Help
Options
Didn't know it?
click below
click below
Knew it?
click below
click below
Don't Know
Remaining cards (0)
Know
retry
shuffle
restart
0:00
Embed Code - If you would like this activity on your web page, copy the script below and paste it into your web page.
Normal Size Small Size show me how
Normal Size Small Size show me how
PSYC 204- ASSIGN2.1
In class assignment 2.1
| Term | Definition |
|---|---|
| Nominal Measurement/Scales | -Data is categorized --Numbers represent categories ---No overlapping values |
| Ordinal Measurement/Scales | -Rank order objects or individuals -Data is categorized, ranked (relative to other categories) --Cannot be used to derive statistics like arithmetic mean --Likert Scales |
| Interval Measurement/Scales | -Rank order, plus has equal intervals/distances between adjacent numbers/values -Data can be categorized, ranked, evenly spaced/equal values between intervals (no ambiguity) --No true zero ---Doesn't measure 0 as a distinct value |
| Ratio Measurement/Scales | -Fully quantitative, includes rank ordering, equal/even spaced intervals, plus has an absolute zero point -Has a natural zero (temperature) -Allows make of proportional statements about measurement data (but only about non-zero numerical values) |
| Mutual Exclusion | - A "case" (what you're measuring) can only fit into one category |
| Exhaustivity | -Covered all bases by including a residual category |
| Residual Category | -Category for a response that does not fit neatly into the existing categories (ex. other/unsure) |
| Predefined Response Categories | -Ready-made responses that the researcher has made and categorized |
| Specify Responses | -Residual category where test taker inputs their own answer/response |
| Likert Scales | -Ordinal scale measure --Ranking responses in terms of magnitude --Unclear if the intervals between response are equal in terms of magnitude |
| Multiple Indicator Measures (MIM) | - For abstract or complex variable measurement (ex. intelligence) -Measure dimensions of a variable (ex. subtypes) --Measure psychological disorders in a population -Useful in ratio-scale level measurement |
| Categorical Data | -Nominal or ordinal scale |
| Continuous Data | -Interval or ratio scale |
| Reliability | -The consistency or stability of scores |
| Validity | -How well a measure actually measures what it is supposed to |
| Reliability Testing: Retest Reliability | -Consistency of a group of individuals’ scores on a test over time |
| Reliability Testing: Equivalent Forms Reliability | -Consistency of a group of individuals’ scores on two versions of the same test |
| Response Similarity | -Measures of similarity between responses --Correlation strength --Testing the independence of two points in statistical data ---Item by item comparisons of responses |
| Reliability Testing: Internal Consistency Reliability | -Consistency with which items on a test measure a single construct |
| Split-Half Method | -Measure Internal Consistency Reliability |
| Flaw: Split-Half Method | -Some items measure different dimensions of a variable(used to compute test result) |
| Reliability Testing: Inter-Rater Reliability | -The degree of consistency or agreement between two or more scorers, judges, observers, or raters |
| Pseudoscience | -Based on reliable but invalid measurements |
| Accuracy | -Degree of validity truth |
| Validity Evidence Based on Content: Face Validity | -Whether the items appear to represent the construct and whether the test or instrument looks valid |
| Reliability Flaw: Face Validity Solution to Flaw | -Easier for test taker to "game" tests (produce an outcome they want) -Use indirect measures with low face validity (harder for test taker to interpret) --Less obvious answer ---Lower chance of participants being able to "game" the test |
| Psychometrics | -Specialized branch of psychological research, focused on measurement and testing |
| Validity Evidence Based on Content: Using Psychometrics | --Determine if someone is actively trying to "game" the test/isn't paying attention ^By comparing high face validity and low face validity/indirect question responses: ---similar=unlikely ---dissimilar=likely |
| Validity Evidence Based on Content: Criterion Validity | -Degree to which scores predict or relate to a known criterion (ex. future performance or an already established test) |
| Reliability Flaw: Criterion Validity Solution to Flaw | -Assuming a measure is valid -Be sure the comparative measures are valid too --Investigate sources of "valid" information! |
| Validity Evidence Based on Content: Predictive Validity | -Degree to which scores obtained at one time correctly predict the scores on a criterion at a later time |
| Statistic | -A numerical characteristic of sample data |
| Parameters | -Actual numerical characteristic of a population NOTE: Statistics always/only ESTIMATE the parameters of a population |
| Sampling Error | -Differences between sample values and the true population parameter --"Luck of the draw" --May not represent general population accurately |
| Reduce Error via Sampling Methods (2 ways) | 1. Increase sample size 2. Improve representativeness -Random sampling |
| Random Sampling | -Each member of a population has equal chance to be chosen for participation --EPSEM rule (equal probability of selection method) |
| Procedures when Sampling | 1. Identify target population 2. Build an element list 3. Decide on sample type |
| EPSEM Rule/Principle | -Equal probability of selection method --Random sampling |
| Response Rates | -The percentage of people selected to be in a sample who actually participate in the research study |
| Strategic Sampling (2 types) -Ensuring representativeness and preserving random selection | 1. Cluster sampling 2. Stratified sampling |
| Cluster Sampling | -Sampling method where clusters are randomly selected --Rather than individual-type units (such as individual people) in the first stage of sampling. |
| Stratified Sampling | -Sample population is divided into distinct, non-overlapping subgroups called strata based on shared characteristics (ex. age, gender, or income) |
| Response Bias | -Only leave response if response is really good or really bad --Polarized |
| Response Bombing | -Motivated and organized group responds in large numbers to skew the results |
| Convenience Sampling | -Non-random -Obtaining participants who are readily available, volunteer, or are easily recruited for inclusion in a sample |
| Quota Sampling | -Non-random -A researcher decides on the desired sample sizes (quotas) for groups identified (elements) for inclusion in the sample, followed by convenience sampling from the groups |
| Quantitative Data Role in Statistics | -Foundation of statistical analysis, allowing: -Objective measurements -Pattern identification -Hypothesis testing -Predictions |
| Univariate Statistics | -Descriptive statistics dealing with one variable |
| Bivariate Statistics | -Descriptive statistics dealing with two variables |
| Multivariate Statistics | -Descriptive statistics dealing with more than two variables |
| Statistics: Range | -The difference between the lowest score or the highest score on an ordinal, interval, or ratio scale --Sensitive to outliers |
| Outlier | -An atypical score (or data value) that can be well outside the range of all of the other scores |
| Histogram | -Visual representation of frequency information using a bar graph --Useful in identifying types of data distribution |
| Normal Distribution | -Bell curve |
| Skewed Distribution | -Leaning bell curve --Favouring higher or lower end of the distribution |
| Uniform Distribution | -Evenly spread data --No obvious mid point/peak |
| Bimodal Distribution | -Two midpoints/peaks --Suggest two overlapping normal distributions |
| Edge Peak Distribution | -Most frequent score/value is outside normal range |
| Curve Diagrams | -Measuring on a continuum with large number of measurement values |
| Bar Charts | -Used to compare data about one variable in terms of other category-type variables |
| Line Graphs | -Type of chart that displays information as a series of data points connected by straight line segments |
| Summary Statistics: Measures of Central Tendency | -Indicate the "typical" value of a variable --Best prediction |
| Summary Statistics: Measure of Variation | -Indicate the degree to which the values/scores of a variable vary in relation to one another |
| Central Tendency Statistic: Types, Scale/Measurement Types, and Weaknesses | -Mean --Interval and Ratio ---Weakness: Outliers -Median --Ordinal, Interval and Ratio ---Weakness: Skewed or Multimodal Distributions -Mode --Any scale type ---Weakness: Skewed Distributions |
| Standard Deviation | -The average amount by which individual values (or scores) of scores of a variable differ – or “vary” – from the mean of that variable. --Allows calculation of average ranges using normal distribution |
| Bi-Variate Descriptive Statistics: Comparison of Means | -Degree of similarity, magnitude of difference |
| Covariation | -How two variables vary together --Pos: same direction --Neg: opposite direction |
| Contingency Tables | -Visually represented by "Cross-Tabulations" to identify relationships between variables |
| N-O | -Nominal and Ordinal variables --Categorical relationships between independent and dependent variables ---Group = variable |
| I-R | -Interval and Ratio variables --Meaningful order/equal intervals between values assigned to a variable --Used as independent or dependent variables --Phenomenon related = variable value |
| Research Validity | -Research quality --The extent to which the results of the research or conclusions drawn from data can be generalized to real life. ---Cannot be computed, it's a matter of judgement |
| Internal Validity | -The correctness of inferences made by researchers about cause and effect --The extent to which a study’s conclusions are supported by the study’s design, how it was conducted, and how the research data that were gathered were analyzed |
| Common Factors that Reduce Internal Validity -Ways to avoid?? | -History effects (outside infl) --Reduce time between treatment and measurement -Maturation effects |
| Threat to Internal Validity: History | -Any event that can produce the outcome, other than the treatment condition, that occurs during the study before posttest measurement |
| Threat to Internal Validity: Maturation | -Any physical or mental change that occurs with the passage of time and affects dependent variable scores |
| Threat to Internal Validity: Instrumentation | -Changes from pretest to posttest in the assessment or measurement of the dependent variable |
| Threat to Internal Validity: Testing | -Changes in a person’s score on the second administration of a test resulting from having previously taken the test |
| Threat to Internal Validity: Regression Artifact | -Effects that appear to be due to the treatment but are due to regression to the mean |
| Threat to Internal Validity: Attrition | -Loss of participants because they don’t show up or they drop out of the research study |
| Threat to Internal Validity: Selection | -Production of nonequivalent groups because a different selection procedure operates across the groups |
| Threat to Internal Validity: Additive and Interactive Effects | -Differences between groups are produced because of the combined effect of two or more threats to internal validity |
| To "game" a test | -Knowingly or unknowingly manipulating results of a test to achieve a desired outcome |
| Purpose of Sampling | -Samples are used to make generalizations about populations |
| Cluster Sampling Weakness | -Probability of a sampling error occurring is greater because each sub-category contains smaller participant samples |
Created by:
user-1982862
Popular Psychology sets