MIDTERM ARM

Causal Research

• One variable influences another, either directly or indirectly, in a causal relationship.
• An illustration of a causal relationship is when a brick falls on a toe, causing pain and an involuntary scream.
• Causal relationships can be unidirectional (A influences B, but not vice versa) or bidirectional (each variable influences the other).
• Exercise and body weight have a bidirectional relationship: exercise influences body weight, and body weight influences exercise.

Strengths of Causal Research

• It identifies the causes behind processes, helping researchers fix problems or optimize outcomes.
• It provides benefits of replication if needed.
• Causal research identifies the impacts of changing processes and existing methods.
• Subjects are selected systematically, increasing internal validity.

Limitations of Causal Research

• Administering causal research is difficult due to the challenge of controlling extraneous variables.
• It is one of the most expensive research methods to conduct, sometimes costing millions.
• Causal research outcomes might reveal plans to competitors, allowing them to enter the market first.
• Confirmation requires additional research beyond causal research alone.

Correlational Research

• A primary goal is to determine if two or more variables covary and to establish the relationships observed.
• It involves observing variables "as is" without manipulation.
• An example includes determining the relationship between pretest anxiety and test performance by having students rate their anxiety and then comparing it to test scores.
• Variable comparisons occur across preformed groups based on participant characteristics like political affiliation or age.
• Group membership relies on participant choice, not researcher manipulation; party affiliation is measurable, but it cannot be changed.
• GPA can be predicted with student SAT scores, this allows determining the value between variables.
• The predictor variable is used to predict, while the criterion variable is being predicted.
• Causation between variables remains an open question.

Strengths of Correlational Research

• Researchers can collect more data than with experiments.
• Results are more applicable to everyday life because the research occurs outside the lab.
• It opens avenues for further scholarly research.
• It provides a good start to investigating phenomena and relationships and helps narrow findings or determine causation.

Limitations of Correlational Research

• It only uncovers relationships without conclusive reasoning for why they exist.
• It does not reveal which variable influences the other.
• A third, unknown variable could cause both correlating variables and reasons assumed be true.

Experimental Research Characteristics

• Manipulation of independent variables and control of extraneous variables form its defining characteristics.
• Values for independent variables are chosen by the experimenter.
• The set values are the levels of the independent variable.
• Sleep deprivation's effect on memory forms an example, participants are assigned to groups with 0, 24, or 48 hours of sleep deprivation.
• Exposing participants to variable levels is necessary to manipulate variables.
• Conditions linked to each level create the experiment's treatments.
• The independent variable is manipulated by exposing different groups to treatments or exposing all participants to treatments in sequence.
• Researchers hope to show that changes in an independent variable's level cause behavior changes.
• Value observed in designs determines the dependent variable.
• In a causal relationship, the level of the independent variable determines the value to some extent.

Control Over Extraneous Variables

• Experimental research involves regulating variables not of interest
• For instance, determining how anxiety therapy affects anxious students and degree can be determined.
• Uncontrolled extraneous variables lead to uncontrolled changes and hard to detect effects.
• Uncontrolled variability may produce chance differences, this makes it appear as though the effects came from the independent variable.
• Extraneous variables are controlled by holding them constant or randomizing effects across treatments.

Strengths of Experimental Approach

• Establish and describe causal relationships.
• The correlational approach can only indicate changes in one variable correspond with changes in another.

Limitations of Experimental Approach

• The method cannot be used to manipulate hypothesized variables.
• Studies on personality relies on correlational approaches to identify possible causal relationships.
• Exposing people to severe conditions to show the causes for personality disorder is unethical.
• Revealing the effect entails tight control over extraneous factors.
• The ability to apply findings to situations is reduced.
• Ability to establish causal relationships is decreased.

Market Analysis Overview

• Assessing quantitative and qualitative data creates a clear picture of tangible and intangible industry factors.
• Four parts consist of a market analysis.

Industry Overview

• Knowing the state of the industry and projected growth includes industry metrics such as size or trends.
• Mobile phone sales determine if mobile phones demand is growing.
• Dining out trends determines if restaurant markets are shrinking as consumers take advantage of grocery services.

Target Market

• The target market looks at who the customers are. Includes:
• Market size involves estimates of potential customers for a product.
• Demographics refers to describing the potential customer's education or income.
• Location indicates where to find your customers.
• Psychographics describe customer likes and dislikes.
• Behaviors are explanations of how customers shop for and buy products.
• Trends indicate what customer behavior is changing.

Competition

• Competition involves reporting a competitor's strength and weakness.
• Includes direct, indirect, and competitive advantages.
• Also includes barriers to entry.

Pricing and Forecast

• Pricing and forecasting includes the price to determine a business’s position in the market.
• The forecast helps determine the share of the market wanting to be acquired.

Financial Analysis

• Financial analysis helps to evaluate businesses, budgets, etc.
• The analysis helps determine performance and suitability.
• Analysis are used to analyze whether a company is solvent.
• It is used to evaluate business trends, while a financial analyst examines statements.
• A common way to analyze financial data is to calculate financial data.

Working Capital Ratio

• Dividing current assets by current liabilities to assess its liquidity.
• Liquidity helps determine how easily a company can cover short term investments.

Quick Ratio

• Also called the acid test, this ratio subtracts inventories, and can divide them into liabilities.
• This assists to show current liabilities that are covered by cash.

Earnings Per Share

• EPS measures income that is earned on shares.
• The analyst divides net income by the average.

Price-Earnings Ratio

• Called P/E, and shows future earning investments on investors.
• The company helps determine their stock price.

Debt-Equity Ratio

• This can reduce margins behind what it owes.
• This can also jack up fixed charges.

Return on Equity

• Calculated by taking net earnings (after-tax), subtracting dividends, then dividing into common equity.
• Common share holders determine how profitable the company is.

Competitor Analysis

• Those who compete for the customer is who the analyst needs to place competitors.
• List their marketing structure/tactics. Relevant questions:
• What are their strategies.
• What media is purchased for advertising.
• How can they be a threat.

Questionnaire Design

• A questionnaire is formalized and a pre-specified set of questions that help reflect objective investigation.
• Consumer attitudes needs to be measured.

Principles of Wording

Relating to factors:

• The nature of questions being asked.
• Beliefs and attitudes measured.
• How questions are worded.
• The language depends on the educational level.
• What form the questions asked are.

Open-ended vs Closed Questions

• Open questions allows different answers.
• Closed questions ask respondents to make the right choice among a set.
• Negativity questions help to engage respondents.
• Double-barreled should be avoided, and double or more questions should be asked instead.

Types of Questions

• Leading phrased questions must be avoided.
• Biased questions is also a type of bias to avoid.
• Easy questions helps the respondent.
• Private data should be private to ensure anonymity of the respondent.
• It is necessary to add questions in the questionnaire.
• Appropriate scales help collect and measure the data, and that it is appropriate to test.
• Wording and measurements are needed for attractive questionnaires.

Good Introduction

• Survey needed for disclosure conveys the identity of the researcher.
• Questions that provide guidance.
• Questionnaires look proper to the respondents.
• Personal data is well-organized.
• Open-ended comments are encouraged at the end.
• Should end on a courteous note.

Descriptive Statistics

• Statistic measures that yield information.

The Mode

• Most frequent occurring part of the number.
• Ordering them the right way helps arrange it.
• The mode is the appropriate measure for different needs.
• The median value helps provide the middle position.
• To find out how many odd observations, is to find the sixth position.

Quantitative Ranks

• Banks must collect quantitative data that is able to be ranked.
• A median is unable to do this.

The Arithmetic and Sample Mean

• The is averaging the numbers to count.
• For a measure of location, the data is divided into numbers to see which the best value is.

Percentiles

• Measures divide a data set and describe falling location, while also using the percentile of interest.

Quartiles

• The Q1 quartile means it is equal to the 25th percentile.
• The Q2 and Q3 are located as the 50th.
• These can have different values.

Measures of Variability

• Measures yield useful information that help analyze the values better.
• Central tendency is often presented as average value.
• More values can be represented by data sets.

The Range

• Maximum and minimum values.
• Help measure data by calculating and measuring the variability.
• Has to be calculate with data from the extremes.

Interquartile Range

• Has the range between the data by computing the values.
• Should consider data towards the middle.

Variance and Standard Deviation

• Squared deviations is what the average is.
• To find them use a chart and list deviations.

Sampling

• Sampling can be done to get gathering and data.
• Data that are gathered is conclusions about process.
• Taking a sample offers the following advantages.
• Can be saved on money.
• More study can be broadened.
• Will not be destroyed from the process.
• Population acess is impossible.

Random vs Non-Random Sampling

• Random sampling and how it can be applied to method.

Simple Random Sampling

• Sampling is from based on.
• One disadvantage is there are less data being selected.

Stratified Sampling

• Sampling based on concepts.
• Is homogenous and will divide into strata.

Clustering

• The cluster in dividing over the same areas.

Systematic

• There is little error reduction, that is why data is needed in a systematic way.

Multi-Stage

• There is a certain amount to follow.
• First may be used to provide for what to do.
• Second it ensures what is being distributed to categories.
• Last it is needed to sample a cluster, and reduce collection costs.

Non-Random Sampling

• Methods that are selected from convenience.

Quota Sampling

• The classes are that the public will use.

Convenience sampling

• Data that needed to be selected.

Judgement Sampling

• Is determined the reseacher is there.

Snowball Sampling

• This was first introduced by respondents.

Sampling Distribution

• It involves that you need is the process.
• The size on sampling in addition to the normal shape.
• Values that will help be added.
• The means in sampling for what to add.
• Deviation that can help get better results