling tends to follow normal curve
Ex: frequency distribution of peoples’ height; most people would be of average height with extremes occurring on either side
Probability
likelihood that something will happen; based on hard data (unlike chance); p is between 0 and 1, 0 indicates impossibility of the event and 1 indicates certainty
Ex: utilized with suicide assessments; scale; if person falls within lower limits or lower end of the scale, the probability of them committing suicide is lower
Parametric vs. nonparametric statistical analyses
Parametric relies on assumptions about the shape of the distribution
Nonparametric do not rely on this assumption
Parametric: statistics based on symmetrical distributions or distributions that come close to symmetry; focus on 1 variable or relationship; robust procedures with negligible amounts of error; random sampling.
based on assumptions about the distribution of population from which the sample was taken.
Nonparametric: data that do not form sufficient symmetry in the distribution; skewed data.
not based on assumptions
