week 13: hypothesis testing type 1

Question 1

how to find a viable research topic

Answer 1

• what are your professional interests
• read, read, read
• use clinical experinces
• identify significant questions that were posed and investigated but not answered in a definite manner (there was a small sample size, uncontrolled variables, etc)

Question 2

ideas to build on existing research

Answer 2

• expand research to a new age group/diagnostic group
• apply different outcome measures
• address questions of social validity
• change the stimuli used
• change the setting of evaluation/treatment
• use up-to date instrumentation

Question 3

variables

Answer 3

phenomena you plan to observe

Question 4

independent variable

Answer 4

conditions or manipulations being studied

*what you change

Question 5

dependent variable

Answer 5

observations or measures obtained

*what you measure

Question 6

what will a well-written research question include

Answer 6

• includes all variables in an unambiguous way
• it should also include the intent of the research is it to
• –describe persons or circumstances
• -discover relationships between variables
• –identify differences between groups

Question 7

ways to formulate a research problem

Answer 7

• does not have to be in the form of a question or series of questions
• can be
• –formal hypothesis
• –statement of purpose
• –conditional if-then statement

Question 8

hypothesis

Answer 8

a formal statement of predicted outcome of the study

Question 9

Ho

Answer 9

null hypothesis

*based on the assumption that the results will yield no significant differences and/or relationships between variables

Question 10

Ha

Answer 10

alternative hypothesis

• statement of what the researchers expected to find when they conducted the study
• –directional is stated in a way in which the researchers had reason to believe a particular outcome would happen (such as a positive relationship between 2 variables)
• –non-direction is states in a way that a significant relationship is expected, but no indication of positive or negative effect

Question 11

two decision options for a hypothesis test

Answer 11

reject the null hypo meaning it is not valid or

fail to reject the bull hypo meaning it is valid

Question 12

statement of purpose

Answer 12

highly flexible

*explains the focus of nearly any type of research including descriptive, relational, and difference studies

Question 13

conditional if-then statements

Answer 13

if the findings turn out one way, the study supports a certain conclusion, but if they findings turn out another way, the study supports a different conclusion

Question 14

what are the criteria for well-informed research questions

Answer 14

• operationalize: give precise, specific details in the description of variables
• experimental treatments needs to be one that other professionals can carry out in a similar way
• –variables also need to be defined in a way that leads to valid and reliable conclusions

Question 15

what are the 6 steps of hypothesis testing

Answer 15

1) state the hypothesis
2) set a level of risk
3) choose a sample size
4) determine the critical value
5) compute the test statistic
6) accept or reject the null hypothesis

Question 16

type 1 error

Answer 16

reject Ho when it is true

Question 17

type 2 error

Answer 17

fail to reject Ho when it is false

Question 18

which type of hypothesis error is considered more serious

Answer 18

type 1 error

Question 19

what is the typical risk level set at for hypothesis testing

Answer 19

0.05 or less which means we are willing to risk a type 1 error 5/100 times

Question 20

what does the sample size determine for hypothesis testing

Answer 20

the probability distribution to be used

• z statistic requires large samples over 30
• t statistic is appropriate for smaller samples under 30 if certain assumptions are met

Question 21

the power of a test

Answer 21

the probability of rejecting the null hypothesis when it is false

• the larger the sample, the more powerful the test
• conclusions drawn from small sample sizes have a high probability of accepting the null hypothesis when it is false (type 2 error)

Question 22

the critical value

Answer 22

the cutoff, it is found be using tables of normal distributions, student’s t, etc
*it depends on the alternative hypothesis and the alpha level

Question 23

what are the two types of alternative hypothesis

Answer 23

• two-tailed meaning nondirectional
• –predicts a treatment effect but not specifying which is more effective
• onetailed or directional
• –hypo the treatment will be better than the other
• ***should generally be two tailed unless there is amazing evidence in favor or a specific direction

Question 24

computing the test statistic

Answer 24

a standard score such as a z or t statistic

Question 25

what factors does the choice of test statistic depend on

Answer 25

• the hypothesis
• the distribution of the target population
• the sample size
• other special characteristics of the sample

Question 26

non parametric statistics

Answer 26

distribution free
*a special class of inferential stats that depends on the ranking

Question 27

making a decision about the null hypothesis

Answer 27

• test statistic is compared to the critical value
• reject the Ho supports the Ha but does not prove it
• we never accept the Ha

Question 28

nominal level of measurement

Answer 28

*researchers assign participants and their responses to categories
*cannot be ranked or arranged in order
(numbers can be arbitrarily assigned like 1 for female and 2 for male)
*cannot compare the categories directly (one is not more or less than the other)
*effective nominal measures are exhaustive meaning every participant fits a category
, and mutually exclusive so only fit one category

Question 29

ordinal level of measurement

Answer 29

• rank ordered data (suggests inequality of data and lacks other numerical properties
• can be from high-low relative one another or use a rating scale to assign number (likert scale)
• gives info about the positions but not the difference between them
• can use labels instead of numbers

Question 30

interval level of measurement

Answer 30

which participants have higher or lower scores as well as how much they differ

• data has arithmetic properties of inequality and equal intervals
• examples: IQ and hearing threshold
• cannot compare scores directly because does not have a true zero

Question 31

ratio level of measurement

Answer 31

*all of the features of an interval, with a true zero (all arithmetic properties)
*3 characteristics
1)ability to arrange numbers on a continuum
20 ability to specify amount and difference between amounts
3) ability to identify an absolute zero relative to a characteristic
*often measures a physical attribute: actual height, intensity duration

Question 32

tables

Answer 32

visual representation of numerical data about participants or statistical findings of a study

Question 33

pie chart

Answer 33

appropriate for nominal, categorical measures

*useful for illustrating percentages or proportions of observations that fit into particular categories

Question 34

histograms vs bar graphs

Answer 34

useful for illustrating the magnitude or frequency of one or more variables

• histograms are for continuous data
• bar graphs are used with categorical data
• –often used for depicting group differences on measures like frequency counts, means, etc

Question 35

line graph

Answer 35

lke bar/column graphs, it is useful for illustrating the magnitude or frequency of one or more variables

• –particularly suitable for depicting several values in a series or for depicting a special kind of nonlinear relationship called an interaction
• –interaction occurs when two or more groups respond in unique ways to the experimental manipulations

Question 36

box and whiskers plot

Answer 36

displays the medial and upper quartile (75th %) and lower quartile (25%)
*easy way to show outliers

Question 37

scatter plot

Answer 37

appropriate for illustrating the relationship between two (sometimes three) continuous measures
—bivariate