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Exam II
Marketing Research
| Question | Answer |
|---|---|
| Causation | means that a change in one variable will produce a change in another |
| Concomitant variation (Correlation): | two variables are correlated and will vary together in a predictable manner |
| Time order of occurrence: | X must occur before Y |
| Elimination of extraneous factors: | Evidence that there is no competing explanation for the relationship |
| Three conditions for causal relationship | Concomitant variation (correlation), time order of occurrence, and elimination of extraneous factors |
| An Experiment is a procedure in which | - 1 or more independent variables are systematically manipulated: the manipulation of the independent variable = treatment - Data on the outcome are gathered (dependent variable) – Other variables that may influence the effect variable are controlled |
| Independent variable: | manipulated variable |
| Treatment group: | group with treatment, i.e. portion of the sample (or population), exposed to the manipulation of the independent variable |
| Control group: | group without treatment, i.e. portion of the sample (or population), for which the independent variable remains unchanged |
| Dependent Variable: | outcome that is being measured |
| laboratory experiment: | a research study conducted in a contrived setting in which the effect of all, or nearly all, influential but irrelevant independent variables is kept to a minimum |
| field experiment | a research study conducted in a natural setting in which the experimenter manipulates one or more independent variables under conditions controlled as carefully as the situation will permit |
| Internal Validity | Whether the manipulation of the independent variables actually cause the effect |
| External Validity | Whether the experiment results can be generalized beyond the experimental setting |
| Internal validity threat - Design contamination: | control group finds out about treatment condition |
| Internal validity threat - Experimenter bias: | researcher’s expectations influence experiment results. Researcher creates experimenter bias by giving cues how participants expected to behave |
| Internal validity threat - Confound: | variable that researcher failed to control for during experiment |
| Internal validity threat - History threat: | other factors/events occur during experiment, history threat compromises internal validity if impacts treatment and control groups differently |
| External validity threat - Selection and Self-Selection effects: | occurs where experiment participants differ than target market |
| External validity threat - Experiment conditions: | participant aware of experiment and experience novelty of being in experiment |
| Sampling | The process of obtaining information from a subset of a larger group |
| Sample | The group of individuals chosen |
| Population | The total group of people from whom we need to obtain information |
| Census | - Data about every member of the population - Entire population is surveyed |
| When is census appropriate? | • Population size is quite small • Information is needed from every individual in the population • Cost of making an incorrect decision is high • Sampling errors are high |
| When is sample appropriate? | • Population size is large • Both cost and time associated with obtaining information from the population is high • Quick decision is needed • To increase response quality since more time can be spent on each interview • If census is impossible |
| Total Error | Difference between the true value and the observed value of a variable |
| Sampling Error | Error is due to sampling |
| Non-sampling Error | Error is observed in both census and sample |
| Sampling Process | Step 1: Determining the Target Population Step 2: Determining the Sampling Frame Step 3: Choose a Sampling Procedure |
| Subset problem: | occurs when the sampling frame is smaller than the population |
| Superset problem: | occurs when the sampling frame is larger than the population |
| Intersection problem: | occurs when some important elements of the population are omitted from sampling frame |
| Probability Sampling | Known, non-zero chance of being selected (sampling frame is crucial) |
| Advantages of probability sampling | - Sampling error can be computed - Determine the degree of accuracy |
| Disadvantages of probability sampling | - Expensive - Take time and effort to design and execute |
| Non-Probability Sampling | Probability of selecting any particular member is unknown |
| Simple Random Sample | Each member of the population has an equal probability to be selected |
| Systematic Random Sample | • Sample in a systematic way • Each member of the population has an equal probability to be selected |
| Stratified Sample | Population is partitioned into mutually exclusive strata - elements are randomly selected from each stratum |
| Proportionate Stratified Sampling | • Number of objects/sampling units chosen from each group is proportional to the number in the population • Directly Proportional or Inversely Proportional stratified sampling |
| Disproportionate Stratified Sampling | • Sample size in each group is not proportional to the respective group sizes • Used when multiple groups are compared and respective group sizes are small |
| Directly Proportional Stratified Sampling | • Strictly follow the population proportion • All segments/groups are valued equally |
| Cluster Sample | • Population is partitioned into mutually exclusive clusters – Randomly select some clusters – Members in the selected clusters are all selected |
| Convenience Sampling | Procedure of obtaining the people or units that are most conveniently available |
| Judgment Sampling | An experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member |
| Snowball Sampling | Initial respondents are selected by probability methods. Additional respondents are obtained from information provided by the initial respondents |
| Quota Sampling | The sample contains the same proportion of characteristics specified by the researcher as is evident in the population being examined |
| Non probability sampling advantage | Costs and trouble of developing sampling frame are eliminated |
| Non probability sampling disadvantage | Results can contain hidden biases and uncertainties |
| What is non probability sampling used in? | • The exploratory stages of a research project • Pre-testing a questionnaire • Dealing with a homogeneous population |
| Total Error = | Sampling Error + Non-Sampling Error |
| Parameter | - Variable in the population - Measured characteristics of the population - Lower case Greek letters as notion |
| Statistics | - Variables in the sample - Measures computed from the sample data - English letters as notion |
| Sample mean | (sum of terms) / (number of terms) |
| Sample variance | sum of squared values subtracted by mean divided by number of observations subtracted by 1 |
| Standard deviation | square root of variance |
| Median | midpoint of the distribution |
| Mode | the value the occurs most often |
| Sampling distribution of sample mean | = population mean |
| Data editing identifies... | omissions, ambiguities, and errors in responses |
| Problems identified with data editing: | - Interviewer Error - Omissions - Ambiguity - Inconsistencies |
| Coding closed-ended questions involves... | specifying how the responses are to be entered; assigning a numeric value to responses |
| Why are open-ended questions difficult to code? | lengthy list of possible responses is generated |
| Close-ended question: | Frequency x rank – Calculate for every option and compare |
| Frequency Distribution | • Reports the # of responses that each question received • Organizes data into classes or groups of values • Shows # of observations that fall into each class • Can be illustrated simply as a number or as a % or histogram • Categories may be combined |
| Cross Tabulations | • Studies the relationships among and between variables • Sample is / to learn how the dep. var. varies from subgroups • Frequency distribution for each subgroup is compared to the freq. distri. for the total • The 2 variables must be nominally scaled |
| Null Hypothesis | • Population parameter is equal to a number; Ex: 𝜇1 = 0 • There is no difference between two groups; Ex: 𝜇1 = 𝜇 |
| Alternate Hypothesis | • Population parameter is not equal to a number; Ex: 𝜇1 ≠ 0 • There is a difference between two groups; Ex: 𝜇1 ≠ 𝜇2 |
| If you find a difference between two groups (i.e., there are not equal) then you... | “reject the null”, if not, then you “accept the null” |
| Null Hypothesis: the ____________ to be tested | hypothesis |
| Alternative Hypothesis: what is _________ to be true if the null hypothesis is false | believed |
| Chi-square test | • Test of Independence (Are there associations between two or more variables in a study?) • Test of Goodness of Fit (is there a significant difference between an observed frequency distribution and a theoretical frequency distribution?) |
| Chi-Square As a Test of Independence | • Statistical independence: TWO variables are statistically independent if a knowledge of one would offer no information as to the identity of the other |
| Null Hypothesis Ho | Two (nominally scaled) variables are statistically independent |
| Alternative Hypothesis Ha | The two variables are not independent |
| Single mean hypothesis test | Compare a sample to the population Population mean must be set. Can we generalize the sample test result? |
| Two unrelated samples hypothesis test | Compare one sample to another sample. Usually the two samples are two different groups of participants. Or same group of participants evaluate on different attributes |
| Two related samples hypothesis test | Compare the results of a group of the same participants before and after a marketing action. Examine the pre- and post- effect |
| P-Values approach to hypothesis testing: | • p-value = largest level of significance at which we would not reject ho • The smaller the p-value, the greater the confidence • p-value is sensitive to sample size; a large = a low p-value • p-value is less than 0.05 we can reject the null hypothesis |
| Measure of association between two continuous (in this case interval- or ratio- scaled) variables | correlation coefficient |
| Using advertising dollars to predict store traffic | simple regression model |
| Correlation analysis | Measures strength of the relationship between two variables |
| Pearson Correlation Coefficient (r) | Provides a measure of the degree to which there is an association between two variables (X and Y) |
| A correlation coefficient always contains two parts: | - Direction of the correlation: positive or negative - Strength of the correlation: strong, moderate, or weak |
| Measure of Causation: Regression Analysis | • Used to relate 2 or more variables. • Objective = build a regression model relating the dep. variable to 1 or more indep. var. • Contains: – A variable of interest: dependent/response variable (Y) – Independent variables (Xs) – what Y is related to |
| R-square: | tells us how well the model fit the data |
| Causation determins: | do changes in X cause changes in Y |
| The question of whether the results of an experiment apply to the real world refers to | external validity |
| To infer a causal relationship, which of the following types of evidence does a researcher need? | -evidence of high level of internal validity and a strong association between an action and an outcome -evidence that the action preceded the outcome -evidence of no strongly competing explanations for the relationship between an action + outcome |
| A researcher is doing a study on wine consumption, She has defined 3 different income groups and selects equal number of respondents from each of the 3 income groups. This is an example of | stratified sampling |
| A selection of every tenth subscriber to the New York Times is an example of | systematic random sampling |
| Jean wants to do a study on Network deer hunters. To effectively reach those hunters, she first interviews one hunter and asks them to recommend some other hunters to take the interview. This is an example of | snowball sampling |
| When all the elements in a population are included in a study, the result is a __________. | census |
| Which of the following is not a possible problem identified with data editing? | Found the sampling error |
| The coding of open-ended questions | - is much more difficult to code than for closed-ended questions - could require the coder to make a judgmental decision - can be difficult when the handwriting of the respondents is illegible |
| Which of the following statements is true of the correlation analysis? | when there is no association between two variables, the Pearson correlation coefficient is zero |
Created by:
coralis
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