Research Methods in MIS Hypothesis, Propositions, and Hypothesis Testing Dr. Deepak Khazanchi

Steps in The Research Process Review Research Ethics Hypothesis Development Vs. Research Questions Testing basics

Research Questions and Hypothesis Research Question: an interrogative statement asking about a conjectured relationship between two or more variables. Is the use of Internet Commerce positively related to sales volume in the furniture industry? Will attitudes towards computer usage be better for employees receiving training. Hypothesis: A declarative statement indicating a conjectured relationship between two or more variables which can be tested. Employees receiving training will have positive attitudes towards computer usage.

Do all research projects require Hypothesis testing? Definitely NOT! Many projects use research questions. Research questions vs. Hypotheses Hypotheses state predicted relationships between variables Research questions ask if a relationship exists Form of each is the same, except RQ is interrogative and the hypothesis is a statement. Research process is similar in almost all respects and most steps are identical Research projects can include both research questions and hypotheses.

Hypothesis vs. Research Question Use Hypothesis Testing if: Sufficient information about the variables and their relationship can be found in the review of related information to formulate an hypothesis. Samples, not populations, are used. You wish to use hypothesis testing procedures Use Research Questions if: There is insufficient information available to formulate hypothesis Population, not samples, are used. You are not willing to pre-specify a probability level at which the hypothesis will be considered supported. You do not wish to use hypothesis testing procedures.

The Role of the Hypothesis Guides the direction of the study Identifies facts that are relevant Suggests which form of research design is appropriate Provides a framework for organizing the conclusions that result

What is a Good Hypothesis? A good hypothesis should fulfill three conditions: Must be adequate for its purpose Must be testable Must be better than its rivals

Hypothesis Testing Process of making statistical inferences about population characteristics by using data obtained from samples of that population. Research Hypothesis: A declarative statement indicating conjectured relationship between variables which can be tested and which the researcher believes will be demonstrated after testing. Also called working hypothesis, alternate hypothesis, motivated hypothesis. Symbolically represented as H1: E = N

Null vs. Research Hypothesis Research hypothesis is not the one tested. It is the Null hypothesis that is tested. Null Hypothesis is a declarative statement indicating that the relationship specified in the research hypothesis will not exist. Symbol: H0: E > N State the research hypothesis, not he null hypothesis (despite what you may have learned in statistics class). The research hypothesis is the one in which you are interested. The null hypothesis is always the one which will tested.

Directional/Nondirectional Hypothesis Directional: An hypothesis in which the direction of the outcome is predicted rather than predicting inequality only. E.g., A will be better than B, X will be less than Y, A will be positively related to B. NonDirectional: An hypothesis in which the direction of the outcome is not predicted and the states relationship between the variables is one of inequality. E.g., A will be unequal to B, A will be related to B.

Probability Probability: The relative frequency of an event occurring, usually reported as a percentage or fraction. The probability of drawing an ace of hearts from a deck of 52 cards is 1/52 or 0.19. Probabilities range from 0 to 1 with 0 indicating no chance of occurrence an d1 indicating 100% chance or certainty that an event will occur. A probability of 0.05 means that an event should occur 5 times out of every t100 times the same situation takes place.

Significance Level Researchers use probability to specify the risk (probability) of coming to the wrong conclusions in hypothesis testing. They specify a probability of being wrong such as 0.05 or 0.01 which means that they want to draw the correct conclusion 95 or 99 times out of 100. The 0.05 or 0.01 value is called significance level. It is the probability, expressed in a percentage, of rejecting the null hypothesis when it is true and should not have been rejected. Also known as TYPE I Error, ALPHA LEVEL, ALPHA ERROR Always selected a priori

TYPE I and TYPE II Errors Type I Error (Alpha, ): Probability of REJECTING THE NULL HYPOTHESIS WHEN IT IS TRUE. TYPE II Error (Beta, ): Probability of FAILING TO REJECT (ACCEPTING) THE NULL HYPOTHESIS WHEN IT IS FALSE. Ideal goal: Reduce both types of errors. Not feasible, since smaller the TYPE I error means larger the TYPE II error will be. Generally decision maker must assess which error is more important.

POWER Power is the probability, expressed in a percentage, of rejecting the null hypothesis when it is false and should have been rejected; a correct decision. Power is computed as 1.00-beta (Type II error).

POSSIBLE RESULTS OF HYPOTHESIS TESTING FAIL TO REJECT (ACCEPT) H0 REJECT H0 (ACCEPT HA) H0 IS TRUE CORRECT DECISION (Prob. of accepting H0 when it is true = 1-alpha) TYPE I ERROR (Prob. of rejecting H0 when it is true = alpha) H0 IS FALSE TYPE II ERROR (Prob. of accepting H0 when it is false =beta) (Prob. of rejecting H0 when it is false = power = 1-beta)

Reducing TYPE II Error (Beta) Increase the significance level (alpha) Increases the risk of rejecting the null, when it is true (Type I error) Increase the size of the sample Can be costly and time consuming. Lowering Type II error increases the power of the statistical test you use.