note slide 12 lectures : 18/5 + 20/5 + 23 /5 notes written

Foundations of Nursing Research, Sixth Edition

Rose Marie Nieswiadomy

Note slide 12

Lectures : 18/5 + 20/5 + 23 /5

Notes written in yellow color

Done by : nursing lijan

FOUNDATIONS OFNURSING RESEARCH

Sixth Edition

CHAPTER

Inferential Statistics

Learning Objective One

Recall The Two Purposes Of Inferential

Statistics

Broad Purposes of Inferential

Statistics

• Estimating population parameters from

sample data (after data collection)

Parameter = symbols

• Testing hypotheses (before data

collection)

Learning Objective Two

Discuss The Sampling Distribution Of The

Sampling Distribution of the Mean

• Approximates the normal curve

• Larger samples are more adequate

• :القانون تاعها

Standard Error of the Mean

• Standard deviation of the sampling

distribution of the mean

• Symbolized ˆ

– Smaller ˆ value means it is more likely the

sample is an accurate reflection of the

population mean.

.كل ما كانت نسبتها عالية كل ما كانت الثقة اقل

Learning Objective Three

Define Terms Used In Inferential Statistics

• Central limit theorem

– Tendency of sample values to be normally

distributed around population value

• Sampling distribution

– Theoretical frequency distribution based on

infinite samples

Confidence Interval

• CI

• Range thought to contain the population value

• Includes lower and upper limit, with specified degree of

probability

يعني لو اخذت عينة واخذتلهم الافريج فراح اشوف قديش راح يكون من لو اخذت

الافريج لمجتمع الدراسة كاملا

: القانون مع مثاللين موجودين بالصفحات القادمة

X pare= mean of sample

1.96= هي عدد ثابت لكنه بختلف حسب النسبة يلي اختارها الباحث

شايفين هون انه الثابت تغير لانه النسبة يلي اختارها الباحثتغيرت

Hypotheses

• Directional research hypothesis

– Predicts results of study

• Null hypothesis

– No difference exists between populations or

no correlation exists between variables in the

population.

– (H0)

– Subjected to statistical analysis

Hypotheses (cont’d)

• Null

– Rejected or retained

• Research

– Supported or not supported

Level of Significance

• Probability level of rejecting a null

hypothesis when it is true

• Symbolized by letter p and Greek letter

alpha (α)

• The most common probability level in

nursing research is p = .05.

• If result <p = reject H 0

• If result >p = fail to reject H 0

• Critical value

– Value in a theoretical distribution at which all

obtained sample values that are equal to or

beyond that point in the distribution are said to

be statistically significant

• Critical region or region of rejection—all

values beyond the critical value

Degrees of Freedom

• Symbolized (df)

• Concerns the number of values that are free to vary

• Interpretation of statistical tests dependent on the

degrees of freedom

• ف 10الى 1لو طلبنا تختارولنا رقم من : 1مثال

Degree of freedom =10

ف 10ارقام يكون مجموعهم 3لو طلبنا انه تختاروا : 2مثال

لانه بس اول رقمين بقدر اتحكم فيهم

*increase freedom = increase possibility

Learning Objective Four

Distinguish Between A One-tailed And A

Two-Tailed Test

One-Tailed Versus

Two-Tailed Tests

• One-tailed tests

– Appropriate for directional research

hypotheses

– Degree of difference or type of correlation

predicted

– Easier to reject the null hypothesis

One-Tailed Versus

Two-Tailed Tests

• Two-tailed tests

– Appropriate for nondirectional research

hypotheses

– Difference or correlation predicted, but the

degree is not indicated

– More difficult to reject the null hypothesis

Learning Objective Five

Describe Type I And Type II Errors

Errors

• Type I error

– Null hypothesis is actually true, but is rejected.

مماثل للالفا

• Type II error

– Null hypothesis is actually false and it is retained.

مماثل للبيتا

Power analysis الها علاقة مع

Acceptance when H0 is false

• Power analysis = 1-B

Ex : 100/100 – 20/100 = 80/100

لازم يكون في موازنة بين نوعي الخطا لانه زيادة تحكم بواحد

منهم بيزيد احتمالية حدوث الاخر لكن الباحثين عادة بهتموا

بانه يضبطوا النوع الاول اكثر من النوع الثاني واي واحد اخطر بيتحدد حسب الموقف

Learning Objective Six

Differentiate Between Parametric And

Nonparametric Statistical Tests

Parametric Tests

• Population parameters

Very powerful

Act to assumption of population

Parametric Tests

• Make assumptions about the population from which a

sample was drawn

– The level of measurement of the data is interval or

ratio.

– Data taken from populations that are normally

distributed on the variable that is being measured

– Data taken from populations that have equal

variances on the variable that is being measured

If violation occur to any assumption , use nonparametric

Nonparametric Tests

• Distribution-free statistics (more flexible )

– Makes no assumptions about the distribution

of the population

• May be used with nominal and ordinal

• Sample sizes may be small.

Learning Objective Seven

Discuss The Power Of A Statistical Test

Power of a Statistical Test

• Ability of the test to reject a null hypothesis

when it is false

• Dependent on the sample size and the

level of significance that is chosen

• The larger the sample size chosen, the

more power the statistical test has.

• The higher the level of significance

selected, the more power the statistical

test has.

Power of a Statistical Test (cont’d)

• A one-tailed test is more powerful than a

two-tailed test.

• If the assumptions of parametric tests are

met they are more powerful than

nonparametric tests.

Learning Objective Eight

List Criteria For Selecting A Statistical Test

The T Test

• Parametric test assumptions

• Compares the difference between mean

values of some variable in two groups

• Particularly useful for small sample sizes

• Interval or ratio data required

Analysis of Variance (ANOVA)

• Parametric statistical test

• Compares differences among more than

two means at one time

• Based on assumptions that data

– Are interval or ratio level

– Have been selected from populations that are

normally distributed

– Have equal variances on the variable that is

being measured

• Different between and in groups

• F=MSB(between group ) / MSW(within group )

Result :

F<1 = H0 true

MSB>MSW = rejection of H 0

F >1 = H0 false

Chi-Square (χ2)

• Nonparametric inferential technique

• Used for comparing nominal sets of data

• Used to inform any group is more

significant different between the groups

when using ANOVA

• Result put in contingency table

•In this example the variable is :

1-type of nurses

2- smoking status

• 50فبيطلع المربع الواحد 4بقسمهم على 200المجموع بقارن بيلي بتوقعه مع يلي تم مشاهدته •

Learning Objective Nine

Identify Statistical Tests Commonly

Reported In Nursing Studies

t Tests

• Parametric test that examines differences

between the means of two groups of

values

• Independent t test or unrelated samples t

– Test for samples with no association or

connection

t Tests (cont’d)

• Dependent t test or paired t test

– Used when scores or values are associated

or have some connection

Analysis of Variance (ANOVA)

• Examines the two types of variances in

data obtained

– “Mean square between” (MSB) groups

explore variation between the means of the

groups.

– “Mean square within” (MSW) groups examine

variation of individual scores within each of

the groups.

Chi-Square (χ2)

• Observed frequencies are compared to

expected frequencies.

• Null hypothesis is rejected if the observed

frequencies are quite different from the

expected frequencies at a specified level

of significance.

Advanced Statistical Tests

• Multiple regression

تحليل الانحدار / التنبؤ

Use to inform affect of independent of dependent variable

بدي اشوف ايش العوامل يلي بتزيد من حدوث الجلطة الدماغية : مثال

• Analysis of covariance (ANCOVA)

Statistical control

Freezing to one variable

No one affect other (dependent or outcome )

• Canonical correlation

• Multivariate analysis of variance (MANOVA)

اكثر من جروبين او اكثر من متغيرين بوقت واحد

• Meta-analysis

• Metasynthesis

Learning Objective Ten

Critique The Inferential Statistics Section

Of Research Reports

Critique of Research Reports

• Minimal understanding of inferential

statistics is sufficient.

(cont’d)

• Reader should

– Search the report for any inferential statistics

that were used in data analysis

– Determine if there is enough information to

make a decision about the appropriateness of

each test that was used

(cont’d)

• Reader should

– Be provided with the value of the statistical

test that was obtained, the degrees of

freedom, and the significance level that was

reached when each hypothesis was tested

– Be able to determine if each of the

researcher’s hypotheses was supported or

(cont’d)

• Every research report should clearly

present the results of hypothesis testing in

both the text of the report and in the

tables.

note slide 12 lectures : 18/5 + 20/5 + 23 /5 notes written

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