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97 Cards in this Set
- Front
- Back
Basic rules for survey questions (3) |
1. Quality - Questions should be easy to comprehend. The shorter, the better. - No specialized jargon, double-barreled questions or double negatives.
2. Bias - Avoid leading questions, loaded words, and mentioning well-known individuals or institutions.
3. Order of questions - Start with an introduction, questions that relate to the same topic should be grouped together, place the demographic questions at the end. - Less sensitive questions should precede more sensitive questions. - General questions should precede more specific questions. - The order in which the questions are presented can produce biased
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Pretest
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Initial testing of the instrument among a small group of respondents who are similar to the larger sample that the researcher hopes to target with the survey. |
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Purposes of pretest (3) |
1. Identify questions that are unclear to respondents |
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Sampling goals (3 steps) |
1. To identify a population 2. To survey a small selection of that population and 3. To draw accurate conclusions about the entire population based on the small selection |
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Population |
An entire group of people, a whole collection of objects.
Example: in the media world, all newspaper stories on a subject |
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Sample |
A small portion of the population that is used to represent the population |
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Random selection |
Each member of the population has an equal chance of being selected for the sample |
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Elements |
The individual members of the population |
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Sampling frame |
A list of all elements in the population |
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Types of sampling (2) |
1. Probability ( = random) 2. Non-probability ( = non-random) |
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4 main points in deciding whether to use probability or non-probability sampling |
- purpose - cost - time - acceptable error |
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Simple random sampling |
Selecting subjects based on the premise that each subject in the population has an equal chance of being selected.
There is no pattern. |
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Systematic random sampling |
Variant of simple random sampling that requires a list of the population.
Pattern: numbering of every subject and then using a mathematical process to select participants |
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Stratified random sampling |
The population is divided into homogenous subgroups, from which several simple random samples are conducted to determine participants for the study.
Pattern: follow equal representation of subgroups |
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Cluster random sampling |
Dividing the population into distinct clusters, usually geographic locations.
Pattern: randomly selecting one cluster and surveying everyone in the cluster |
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Multi-stage cluster sampling |
Similar to cluster sampling, but the researcher chooses a sample at various stages. |
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5 types of probability sampling |
1. Simple random sampling 2. Stratified random sampling 3. Systematic random sampling 4. Cluster random sampling 5. Multi-stage cluster sampling |
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Causes of sampling errors (3) |
- Chance - Poor sampling techniques - Non-sampling error, caused by the measuring instrument or the participants themselves |
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3 types of non-probability sampling |
1. Available / convenience sampling 2. Snowball sampling 3. Quota sampling - Not generalizable to population as a whole. |
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Available / Convenience sampling |
Participants are chosen for inclusion in a study because they are a captive audience and/or are willing to participate |
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Snowball sampling |
Asking respondents to forward the survey to friends and family. |
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Quota sampling |
Similar to a stratified random sample.
Difference: the participants in a stratified sample are randomly selected, but those in a quota sample are not. |
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Survey types (5) |
1. Mail 2. Telephone 3. Personal 4. Group administered 5. Online |
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Survey |
Any procedure to ask questions of respondents' behavior, opinions, attitudes, tastes. |
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Comparability (3) |
- To compare social groups (within society and between societies) - To assess trends - To apply statistical analyses |
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Advantages mail survey (3) |
• Less costly than telephone or personal interview • Respondents might feel less inhibited since interviewer is not present |
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Disadvantages mail survey (4) |
• Low response rate |
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Advantages telephone survey (4) |
• Good response rate |
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Disadvantages telephone survey (3) |
• Technology-related difficulties in reaching respondents • Does not allow use of visuals |
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Advantages personal interview (3) |
• Good response rate |
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Disadvantages personal interview (3) |
• High costs |
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Advantages group-administered survey (2) |
• Good response rate |
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Disadvantages group-administered survey (2) |
• Potential interaction between respondents • Potential high costs |
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Advantages online survey (4) |
• Can access large number of respondents at one time • Data can be automatically entered into program for analysis • Allows use of visuals and audio |
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Disadvantages online survey (3) |
• Not all potential respondents have Internet access or use the Internet |
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Advantages open-ended questions (2) |
• Respondents can freely respond in unique manner • Respondents might reveal unexpected insights |
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Disadvantages open-ended questions (2) |
• Takes longer to answer questions • Responses might not be legible
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Advantages closed-ended questions (2) |
• Respondents can answer questions quickly • Responses can be tabulated quickly |
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Disadvantages closed-ended questions (2) |
• Respondents are limited in their responses • Respondents might not agree on the meaning of response categories |
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Hypothesis testing |
The statistical procedure designed to test a claim. - Involve at least 2 variables and the relationship between them. |
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2 types of hypotheses |
1. Null hypothesis (H0) - No difference or change - Never stated, always implied 2. Alternative hypothesis (H1) - Statement of prediction - Actual research hypothesis
The goal of hypothesis testing is to reject one hypothesis and accept the other. |
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Which values are important when deciding to reject or accept the hypothesis? (4x) |
1. P-value (significance level) 2. The effect size value |
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Significance level (P-value) |
The probability that you are wrong when rejecting the null hypothesis. - This is the idea of determining if results observed may be due to chance alone. - With alpha = 0.5 you are 95% sure the outcome is not due to chance. |
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Errors of analysis: Type I error |
Type I error refers to being wrong when rejecting the null hypothesis when the researcher really should have accepted it. - Alpha error - False positive - Falsely accepting H1
The two types of error have an inverse relationship. |
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Errors of analysis: Type II error |
Type II error refers to being wrong when accepting the null hypothesis when the researcher really should have rejected it. - Beta error - False negative - Falsely rejecting H1
The two types of error have an inverse relationship. |
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Effect size |
The degree to which variables are interdependent. - Reflects the proportion of variance in the dependent variable that is associated with levels of an independent variable. |
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Arrays |
A listing of the variable for each case in the data set. |
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Frequency distributions |
Each unique attribute of a variable is listed, along with a count of the frequency with which it occurs. |
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Relative frequencies |
With large numbers statisticians convert frequencies to relative frequencies (i.e. percentages). |
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Rate |
A proportion converted to a whole number (i.e. birth rate per 100 individuals). |
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Ratio |
Compares the size of two numbers by placing them in a fraction. |
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Percentage change |
Generates a percentage to reveal the amount of change over time. |
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Central tendency |
A construct that refers to the score or attribute of a variable that is most typical or representative of the variable distribution.
• Mode • Skewness |
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Mode |
Defines what is most typical or representative as the attribute or score in a variable distribution that occurs most frequently. |
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Median |
The score or attribute of the case that is in the middle. |
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Arithmetic mean |
Defines the most typical or representative score as a distribution’s arithmetic balance point ( = average).
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Standard deviation |
Measures the amount of variation or dispersion from the average. |
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Range |
The difference between the highest and lowest scores of a distribution. • Inter-decile range, inter-quintile range, and inter-quartile range |
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Choosing the best measure |
1. Use the mode or percentage for a nominal variable.
2. Report the arithmetic mean and the standard deviation for distributions for quantitative (interval and ratio) variables in which the median and arithmetic mean are relatively close together.
3. Report the median for distributions in which the arithmetic mean and the median are not very close together.
4. Continue to use the commonly accepted measure of central tendency for a particular variable. |
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Contingency table |
Cross tabulation or crosstabs table.
Occurrences of attributes on one variable are tabulated across = contingent on the attributes of a second variable.
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Theory |
A group of abstract constructs including statements about the relationship between them. |
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Construct |
Abstract term. Not directly observable, has to be made observable > operationalization into variables.
Example: agression, intelligence |
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Variable |
A characteristic or attribute that varies among that which is being studied. - Temporal order and probable causation |
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Independent Variable (IV) |
Those that probably influence or affect outcomes. Examples: treatment, manipulated, antecedent, predictor |
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Dependent Variable (DV) |
Those that are the presumed result of the influence of the IV. Examples: criterion, outcome, effect |
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Important criteria for a good operationalization (3) |
– You have valid measures You measure what you wanted to measure – You have a reliable measure The measure always measures the same – You have an objective measure The measure measures the same when you conduct the study or when your neighbor does it |
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Rule of thumb |
Use well-established scales, control the environment, use pc-driven measures and assign participants randomly to the conditions. |
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Real experiment |
Participants have to be randomly assigned IV: has to be manipulated DV: has to be measured |
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Quasi experiment |
Experimental conditions depend on attributes of participants.
• Some IVs cannot be manipulated Examples: gender, culture, age (Can never have within design) |
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Field experiment |
- Intervention in the "real" world - No laboratory
Advantages: – Good external validity Disadvantages: – Influence of further factors? – Control conditions? |
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Repeated measurements |
DV is measured more than once. - Example: communication skills after 1, 2, 3, or 4 glasses of alcohol. |
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Within design |
Same sample is used for each condition. – At least two repeated measurements – Participants are the same or...
- Example: interview same person after 1, 2, 3, and 4 glasses of wine. - Problems: 1) boredom and 2) training effect (less errors) |
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Between design |
Different sample is used for each condition. – Each condition has different participants – No repeated measurements
- Example: different sample/person for 1, 2, 3, and 4 glasses of wine. - Problems: 1) different drinking capabilities, 2) communication skills, and 3) sample too small. |
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Which measure of the central tendency do you use for which level of measurement? |
Nominal - mode Ordinal - median Interval - mean, standard deviation Ratio - mean, standard deviation |
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What is a measurement? |
x = the measurement
m = the mean aj = condition effect E = error in measurement – Condition – Participant attributes (e.g., intelligence, drinking capabilities)
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Problems with repeating (3) |
• Some measurements are not repeatable • Some cover stories only work once • Data collection – Training effects? – Comparable?
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Mixed designs |
Some IVs are repeated, some are not.
Example: Within: pre- and post measurements of aggressive behavior. Between: violent vs. non-violent digital game. IV 1: T1 and T2 of measurement IV 2: violent versus non-violent game |
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Explicit measurements |
Participants might not know that there is something measured, but they can actively influence it.
Examples: • Recall • Behavior |
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Implicit measurements |
Participants might know that there is something measured but they cannot actively influence it.
Examples: • Response latencies • fMRT |
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Advantages and disadvantages explicit measurements |
Advantages: - (often) easy to interpret
Disadvantages: - Self-fullfilling prophecy? - Participants can actively influence the measurement - No underlying processes |
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Advantages and disadvantages implicit measurements |
Advantages: - Underlying processes
Disadvantages: - Interpretation? - How to analyze? - (Often) laboratory necessary - Some measurements need expensive equipment and a very good technician - Data confusion? - Fragile: Fine measurements can be easily destroyed (e.g., noise, concentration) |
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Information reduction |
Filter information on what's important and what's not.
- Start by finding correlations (r). • r can be between -1 and +1
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Index (vs. factor) |
Type of composite measure that summarizes and rank-orders several specific observations and represents some more general dimension – Several items which all count the same
Example: final exam > only 1 grade |
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Factor (vs. index) |
Type of composite measure composed of several items that have a logical or empirical structure among them. – Finding cohesion/pattern - (Factor = scale)
Example: personality test > big 5 (extraversion, agreeableness, etc.) |
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Factor loading |
The contribution of items to the factor |
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Factor-analytic (inductive) approach |
- Use theory to create items. - Analyze how these items cluster together.
The correlations among a number of items is the basis. |
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Factor analysis + Reliability analysis |
Factor loading
Next step: Reliability analysis
Reliability is done by Cronbach’s alpha – Run a reliability analysis for each factor |
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Data reduction |
Find the pattern/structure among many items.
Confirmative (theoretically based) • Example: Big Five • Media induced emotions |
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Factor analysis assumptions |
Assumptions • Dichotomous variables also possible |
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Rotation (2 types) |
To create a better distribution of variables.
Varimax - Factors are independent from each other - Factors do not correlate with other factors
Oblique (Oblimin and Promax)
Based on theoretical assumptions. |
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Item |
The questions asked (to create the factor). |
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Which test in SPSS for...
...two levels of IV + repeated measurements? |
Paired sample t-test - Within design |
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Which test in SPSS for...
...two levels of IV + unrepeated measurements? |
Independent sample t-test - Between design |
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Which test in SPSS for...
... more than two levels of IV + repeated measurements? |
ANOVAr (= ANOVA with repeated measurements) - Within design |
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Which test in SPSS for...
... more than two levels of IV + unrepeated measurements? |
One-factorial ANOVA - Between design |
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Which test in SPSS for...
... more than two IVs + only unrepeated measurements? |
Factorial ANOVA - Between design |
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Degrees of Freedom (df) |
The number of values in a statistical analyses that are free to vary. - They can tell you something about sample size, number of IV levels, number of factors etc. - N-1, N = sample size - related to critical value, alpha level, and sample size
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