Vvce - Business Research Methodology MCQ - Internship (Project Work) 2014

When the alpha value is 0.05, it means that the level of significance is set at 5%. This indicates that there is a 95% confidence level in the interval estimate. In other words, there is a 95% probability that the interval will include the true population parameter. Therefore, the statement "you can be 95% confident that your interval will include the population parameter" is true.

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The symbols N and n are commonly used to represent population and sample size, respectively. N represents the total number of individuals or elements in a population, while n represents the number of individuals or elements in a sample. These symbols are widely recognized and used in statistics to differentiate between the size of the entire population and the size of the subset or sample taken from that population.

The correct sequence for the steps of research is as follows: First, the researcher needs to identify the research problem (step 1). Once the problem is identified, the researcher can then list the research objectives (step 2). After that, the researcher needs to determine the research methodology (step 4) to be used in the study. Once the methodology is decided, the next step is to collect the data (step 3). After the data is collected, it needs to be analyzed (step 5) to draw meaningful conclusions. Finally, the results and discussions (step 6) can be presented based on the data analysis.

When a study has been conducted and is available for others to see, it becomes secondary data. Secondary data refers to data that has been collected by someone else for their own purposes, but can be used by others for research or analysis. In this case, the study has already been conducted by someone else and the results are now available for others to access and use. Therefore, it is considered secondary data.

A Type-II Error occurs when you fail to reject the null hypothesis, even though it is actually false. In other words, it is a false negative result. This means that you incorrectly conclude that there is no significant difference or relationship between variables when there actually is. It is important to minimize Type-II Errors as they can lead to missed opportunities and incorrect conclusions in statistical analysis.

The given statement is an example of a Null Hypothesis. A null hypothesis is a statement that assumes there is no relationship or difference between variables being studied. In this case, the null hypothesis assumes that there is no relationship between higher motivation level and higher efficiency.

Creativity and Intelligence are perfectly positively correlated

A sample is a subset of a population. In statistical analysis, a sample is a smaller group that is selected from a larger population to represent the characteristics of that population. The sample is used to make inferences and draw conclusions about the entire population. It is important for the sample to be representative of the population in order to ensure accurate results.

The interview is a data collection method where the researcher directly interacts with individuals or participants to gather information. It involves asking questions and having a conversation with the interviewee to obtain qualitative or quantitative data. This method allows for in-depth exploration of the research topic and provides valuable insights from the perspective of the interviewee. It is commonly used in social sciences, market research, and qualitative studies.

A questionnaire is a tool for data collection because it is a set of questions designed to gather information from individuals. It allows researchers to collect data from a large number of people in a standardized and efficient manner. Questionnaires can be administered in various formats such as printed paper, online forms, or even through interviews. By using a questionnaire, researchers can collect data on various aspects of a research topic, such as attitudes, opinions, behaviors, or demographics. This data can then be analyzed and used to draw conclusions or make informed decisions.

The scientific method is preferred over other ways of knowing because it is more reliable, systematic, and accurate. It relies on empirical evidence and follows a structured approach, ensuring that results can be replicated and verified. The scientific method also emphasizes objectivity and minimizes bias, leading to more accurate and unbiased conclusions. Overall, these characteristics make the scientific method a more trustworthy and effective way of acquiring knowledge compared to other methods.

Statistics is used by researchers to analyze the empirical data collected in a study. This involves organizing and summarizing the data, identifying patterns and relationships, and drawing conclusions based on the data. By using statistical analysis, researchers can make objective interpretations of their findings and determine the significance of their results. This helps to provide evidence and support for their research claims, increasing the validity and reliability of their study.

Management research concerns all of the above options: marketing planning, production planning, and human resources planning. It involves systematic research to gather information and find out things about these areas of business and management. This research helps in making informed decisions and improving the overall effectiveness and efficiency of the organization. Financing decisions, although important, are not explicitly mentioned in the given explanation, so they are not included in the answer.

The correct answer is Marketing Research. This is because the top manager is asking for a report on how a new competing chocolate bar is being advertised, which falls under the scope of marketing research. Marketing research involves gathering and analyzing information about market trends, customer preferences, and competitor strategies to make informed marketing decisions. In this case, the manager is specifically interested in understanding how the new chocolate bar is being advertised, which is a key aspect of marketing research.

Both A and B include examples of quantitative variables. Age, temperature, income, and height are all quantitative variables because they can be measured and expressed numerically. Grade point average, anxiety levels, and reading speed are also quantitative variables because they can be measured on a numerical scale. Gender, religion, and ethnic group, on the other hand, are examples of qualitative variables as they cannot be measured numerically.

Sampling refers to the process of selecting a subset of individuals or items from a larger population to gather information or make inferences about the whole population. It involves selecting a representative sample that accurately reflects the characteristics of the population. Respondent selection is a term used interchangeably with sampling, as it refers to the process of selecting individuals or respondents for research or survey purposes. Therefore, the correct answer is Sampling.

A list of questions which is handed over to the respondent, who reads the question and records the answer himself is known as a questionnaire. This is a common method used in surveys and research studies to gather data from a large number of people. The respondent is given the freedom to answer the questions at their own pace and convenience. This method allows for standardized data collection and can be easily analyzed. It is different from an interview schedule or interview guide, where the questions are asked verbally by an interviewer.

A positive correlation is present when two variables move in the same direction. This means that as one variable increases, the other variable also increases, and as one variable decreases, the other variable also decreases. In other words, there is a direct relationship between the two variables, and they tend to change together.

Research is the process of working in a scientific way to search for the truth of any problem. It involves the systematic investigation, collection, analysis, and interpretation of data to answer a specific question or solve a problem. Through research, individuals or teams aim to contribute new knowledge, understand phenomena, test hypotheses, and advance existing theories. It is a rigorous and methodical approach that requires careful planning, data gathering, and critical analysis to establish reliable and valid findings.

The correct answer is "Numerical data that could usefully be quantified to help you answer your research question(s) and to meet your objectives." This answer accurately describes what quantitative data refers to. It specifies that quantitative data is numerical in nature and can be quantified to provide useful information for answering research questions and achieving objectives. This definition aligns with the common understanding of quantitative data as data that can be measured and analyzed using statistical methods.

The purpose of conducting research work as part of the MBA curriculum is to apply theoretical concepts in finding solutions to practical problems. Research helps students understand how to use their knowledge and skills to address real-world challenges faced by businesses. Additionally, research can assist in finding solutions when a business is in trouble by providing evidence-based strategies. It also helps create awareness among students about the career opportunities in the area of research. Thus, all of the given options accurately describe the purpose of conducting research in the MBA curriculum.

A Likert scale is a commonly used measurement tool in surveys and research studies to assess people's attitudes or opinions. It typically consists of a series of statements or questions that respondents rate on a scale. The options provided in the question represent different formats of the Likert scale, ranging from 3 to 7 points. The correct answer, "6-Point Likert Scale," is not a valid format as Likert scales typically have an odd number of response options to allow for a neutral midpoint.

In the section of Data Analysis and Discussions, you fully interpret and evaluate your results. This is where you analyze the data collected during your research and discuss the findings in relation to your research objectives and literature review. You may use statistical tools, charts, and graphs to present and interpret the data. Additionally, you can compare your results with previous studies and theories mentioned in the literature review to provide a comprehensive evaluation. The Data Analysis and Discussions section is crucial for drawing conclusions and making recommendations based on the results of your research.

The correct answer is "Techniques used." Qualitative and quantitative research are classified based on the techniques used to gather and analyze data. Qualitative research involves collecting non-numerical data through methods such as interviews, observations, and focus groups. On the other hand, quantitative research involves collecting numerical data through methods such as surveys, experiments, and statistical analysis. The choice of techniques used determines whether a research study is qualitative or quantitative.

The research objective of "To investigate new markets" aligns closely with the marketing function. This is because marketing involves identifying and analyzing market opportunities, understanding customer needs and preferences, and developing strategies to target and enter new markets. By conducting research on new markets, the marketing function can gather valuable insights and data to inform decision-making and develop effective marketing strategies to penetrate and succeed in those markets.

One-Way ANOVA is the correct answer because it is a statistical test specifically designed to compare means between two or more groups. This test is used to determine if there are any significant differences in the means of the groups being compared. It is commonly used when there is one independent variable and one dependent variable, and the goal is to determine if there is a significant difference in the means of the dependent variable across the different levels of the independent variable.

Simple Random Sampling is the least expensive and least time-consuming of all sampling techniques because it involves randomly selecting individuals from the population, without any specific criteria or stratification. This method ensures that every individual in the population has an equal chance of being selected, making it a fair and unbiased approach. Additionally, since there are no specific criteria or stratification involved, it requires less time and resources compared to other sampling techniques.

Sampling error refers to the discrepancy between a sample statistic and the corresponding population parameter. It occurs due to the fact that a sample is only a subset of the entire population, and therefore may not accurately represent the true characteristics of the population. Sampling error can occur due to various factors such as random sampling, sample size, and variability within the population. It is important to consider and account for sampling error when generalizing the findings from a sample to the larger population.

The median is not a measure of variability because it represents the middle value in a dataset, regardless of the spread or dispersion of the data points. Measures of variability, on the other hand, quantify the spread or dispersion of data points around a central value. Variance, standard deviation, and range are all measures of variability as they provide information about the spread of data points.

The size of an adequate sample is not determined by the researcher's insight into sampling. The size of the sample is typically determined by factors such as the nature of the population, the type of sampling design, and the degree of precision desired. The researcher's insight into sampling may influence the selection of an appropriate sampling design or the determination of the degree of precision desired, but it does not directly determine the size of the sample.

Conducting surveys is the most common method of generating primary data. Primary data refers to the data that is collected firsthand by the researcher for a specific purpose. Surveys involve directly asking individuals or groups of people questions to gather information. This method allows researchers to obtain data that is tailored to their research objectives and provides them with more control over the data collection process. Therefore, conducting surveys is a commonly used method for generating primary data.

In multiple regression, only one dependent variable is used. Multiple regression is a statistical technique that examines the relationship between a single dependent variable and multiple independent variables. It allows us to understand how the independent variables collectively influence the dependent variable. Therefore, the correct answer is 1.

When the sample size is more than 30, the appropriate method to measure the difference between means of the observations is the t-test. The t-test is used when the population standard deviation is unknown and the sample size is small. It calculates the t-value by comparing the means of two groups and determines whether the difference between them is statistically significant. The t-test is commonly used in hypothesis testing and allows researchers to draw conclusions about the population based on the sample data.

The simplest form of measurement is the nominal scale. This scale categorizes data into distinct categories or groups without any specific order or numerical value assigned to them. It is the most basic and least precise form of measurement, where data is only classified into different categories or labels. This scale is commonly used to classify data such as gender, ethnicity, or marital status, where there is no inherent order or hierarchy among the categories.

Exploratory research involves gathering information and exploring a topic that is not well understood or lacks previous research. Primary data collection is a crucial part of exploratory research as it involves collecting new and original data directly from the source. This data is collected through methods like surveys, interviews, observations, or experiments. By collecting primary data, researchers can gain a deeper understanding of the subject and generate new insights or hypotheses. Therefore, the correct answer is exploratory research.

A good report should have a well-organized structure, with a clear sequence of information and a logical flow. It should also have relevant and accurate content, presenting the necessary information in a concise and coherent manner. The report should be free from any grammatical errors and should consist of articulate literary sentences. Therefore, the qualities of a good report include structure, sequence, and content.

If a nominal scale is used, it is permissible to calculate the mode. In statistics, the mode refers to the value that appears most frequently in a dataset. Unlike other statistics like mean and median, the mode can be calculated on nominal data because it only requires counting the frequency of each category. This makes it a suitable measure of central tendency for nominal variables, where the data is divided into distinct categories or groups without any inherent order or numerical value.

Content analysis is not a source for getting information for exploratory research because it is a method used to analyze and interpret existing data or content, such as documents, texts, or media, to identify patterns, themes, or trends. It does not involve collecting new data or information directly from individuals or sources. Exploratory research typically involves gathering primary data through methods like surveys, case studies, or pilot studies to gain initial insights and understanding of a research topic.

Validity is the process of gathering evidence supporting inference-based test scores. It refers to the extent to which a test measures what it is intended to measure. In other words, it assesses whether a test accurately measures the construct or concept it claims to measure. Validity is crucial in ensuring that test scores are meaningful and useful for making accurate inferences and decisions. It involves collecting evidence from various sources, such as content validity, criterion validity, and construct validity, to establish the validity of a test.

Convenience sampling is a non-probability sampling method because it involves selecting individuals who are readily available and convenient to participate in the study. This method does not provide an equal chance for all individuals in the population to be included in the sample, therefore it cannot be considered a probability sampling method like simple random sampling or stratified sampling. Snowball sampling, on the other hand, is a type of non-probability sampling where participants are recruited through referrals from existing participants.

The correct answer is "A tentative statement about the relationship." A hypothesis is a proposed explanation or statement that can be tested through research and experimentation. It is a tentative statement because it is not yet proven or confirmed, but it serves as the basis for conducting further investigation. A hypothesis helps to guide the research process and allows scientists to make predictions and draw conclusions based on the results of their experiments.

Research is not necessarily needed if the aim is to increase the extent of knowledge or make it more complex. Research can also be conducted to confirm or validate existing knowledge, to solve a specific problem, or to explore a new area of study. The aim of research can vary depending on the specific goals and objectives of the study.

Cluster sampling is a type of probability sampling where the population is divided into clusters or groups, and a random sample of these clusters is selected. Each cluster represents the characteristics of the entire population, and all individuals within the selected clusters are included in the sample. Therefore, cluster sampling is a probability sampling method.

Data refers to information that is collected or gathered through various methods such as observation, measurement, or research. It consists of raw facts or details that are used for analysis, interpretation, or decision-making. Observations are a form of data as they involve the systematic gathering and recording of information about a particular phenomenon or event. Therefore, the answer "Observations" is correct as it accurately represents one of the meanings of data.

Research questions and research objectives are different in terms of their origin. Research questions are formulated by the researcher themselves, while research objectives are often proposed by a supervisor or someone in a higher position. This suggests that research objectives are more likely to be influenced by external factors and may be more specific or focused compared to research questions.

Factor analysis is a statistical technique used to reduce the data size by identifying underlying factors that explain the patterns in the data. It helps in understanding the relationships among a large number of variables by grouping them into a smaller number of factors. This reduction in data size makes it easier to interpret and analyze the data. Therefore, factor analysis is an appropriate analysis technique for reducing the data size or compressing the data.

The run test is not a non-parametric test. Non-parametric tests do not make any assumptions about the underlying distribution of the data, while parametric tests do. The t-test, sign test, and chi-square test are all non-parametric tests commonly used to analyze data when the assumptions of parametric tests cannot be met. However, the run test is a parametric test that is used to test for randomness or independence in a sequence of observations.

The paired comparison method is a scaling technique that is used to rank or compare items based on their relative importance or preference. In this method, each item is compared to every other item in pairs, and a preference or ranking is assigned based on the comparison. This method is particularly useful for ordinal scale data, where items can be ranked in a specific order but the differences between the ranks may not be equal. Therefore, the correct answer is ordinal scale.

The correct answer is "None of the above." This is because the given options do not accurately define a null hypothesis. A null hypothesis is a statement that assumes there is no significant difference or relationship between variables being tested. It does not specifically refer to errors in sampling or the difference between two parametric means being zero. Therefore, none of the given options correctly explain a null hypothesis.