MIDTERM REVIEW Q'S - marketing Research
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Feb 20, 2024
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Chi-square test -
Draw a conclusion on the table.
-
Chi-square (2 columns) data
individual, income, brand
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Hypothesis statements must be clear -
Just run the test (write hypothesis statements correctly)
-
Are these two variables related or dependant or not related
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What is my alternate to hypothesis? -
Ho: income and brand are independent or not dependany
Ha: Income and brand are dependant.
2
nd
step – insert, pivot table, rows (income, (column (brand) values (individual) 3
rd
step – take data and transfer to chi square test calculator and process the results with the values in the table.
(If just given the table in the exam, just replicate the data to the chi-test calculator)
-
When we look at the results, we are going to compare it to the alpha which is 0.05 and in this case the P-value was .225869. So, the interpretation is.
-
We fail to reject the null because the p value is less than 0.05
-
They are independent because we fail to reject the null hypothesis.
Regression examples:
-
What’s the effect of the set of variables
do any of these variables influence the outcome?
-
Go to data in excel, data analysis, choose regression, select the Y range (outcome of interest)
such as sales (Y) and then the X range (column D) the discounts, then choose Labels (box needs to be checked) then hit OK.
-
Then from the table convert everything under the P-value to compare to the 0.05
then see what influences sales -
Go to coefficient columns and see if it has a negative or positive effect (sales go down by
-0.17) T-test examples:
-
Two-tale t-test and the right tale t-test
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=Average and select data. =STDEV, =Count and then look at the df, p-value is 0.079 which is more than 0.05 -
We fail to reject the null because it is greater than 0.05 (this goes into the conclusion tab)
-
(The average number of swipes is not different from 70)
interpretation
Next example: Dates
-
Avg no of dates < = 7
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Avg no. of dates > 7
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Select the columns (COUNTA)
-
Convert it into numeric format
-
P-value is less than 0.05
conclusion since p-value = 0.05, we reject the null
-
Avg no. dates > 7
interpretation.
Last test – 2 sample tests
-
Use t-test calculator hyperlink. Copy pastes the data, Group 1 and group 2 and then calculate and receive the values.
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T-tale P value is 0.5806 (reject the null) (fail the reject the null)
-
H0: Avg grades in A01 are NOT DIFFERENT from A02
stays
Samples Questions & Definitions:
-
B. Depth interviews
are not considered projective techniques.
-
Projective techniques
are a set of research methods used to uncover individuals' deeper thoughts, feelings, attitudes, or motivations indirectly, often by asking them to respond to
ambiguous stimuli. The goal is to elicit responses that reveal subconscious or hidden aspects of a person's psyche. The other options, A, C, and D, are all examples of projective techniques:
-
A. Sentence completion tasks
: Participants are asked to complete sentences or phrases, and their responses can reveal underlying thoughts and feelings.
-
C. Word association tasks:
Participants are presented with words or phrases and asked to respond with the first word that comes to mind. This can uncover associations and connections in their subconscious.
-
D
. Storytelling:
Participants are asked to tell a story or create a narrative based on a given stimulus or picture. This can reveal their attitudes, values, and emotions indirectly.
1.
If a subject forgets to show up to the second observation time period, the research project has experienced
-
Experimental mortality
2.
The before
-
after design with control group (or two group before-after design) may not control for
-
The interactive testing
3.
A nurse researcher wants to test the effects of a smoking cessation intervention. She randomly assigns a sample of smokers to experimental and control groups. She measures the amount of smoking both before and after the intervention for both groups. This experimental design is a n )
-
Post-test - only control group
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One - group pretest - post-test
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Non-equivalent control group
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Pretest - post-test control group
-
Pretest- post control group
is a study design in which randomization is done to allocate the subjects in two groups one being experimental and other control group.
Further in this type of study measurements are done both pre and post intervention unlike any other study design in which measurement is usually done post intervention only
4.
The nurse researcher is planning a quantitative study. What does the nurse identify as an advantage of using this research approach?
- The process is objective.
- The number of phases is variable.
- Mathematic calculations are minimal.
-Use of the findings in nursing practice can be omitted
* Quantitative research is known for its objective nature and its objective process. In order to find patterns, connections, and trends, numerical data must be gathered and analysed. This strategy minimises the impact of individual bias and subjective interpretation by relying on systematic techniques and standardised instruments to collect data. The information gathered frequently takes the form of quantifiable variables, enabling statistical analysis to produce results. The study process' objectivity improves the findings' dependability and trustworthiness.
* The other options mentioned do not typically align with advantages of quantitative research:
Variable number of phases: Rather than being a disadvantage, the number of phases in
a research study should ideally be chosen depending on the research topic, design, and
methodology.
There is little use of mathematical calculations in quantitative research, which frequently
uses calculations to analyse data and develop conclusions. Minimal calculations could indicate a shallower level of analysis, which might not be a major advantage.
-
It is possible to forego using the findings in nursing practise: One of the main objectives of nursing research is to apply research findings in nursing practise. One of the primary goals of performing field research would be defeated if the findings weren't used.
-
Using a quantitative research approach gives the distinct advantage of being an objective process, the nurse researcher concludes. The impact of individual bias and subjective interpretation is minimised by the systematic collection and analysis of data using standardised procedures and tools. Quantitative research strengthens the dependability and trustworthiness of its conclusions by emphasising numerical data and statistical analysis. Other approaches were investigated, however they do not accord with the benefits of the quantitative
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research approach, such as varied phases, simple computations, and not applying the results to nursing practise. The nurse researcher's decision to place a strong emphasis on impartiality highlights the rigorous and methodical nature of
quantitative research, which enhances its ability to produce insightful data that will be useful for nursing practise and healthcare.
5.
The nurse researcher is planning to use a randomized control trial for the design of a quantitative study. What will occur when this design is used?
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Data will be collected as it unfolds
-
The researcher will serve as an observer.
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A large sample will be used from multiple sites and settings.
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Data will be collected on events that occurred before the study
*
Data will be collected as it unfolds: This means that data will be collected in real-time during the study, typically as participants are randomized into different groups and receive different interventions or treatments. Data collection occurs during the course of the study.
- The researcher will serve as an observer: In an RCT, the researcher often plays the role of an observer who monitors the study participants, administers interventions, and collects data on outcomes. This is to ensure that the study is conducted in a controlled and standardized manner.
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A large sample will be used from multiple sites and settings: RCTs typically aim to include a large and diverse sample of participants from multiple sites or settings. This helps to enhance the generalizability of the study's findings to a broader population.
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- Data will be collected on events that occurred before the study: RCTs primarily focus on collecting data on events and outcomes that occur during the study period. Data collection is typically prospective, meaning that information is gathered as the study progresses, rather than on events that occurred before the
study.
1.
Descriptive Research:
This type of research involves describing the characteristics of a phenomenon. In this case, you might use descriptive research to quantify aspects of sales patterns, customer behavior, or other relevant factors.
2.
Explanatory Research:
If you need to understand the reasons behind
the observed phenomena, explanatory research can help by testing hypotheses and providing more in-depth insights.
3.
Causal Research:
Causal research aims to establish a cause-and-
effect relationship between variables. It can help determine whether specific factors are directly influencing the decline in sales.
4.
Data Analysis Research:
Analyzing existing data can provide valuable insights. This could include sales data, customer feedback,
and other relevant information that can be statistically analyzed to identify patterns or correlations.
5.
Causal Chains:
This involves exploring the causal relationships between different variables to understand how changes in one variable
may lead to changes in another.
6.
Random Sampling Research:
If you're conducting surveys or collecting data from a subset of the population, random sampling can help ensure that your findings are representative of the larger population.
In summary, the most appropriate approach would be to start with exploratory research
to understand the problem and then proceed to other types of research based on the insights gained. The specific research methods chosen would depend on the nature of the decline in sales and the information needed to address the issue effectively.
-
Descriptive Research
is focused on describing the characteristics of a phenomenon.
-
Causal Research
aims to establish cause-and-effect relationships between variables.
-
Quantitative Research
involves the collection and analysis of numerical data.
-
Qualitative Research
involves the collection and analysis of non-
numerical data, often to explore attitudes, behaviors, and experiences.
-
Hawthorne Method
refers to studies conducted at the Western Electric Hawthorne Works in Chicago in the 1920s and 1930s, which were influential in shaping ideas about workplace motivation and productivity. It's not a type of research method in the same sense as the others.
-
Explanatory Research
is conducted to explain the relationships between variables and to test hypotheses.
Which of the following statements is NOT true?
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Marketers have an interest in consumer motives because it is believed that motives offer a strong basis for predicting future behavior.
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A motive might be considered a state that produces certain behavior
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Marketers believe that an understanding the motives behind a behavior might allow them to predict future behavior
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The knowledge of motives allows marketers to understand the needs of the customers
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None of the above
7.
A high P-value shows that the null hypothesis is true. True or false?
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The statement is false because a high P-value indicates that the data are consistent with the null hypothesis, but it does not prove that the null hypothesis is true
8.
I wanted to test the hypothesis that more than 2/3 (67%) of VCU business students are non-smokers. If a (alpha level)=0.05, what is the conclusion of this hypothesis test?
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There is weak enough evidence to show the population proportion of VCU business students who are non-smokers is more than 0.67
9.
A very low P-value provides evidence against the null hypothesis
-
True
10. If the null hypothesis is true, you can't get a P-value below 0.01. True or false
-
False. You will get a P-value below 0.01 about once in a hundred times
11.
The following statement best describes the sample used in which type of descriptive design? "We will use a carefully selected sample, selection being based on household size, income level, and television viewing patterns. We must
also be concerned with the stability of the sample elements in order to ensure repeated availability for measurement."
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Longitudinal design
12. A longitudinal study is characterized by each of the following statements EXCEPT:
- It involves a panel, which is a fixed sample of elements.
- Elements may be stores, individuals, or other entities.
- The panel remains relatively constant through time.
- Characteristics of the elements, or sample members, are measured only once.
- Members may be added to replace dropouts or to keep the panel representative
13. Which type of primary data is concerned with anticipated or planned future behavior?
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Intentions
14. Which of the following statements about panels is FALSE?
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Panels typically allow the collection of more classification information than cross-
sectional studies.
- Panel data is more accurate than cross-sectional data because panel data tend
to be freer from errors associated with reporting past behavior.
- Panels reduce interviewer-respondent bias because of a trust built up through repeated contacts between the two individuals.
- No representativeness of panel members may be a major weakness of
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longitudinal designs.
- All of these statements about panels are true; none of these are false.
15. You should use the lowest level of measurement possible when developing a measure for some attribute.
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False
16. Ratio scales can be used to do everything that interval, ordinal, and nominal scales can do.
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True
17. A useful property of all scales above the nominal level of measurement is that of order.
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True
18. If s/he wanted to use the highest level of measurement possible, the researcher measuring
respondent age would most likely suggest using ____ and a(n) ____ scale.
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an open-ended item; ratio
19. Consistency is the hallmark of validity.
-
False
T-TALE EXAMPLES AND STEPS
To determine whether the average satisfaction from the researcher's study is
significantly different from the recent research (population mean of 8.1), you
can perform a hypothesis test. In this case, you can use a t-test for the mean
since you have the sample mean, sample standard deviation, and the sample size.
Here are the steps:
1.
Formulate the Hypotheses:
Null Hypothesis (H₀): The average satisfaction from the researcher's study is equal to the recent research (μ = 8.1).
Alternative Hypothesis (H₁): The average satisfaction from the researcher's study is different from the recent research (μ ≠ 8.1).
0:
=8.1
�
�
H
0
:
μ
=8.1
1:
≠8.1
�
�
H
1
:
μ
=8.1
2.
Choose the Significance Level (α):
Let's say you choose a common significance level of 0.05.
3.
Calculate the Test Statistic:
Use the formula for the t-test for a population mean:
Where:
ˉ
�
x
ˉ
is the sample mean (7),
�
μ
is the population mean from recent research (8.1),
�
s
is the sample standard deviation (1.3),
�
n
is the sample size (200).
4.
Determine the Critical Region:
Use a t-distribution table or statistical software to find the critical
t-values for a two-tailed test with 5.
Make a Decision:
If the calculated t-statistic falls into the critical region, reject the null hypothesis. If it doesn't, fail to reject the null hypothesis.
6.
Draw a Conclusion:
Based on your decision, draw a conclusion about whether there is enough evidence to say that the average satisfaction from the researcher's study is different from the recent research.
Performing these calculations will give you the statistical evidence to determine if the difference in average satisfaction is significant or not.
The null hypothesis (H₀) and alternative hypothesis (H₁) for the statement could be formulated as follows:
Null Hypothesis (H₀):
There is no significant difference in satisfaction levels between travelers who are served fresh food and those who are served microwaved food on long-distant flights.
Alternative Hypothesis (H₁):
Travelers who are served fresh food on long-
distant flights are more likely to be satisfied with their flying experience compared to those who are served microwaved food.
In a statistical test, the goal is to either reject the null hypothesis in favor of the alternative hypothesis based on the evidence from the data or fail to reject the null hypothesis due to insufficient evidence. The directionality of the alternative hypothesis suggests that we are interested in whether fresh food leads to higher satisfaction levels.