×
Get Full Access to Carleton University - NEUR 2001 - Study Guide - Final
Get Full Access to Carleton University - NEUR 2001 - Study Guide - Final

×

CARLETON UNIVERSITY / OTHER / NEUR 2001 / what are the Main effects and interactions?

# what are the Main effects and interactions? Description

##### Description: These notes cover both the lecture content (good copy of summary notes taken during the lecture) and textbook notes for the; - Factorial research design (Topic 8) - Descriptive and Inferential statistics (Topic 9) - Literature Search (Topic 10) - Communicating your research (Topic 11) - Responsible scientific conduct (Topic 12) This is the information that will be on the final exam for NEUR2001. I JUST ADDED MY FULL NOTES
190 Pages 5 Views 11 Unlocks
Reviews

Factorial Design – Introduction

## what are the Main effects and interactions?

Main ideas:

- Introduction to the factorial design

o A research study involving two or more factors. This is a beneficial research strategy as it increases external validity by making the experimental conditions more akin to  real life.

- Main effects and interactions

o In the factorial design, each factor produces a main effect. The factorial design is  used to analyze if there is an interaction between factors

o This occurs when two factors, acting together, produce mean differences that are  not explained by the main effects of the two factors

- Identifying interactions

o Main effect in one factor bot no main effect in the other with no interaction  o Main effects in all factors’ bot no interaction

o No main effects for any factor with an interaction

- Types of factorial design

o Between-subject design

## what are the Types of factorial design?

o Within-subject design

o Mixed design

- How to display data in the factorial design

o Tables and graphs

o The factorial matrix

 How to analyze the matrix to determine the interaction

- Hypothesis tests and analyzing the results

o The P-value and what it means

o Determining if the interaction is due to chance

Key terms:

- Factor: in an experiment, an independent variable is often called a factor,  especially in an experiment that includes two or more independent variables  • Independent variables of an experiment

• Handling a mouse

- Level

• Values of the independent variable

• Handling for 0, 10, 20min, etc

• Drug dose at 10, 20, 30mg etc If you want to learn more check out What Does Government Do?
Don't forget about the age old question of Storytelling ​​performances ​​consisted ​​of ​​both what?

- Condition (treatment)

• How is the group treated in the experiment

## How to display data in the factorial design?

• What is the subject experiencing by each of those groups

• A combination of the factor and levels

• Handled for 10min and given 10mg drug

• Not handled and given 30mg drug

- Factorial design: a research design that includes two or more factors  - Main effect: the mean differences among the levels of one factor are the main  effect of that factor

o A two factor study has two main effects; one for each of the two factors

- Interaction between factors: when the effects of one factor depend on the  different levels of a second factor, then there is an interaction between factors  o Occurs whenever two factors, acting together, produce mean differences  that are not explained by the main effects of the two factors.

o If the main effect for either factor applies equally across all levels of the  second factor, then the two factors are independent and there is no  interaction.

- Mixed design: a factorial study that combines two different research designs  o A common example is a factorial study with one between-subjects factor and one within-subjects factor If you want to learn more check out What is the code of event “Pre-Admit a Patient”?

- Combined strategy study uses two different research strategies in the same  factorial design. One factor is a true independent variable (experimental strategy)  and one factor is a quasi-independent variable (nonexperimental pr quasi experimental strategy)

- Analyzing factorial studies

o Main effects

o Interactions

- Types of factorial design

o Pure factorial design

o Between-subjects groups Don't forget about the age old question of What is Newton's Third Law of Motion (the law of action and reaction)?

o Within-subjects measures

- Mixed factorial design

Factorial Design

• Factorial experiments are experiments where multiple independent variables are  manipulated

• In the real world, effects have multiple causes and thus the factorial de • Advantage: The factorial design creates a more realistic situation that can be obtained by  examining a single factor in isolation.

• Because behaviour is influences by a variety of factors usually acting together, the  factorial design enhances external validity because it can examine two or more  factors simultaneously.

• Manipulating multiple independent variables to determine whether or not they have an impact on your outcome.  We also discuss several other topics like Theory that states Indigenous people may have came to America through a giant plane of land connecting Asia and North America.

• Having multiple independent variables allows them to interact with one another to  make the effective one variable stronger or weaker.

• Examining multiple factors increases external validity

• Because you’re taking all possible scenarios into account.

• By being able to assess the multiple determinants of behaviour, you are able to  better approximates what the behaviour would be like in the real world

Factorial design: terminology

- Uses one numeral for each factor/independent variable,

- The value of the numeral indicates the number of levels for that independent  variable (factor)

- Each level of independent variable is paired with the level of every other  independent variable

o 2 X 2 factorial design

 Two independent variables, A and B

 A and B (independent variables) Both have Two levels

for each independent variable

o Four conditions

 4 X 2 factorial design We also discuss several other topics like What drives global atmospheric circulation?

 Two independent variables

 Four levels for independent variable A, two levels for

INDEPENDENT variable b

- Eight conditions

o 5 X 3 factorial design

 Independent variables: two

independent variables (two numerals)

 # of levels:

 1st = 5 levels

 2nd = 3 levels

 Fifteen total conditions

o 2X3X2 factorial design

 Three independent variables

 1st = 2 levels

 2nd = 3 levels

 3rd = 2 levels

 12 total conditions

Simplest 2x2 factorial design

- To identify an interaction in a factorial study, you must compare the mean  differences between cells with the mean differences predicated from the main  effects.

- If there is no interaction,

o The combination of the two main effects completely explains the mean  differences between cells.

- The main effects from a two-factor design reveal how each of the factors affects  behaviour.

- And the interaction reveals how the two factors operating together can affect  behaviour.

o The existence of the interaction indicates that the combined effects of the  two factors acting together is not the same as the effect of one factor acting  alone.

- When the data from a two-factor study are organized in a matrix,  o the mean differences between the rows describe the main effect for one  factor

o and the mean differences between the columns describe the effect for the  second factor.

- The extra mean differences that exist between cells in the matrix  o Are differences that are not explained by the overall main effects  o Describe the interaction and represent the unique information that is  obtained by combining the two factors in a single study.

- Example: Effect of diet and exercise on body weight

o Independent variable: Diet (chow vs ketogenic diet)

o Independent variable: Exercise (housed with wheel vs no wheel) o Dependent variable: Body weight

How to plot factorial matrix

- No effect 2 Main

effects

- Y axis= dependant variable x axis= independent variable  o When there are two independent variables you choose one factor and you  put that on the x=axis and the other independent variable becomes part of  the legend

o Whatever is going across factorial matric, put that across the graph  o Factor B becomes part of your legend

 You would put a filled circle as no wheel and a empty circle as the with  wheel condition

- In no effect graph

o All the groups weigh the same

o So you plot both at the same body weight and connect the dots. o You should have two data points with filled dot and two data points with non filled circle

 There is no effect because all the mice weighed the same

- In the 2 main effect graph

o There is a change in body weight of the mice so you see that both the mice  in groups with and without wheel lost weight when they did the ketogenic  diet

How to approach a factorial matrix

- Identify the main effects, if any

o Calculate the average of each

row/column

o If the row/column averages are different

 main effect of that factorIf column

averages are different  main effect of

column factor

- Determine if there is an interaction

o On the graph, are the lines parallel or not

 If not parallel, there is an

interaction

 If parallel, there is no interaction

o Also a mathematical way to determine if

there is an interaction

- Discuss the main effects

o If there is an interaction, then you cant

describe the effect of A without taking

the effect of B into consideration

Main effect

- The main effects reflect the results that would be obtained is each factor were  examined in its own separate experiment.

• The effect of one independent variable

• Is there an overall effect of manipulating the independent variable? • Mean differences among the levels of one factor

• Calculate the average of row or column

• The main effect for one factor is obtained by averaging all the different levels  of the second factor. Because each main effect is an average, it may not  accurately represent any of the individual effects that were used to compute the  average.

Mathematical way to observe effect of diet

1. Take the average of the rows and columns

2. If mean average values are not the same on row = effect

3. If mean average of the columns are not the same = effect

interpreting main effects and interactions

Interaction

- When the effects of one factor depend on the different levels of a second factor then there is an interaction between factors.

o When the size of factor A depends on the effect of factor B such that as  factor B increases/ decreases there is a change in factor A then there is an  interaction.

o when the results are graphed, the existence of nonparallel lines (lines that  converge or cross) is an indication of an interaction between the two factors  - Is two factors are independent so that the effect of one is not influenced by the  other then there is no interaction.

o When the size of the effect of one factor does not depend on the effect of  the other = factor A does not depend on factor B and there is no interaction  o When the data is plotted on a graph there is a noticeable pattern such that  as the line on the graph for factor A increase/ decrease factor B changes  proportionally with it. The effect is two parallel lines on a graph. And  there is no interaction

- Determines how a combination of factors work together to affect behavior o Occurs when one factor has a direct influence on the effect of the other  If Factor A and Factor B are independent, they have no interaction  Factor A can exaggerate/minimize the effects of Factor B

- Compare the mean differences in any individual row with the mean differences  in other rows

o No interaction: Size and direction of the differences in each row is the same  as other rows

o Evidence of interaction: Differences change from one row to another - Nonparallel lines indicate an interaction between the two factors o Not parallel = interaction

o Parallel = no interaction

- Note: still need statistical test to determine if difference is significant - The mean differences are simply descriptive and you still need to evaluate  by a statistical hypothesis test before they can be considered significant.

Lines are not parallel

- The interaction shows the effect of

exercise

o Body weight decreased when

fed a ketogenic diet. Body

weight further decreased with

given exercise.

- Because there is an interaction,

cannot describe the effect of

exercise without taking into account

the diet

- You only need one of the interactions

to be different to have an

interaction

- Lines are not parallel = interaction

- The main effect does not accuratelt

describe the results. It would be incorrect to conclude that there is no relationship  between the two factors.

1 main effect, no interaction

- You compare the values row to row and column to

column

- The lines are parallel so there is no interaction

Discuss the main effects: interpreting results

- The effects of exercise (Factor A) on body weight (DV)

depend on the diet consumed (Factor B)

o Main effect of diet and exercise

o Regardless of exercise, body weight decreases

when eating a ketogenic diet (compared to

chow). However, when allowed to exercise,

body weight further decreased when eating a

ketogenic diet

Two main effects, no interactions

More comples: 2x3 factorial design

Arousal level

Recall

Hig

h

Mediu

m

Lo

w

Simple

40

50

40

Comple

x

10

25

20

- What are the main effects

o To determine the number of main effects possible, look at the number of  factors

 Every factor can have an effect on your variable. So there is one main  effect per factor

 2x2 = 2 main effects

 2x3 = 2 independent variables + 3 levels = 2 main effects

- How many interactions

o 2-way interaction = ninteraction between 2 factors

o 3-way interaction = interaction between 3 factors

- The number of interactions depends on the number of factors

o 2x2 design

 One 2-way interaction

o 2x2x2 design (variables A, B, and C)

 Three 2-way interactions

 AB, AC, BC interact together

 And one three way interaction

 ABC interact together

o 3x3x3x2 (variables A, B, C, and D

 Four possible main effects

 2-way interactions

 AB, AC, AD, BC, BD, CD

 3-way interactions

 ABC, ABD, BCD, ACD

 4-way interaction

 ABCD

Independence of Main effects and interactions

- The two factor study allows researchers to evaluate three separate sets of mean  differences

o The mean differences from the main effect and factor A

o The mean differences from the main effect and factor B

o The mean differences from the interaction between the factors.  - The three sets of mean differences are separate and completely independent. Thus  it is possible for the results from a two-factor study to show any possible  combination of main effects and interactions.

Mean differences between levels of factor A but no mean  differences for factor B

- To identify the main effect for factor A, notice the overall mean for the top row is 10  points higher than the overall mean for the bottom row. This difference is the main  effect for factor A.

- To evaluate the mean effect for factor B, notice that both columns have exactly the  same overall mean, indicating no difference between levels of factor B, hence no B effect.

Data with an A effect and a B effect but no interaction  - The A effect is indicated by the 10-point mean difference between rows  - The B effect is indicated by the 20 point mean difference between columns.  - The fact that the 10-point A effect is constant within each column indicates no  interaction

o Because it doesn’t change as the B factor changes

Data showing no main effect for either factor, but an interaction  - There is no mean difference between rows = no A effect

- And no mean difference between columns = no B effect

- However, within each row and column there are mean differences  o The extra mean differences within the rows and columns cannot be  explained by the overall main effects (individual effects of factor A and factor B) and therefore indicate an interaction

Types of Factorial Designs

Pure factorial

- Disadvantages of a pure factorial design

o You need more participants/ subjects because separate subjects are needed  in each of the two groups (control and experimental)

o Example: in a 2x4 design, each participant must be measured in eight  different treatment conditions. The large number of different treatments can  be time consuming, and increases participant attrition

o Having each participant undergo a long series of treatment conditions can  increase the potential for testing effects (fatigue or practice effects) and  make it more difficult to counterbalance the design to control for order  effects

Types of factorial designs

• Between-subjects

• Independent groups

• Factorial experiments between different groups of people

• Within-subjects

• Repeated measures

• Factorial experiments using different manipulations on the same  individuals

• A single group of individuals participates in all of the separate

treatment conditions

• Mixed design

• Combination of between- and within-subject design

• One factor is between-subjects and another factor is within-subjects

Pure factorial within-subject design

- Between-groups design

o participants are randomly assigned to each cell of the design

o best suited for situations in which individual differences are relatively large  and there is little reason to expect order effects to be large and disruptive.  - Disadvantages

o Individual differences can become confounding

variables, increases variance of scores

o Avoid order and sequence effects

Within-group Pure factorial design

- Within-groups design – same individuals participate in ALL conditions - Cons

o Number of different treatment conditions can be high and time-consuming  (participant attrition, fatigue/practice)

o Each participant/subject have to go through both coniditons (experimental  and control)

- Pros

o Fewer individuals needed

 Reduces problems related to individual differences (choose this if you  know the individual differences will be very large)

- Example: Wilson et al., 1994: Effect of marijuana on cognitive performance o 1 group of n = 10  10 subjects total

o Main effect of task difficulty and marijuana

o There is an interaction between marijuana and task perfromanse   As reaction time increases when performing a hard task, this

performance can be greater when smoking something with THC

- Order becomes something you have to consider

Mixed factorial design (no longer pure factorial design) - Combines both between- and within-subjects design

- Effect of manipulations on two groups of individuals

- Often used in before-after situations between two groups

- Pros

o Control for unwanted individual differences, while investigating specific  individual differences

 4 groups down to two groups

 Limit time effects of within subject design

- Cons

o Limits ability to make causal statements about the relationship between  variables; limit internal validity

EXAMPLE:

- Effect of caffeine to offset debilitating effects of alcohol

o Randomize in placebo or caffeine group

o Each individual gets water and alcohol, compare effect of placebo/caffeine in  water/alcohol

- Increases external validity (Generalizations)

o Variation of single independent variable is unusual in real-life situations o Allows you to better generalize to a real effect

 Because situaitons in real life are very rarely due to only one cause  So by taking multiple variables into account you are taking into

consideration that multiple things can effect one behaviour

- Theories with two or more independent variables can only be tested via complex  factorial designs

o Exercise, diet

- Greater experimental control

o Testing multiple variables in one experiment reduces random variation that  arise when comparing different variables in different experiments

 Example: Some mice are better wheel runners than others, if you had  totally different groups of mice, will see more within-group variability

o Having less groups with larger number of participants you minimize the  variablility between groups and makes the statistics of your analysis a bit  more robust

- Too many variables can result in:

o Huge experiments and requirement for multiple conditions

 And potentially a lot of participants/subjects

o Interactions that are not easily interpreted because combinations may not  make sense.

 Some conditions tested may not arise naturally

 Interaction between day length and temperature on work

productivity

o Long/cold days

o Long/hot days

o Short/cold days

o Short/hot days

 You don’t have short long days because hot days are

in the summer where the days are longer.

Descriptive Statistics

Towards the end of the research process.

- Descriptive statistics: are methods that help researchers organize,

summarize and simplify the results obtained from research studies.  - Observations are inherently variable

o We want to have a set of criteria to ensure our measurements aren’t due to

chance.

o When observations are variable it is partly due to measurement error o Statistics allow accurate conclusions to be drawn from data

o Despite this variability statistics draw meaning from the data

- Allow quantification of observations

o Quantifying the observations = increase by 1% or decrease by 1% - Allow observations to be summarized

o Statistics allow you to discuss data/observations as a group

Types of Statistics

- Descriptive statistics  used to describe data

o Organize data

o Summarize data

- Inferential statistics  allows you to make inferences from data

o Use results from a sample to make conclusions about a population o Determine if there is statistical significance within data set

Examples of descriptive statistics

- Organize data

o Frequency distribution

- Summarize data

o Measures of central tendency

 Mean, median, mode

- Measures of dispersion

o Standard deviation, variance, range

- Measures of relationship

o Pearson, Spearman

Frequency distribution

- One method of simplifying and organizing a set of scores by grouping scores into an organized display that shows the entire set of scores.

o Consists of a tabulation of the number of individuals in

each category on the scale of measurement.

- Displays two sets of information

o The set of categories that make up the scale of

measurement

o The number of individuals with scores in each category.  - Can often look like a bar graph but it allows you to

quantify all your scores by grouping them into a display  o Eligible for all scales of measurement

o Preliminary method of statistical analysis, rarely shown in

publications

- Frequency distribution table

Da

ys

Freque

ncy

0

833

1

824

2

423

3

162

4

126

5

201

6

175

7

274

8

172

9

138

10

144

11

60

o Example: the number of times students in NEUR2001 visit CULearn before a

midterm

o Days= number of days before a midterm (x-axis/ first column)

o Frequency = number of visits (y-axis)

First column

Second column

Scale of measurement

Or simply lists the set of categories into  which individuals have been assigned

Frequency or the number of individuals in  each category

- The same information presented on a frequency distribution table can be

presented on a graph.

o Frequency distribution graph

- Frequency distributions, especially graphs, can be an effective method for

presenting information about a set of scores.

- The distribution shows whether the scores are clustered together or spread out  across the scale.

o Gives you a concrete representation of all the individual scores as well as the

appearance of the entire set of data

o You can see whether the scores are clusters or spread out.

o You can see is the scores are generally high or low (where the distribution is

centered)

o And if there are any extreme scores that are very different from the rest of the

group

-

-

- X-axis  Scale of measurement

- Y-axis  Frequency

- Frequency distribution plot can be misconstrued as a bar graph  - Primary differences

o In histogram the bars are touching in a bar graph the bars are not touching  o There is not relationship between 100 and 200 in the bargraph  o In the histogram there is a relationship you are trying to see between

number of days and visits

- Histogram

o Shows a bar above each score so that the height of the bar indicates the  frequency of occurance for that particular score

- Bar graph

o When categories on the cale of measurement are not numerical values (and  are thus qualitative values) the frequency distribution.is presented as a bar  graph.

o A bar graph is like a histogram except that a space is left between adjacent

bars.

o The height of each bar indicates the frequency associates with that particular category.

Mode

- How we identify the score with the greatest frequency

o Which one is the most common

o Applies to all scales of measurement

- Not commonly used

o If you wanted to get the most common score you would use this - Examples

o 5, 7, 7, 8, 8, 8, 10: Mode = 8

o Because 8 happened the most times (3 times)

- When the sample contains a few extreme (outliers) scores the mean tends to be  distorted by the extreme values. This causes a large distribution.

Median

- What is the score that divides the distribution in half

o (number right in the middle of your entire data

set)

o Midway point of your distribution

- Cannot be used for nominal scale

- Can be useful if there are outliers

- Score that divides the distribution in half

o Data set: 5 6 4 9 1 1 3

 Arrange them in order: 1 1 3 4 5 6 9

 Middle number = median: 4

- Cannot be used for nominal scale

- Can be useful if there are outliers

- Score that divides the distribution in half

o If there were two numbers in the middle, take the average

o 5 6 4 9 1 1 3 2  1 1 2 3 4 5 6 9  3.5

-

Mean (average)

- Add all scores and divide the sum by the number of individuals o Amount each individual receives if the scores were divided equally - Cannot be applied to nominal or ordinal scale

o In order to add values together, the values must be

meaningful

o Easily understood and computed

o Useful for inferential statistics

o You’re not dealing with each individual number

o You can determine what the trend is/ what you expect to

see

o Does not produce a representative value when there are outliers o You lose the nuance in how the data set is changing over time

Recap: Scales of

measurement

- Nominal – used to name or

label a series of values

- Ordinal – give information on

the order of choices

o Put in rank order

- Interval – gives order of

values and difference between

values

- Ratio – gives order and

calculate ratios

Measures of dispersion

- This measures the variability

- How much the scores in a distribution differ from the measure of central tendency - The “range” of scores in the dataset

Range

- Difference between the largest and smallest

score

o From lowest to highest score and

everything in between but you don’t

know the individual scores in between

from just looking at the range. You

would only know that all scores fall

between the range

- Only based on two scores

- But not informative because it ignores most of

the data

Measure of variance

- Variability describes the spread of scores in a distribution. When variability is

small, it means that scores are all clustered together

o Large variability means that there are big differences between individuals

and the scores are spread across a wide range of values.

- Better measurements of dispersion would include information from every score

- The means are identical for these two plots but the spread of data is different - If data is summarized only via the mean score, one would lose a lot of information  about the differences between them

-

Standard deviation

- Standard deviation uses the mean of the distribution as a reference point and

measures variability by measuring the distance between each score and the mean. o When the scores are clusters close to the mean The standard deviation

is small

o When the scores are scattered widely arounf the mean the standard

deviation is large

- Standard deviation (SD) Indicates how much everyone within a sample deviates

from the sample mean

- Measures the average distance from the

mean

o Value is derived from the variance

- Variance = sum of the squares of the

deviations from the mean, divided by the

number of scores minus 1

- Variance measures the variability by

computing the average squared distance from

the mean

- Standard deviation = square root of variance

In summary, the calculation of varience and standard deviation is  a series of steps

1. For each score, measure the distance away from the mean. This distance is  often called a deviation.

2. Square each of the distances and compute the average of the squared  distances. This is variance.

3. Note: the average squared distance for a sample is computed by dividing the  sum of the squared distances by n – 1, where n is the number of scores in the  sample.

o The value of n – 1 is called degrees of freedom, or df.

o Finding the average by dividing by n – 1 (instead of n) produces a  variance for the sample that is an accurate and unbiased representation  of the population variance.

4. Because the variance measures the average squared distance from the mean,  simply take the square root to obtain the standard deviation.

5. Thus, variance and standard deviation are directly related by a  squaring or square root operation

Standard error of the mean (SEM)

- Standard deviation  spread of values within a sample

- Provides a measure of how much difference is reasonable to expect between a

sample and its population

- Measures how accurately the sample reflects the population

o Sample mean varies from sample to sample

o Estimate how much sample means vary from the distribution

o “How certain am I that the sample mean accurately estimates the population

mean?”

 You need to find the standard error of the mean

o Standard deviation = variance within the sample

o Standard error= how well you are reflecting the population

- The expected value for the standard deviation of several sample means, and is

estimated from a single sample

- Depends on standard deviation and sample size

- Sample size increases, standard error decreases

- By contrast, standard deviation stays the approximately the same even when

sample size increases

- When values are deviating far from the mean then the standard deviation is

going to be large

o because standard variation describes the variability of individual values it is

a measurement of variability.

o It is not describing how accurate your sample mean is it is just  measuring how variable your data set is from the mean within your  sample.

o Standard variation increases as variance increases

- Standard error

o Can be altered as sample size changes because of the calculation  o It depends on standard deviation and sample size.

o As your sample size increases, standard error will decrease

getting more and more accurate of a representation

 To be able to get a sample as close as possible to population you have  to sample more people/ animals

In practice

- Typically use standard deviation when trying to assess your sample or identify

outliers

o Outliers – falls outside of the normal distribution

o Values that fall two standard deviations away from mean it is considered to

be an outlier

o Which is usually excluded from the data set.

- Then use standard error to report final mean

o Because you’re trying to estimate if your data set can represent the

population

- Make sure to declare what is used in your results

o Declare whether or not you’re using the calculation for standard deviation  o Use it if you’re trying to only look at your sample and not trying to reflect the

experimental results to the population

o If you are trying to do this then use standard error

Inferential statistics

- Are methods that use the results obtained (limited information) from

samples to help make generalizations about populations

- Allow researchers to infer/generalize observations to the larger population - Goal of all research, regardless of the research strategy, is to find a significant

effect

- The goal of inferential statistics is to use limited information from samples as

the basis for reaching general conclusions.

- Want your results to be so extreme that it is unlikely the results are due

to chance alone

o Allow researchers to say how likely it is that the observed difference between groups is due to random chance

Sampling error

- The goal if inferential statistics involves making a generalization or an inference  from limited information to a general conclusion.

- This leads to sampling error

o A sample does not provide a perfectly accurate picture of its population.  There is some discrepancy, or error between the information available and  the true situation that exists in the general population

Example: Is my coin biased

- Is it more likely to land on heads or tails and are the results just due to chance  o Coin 1  80 heads, 20 tails more convincing results than 8:2

o Coin 2  53 heads, 47 tails

o Coin 3  8 heads, 2 tails

o Coin 4  60 heads, 40 tails

- Where do you draw the line in deciding if the coin is biased or not? - Coin 1 vs coin 3

o 80 vs. 20 is more convincing than 8 vs 2 even though the proportion of

scores that deviatr from expectations is the same

o So what is the threshold?

- Coin 2 vs coin 3

o 53:47 vs 8:2

o In both cases the difference is only 6

o The number of scores that deviate is the same but you might say that coin 3  is more biased because the numbers are lower

o But actually the difference between the two is fairly similar

- Where do you draw the line in deciding if the coin is biased or not? - The arbitrary line between sufficient and not sufficient is the threshold of

statistical significance  P value

o 95 heads, 5 tails: the coin is probably biased

o 55 heads, 45 tails: the coin is probably not biased

o If the probability is small its probably not due to chance

- Inferential statistics – puts a value on that probability that the result is not

biased

o the probability that the result is due to chance

- Get a probability that the coin is not biased this probability is the P value

P value

- Range between 0 (impossible that it is due to chance) and 1 (certain it is due

to chance)

o 70 heads and 30 tails, p = 0.001

 There is very low probability (0.1%) that the coin is NOT biased  Therefore, the coin is biased

- Refer to the probability that there is NO differences between the groups - By convention, this boundary is commonly p = 0.05

- If it is definitely due to chance then the effect that you’re seeing is not real

P value in Inferential statistics

- Allow researchers to infer/generalize observations to the larger population

(whether or not something was due to chance)

o Allow researchers to say how likely it is that the observed difference between

groups is due to random chance

- “Probability that the difference is due to chance is 5%”

- 5% chance that the null hypothesis is correct  p value

o If you only have 5% chance that the effect is due to chance then the effect is

not due to chance.

o 5% chance that the null hypothesis to be correct

- Hence unlikely that the difference is due to chance, thus conclude the treatment

effect is real

- Low p value  allows you to make inferences (generalizations) from the sample to

the population

o The p value allows you to reject the null hypothesis

- Inferential statistics involves hypothesis testing

Hypothesis testing

- A systematic/ statistical procedure that uses sample data to evaluate the credibility

of a hypothesis about a population

- Attempts to distinguish between two explanations for the sample data 1. That the patterns in the data represent systematic relationships among  variables in the population

2. That the patterns int the data were produced by random variation from  chance or sampling error

- determines whether the sample data provide convincing evidence to

support the original research hypothesis

o Procedure that allows you to assess whether your sample data is giving you

convincing evidence to support your original hypothesis

- State a null hypothesis – statement about the population parameter being

examined and always says there is no effect, no change, or no relationship.  o Specifies what the population parameters should be if nothing

happened.

o For example: Exercise as a new therapy for treating depression

 Begins by assuming that you’re wrong and then you conduct a study to

 Null hypothesis: Exercise does not reduce depression symptoms

- Conduct a study to see if this assumption can be rejected o If shoot down the assumption, then you have support for the alternative…  o The alternative that the new therapy does reduce depression symptoms

P-test (a test statistic)

- A P-value-test is a summary value that measures the degree to which the sample

data are in accordance with the null hypothesis.

o A large value for the test = a large discrepancy between the sample statistic  and the parameter specified by the null hypothesis and leads to rejecting the null hypothesis.

- P = 1.00 means the result is 100% due to chance ∴ no effect o Null hypothesis states that the results of the research study represent

nothing more than chance.

 If this is true then the actual results and the chance results should be  very similar and the test-statistic (P Value) will have a value near 1.00  or 100%

 A P value near 100% means there is no effect, that any patterns in the

sample is just due to chance

- The goal of a hypothesis test is to rule out chance as a plausible explanation for the  results. To do this researchers must determine which results are reasonable to

expect just by chance and which are unlikely to happen by chance alone. You do  this by determining the alpha level (P-value less than 0.05)

o the level of significance for a hypothesis test is the maximum probability that the research result was obtained simply by chance. A P value of under 0.05  means that the test demands that there is a less than 5% probability that the results are caused only by chance

- Goal of the research study is to show that a treatment has a real effect  and that this effect is not due to chance and therefore you want a small P value

o P = 0.05 means 5% chance the result is due to chance

- Examples of null hypothesis:

o When comparing two treatments

 Null hypothesis = There is no difference between the treatments  P=1.00 = 100% chance there is no difference

 Therefore, there is no difference

 P=0.0001 = 0.01% chance there is no difference

 Therefore there is a difference

o If you’re P value is tiny then there is a difference between the treatments

(control versus experimental group)

- Examples of a null hypothesis

o Examining a correlation

 There is no relationship between the two variables and the correlation  for the relationship is zero

P value

- Range between 0 (impossible) and 1 (certain)

o 70 heads and 30 tails, p = 0.001

 There is very low probability (0.1%) that the coin is NOT biased

 Therefore the coin is biased

- Refer to the probability that there is NO differences between the groups - By convention, this boundary is commonly p = 0.05

o If p < 0.05, reject the null hypothesis (reject that there is no difference) o If p > 0.05, fail to reject null hypothesis (cannot reject that there is no

difference)

 If you cant reject that theres a null hypothesis then you cant reject that there is no difference

Test statistics

- There are many other tests out there that can be used

What is the logic behind test statistics

Null hypothesis is correct…

- Null hypothesis: There is no difference between treatments (blue is right behind

green so you cant see it and there is no difference)

- P = 1.00 ∴ 100% chance that there is no difference

o Therefore there is no difference

Reject null hypothesis

- Reject null hypothesis if there is a very low chance that the means are the same - P < 0.05 ∴ less than 5% chance that there is no difference

-  there is a difference

Two ways to increase the difference between groups  - Goal: We want to see if there is a difference between population mean vs sample

mean

- You want the curves to be far apart in two ways

1. Increasing the difference between the means

a. By doing this you can shift the curves

2. Decrease the standard deviation between the means

a. The means are still the same distance apart but the standard deviations are  smaller

b. When they are smaller we can segregate the two different groups of data  that we have (clearly see the difference between group a and group b)

Take home message

- Increase the difference between the means

- Decrease the variance/standard deviation in each sample

- Decrease standard error of the mean to generalize sample data to population (by  increasing sample size)

Literature search – topic 10

Primary and secondary sources

- Primary source

o Firsthand report of observations or research results written by the individuals who  actually conducted the research and made the observations

o Used to obtain complete and accurate information.

o Examples:

 Empirical journal articles

 Theses and dissertations

 Conference presentations of research results

- Secondary source

o A description or summary of another persons work

o Written by someone who did not participate in the research or observations being  discussed.

o Provide concise summaries of past research, are always incomplete.The author has  selected only bits and pieces of the original study – the selected parts may be taken out of context and re-shaped to fit a theme different from the original authors.

o Used to gain an overview and identify specific primary sources for more detailed  reading. Are only a good starting point for a literature search

o Examples:

 Books and textbooks in which the author describes and summarizes past  research

 Review articles or meta-analyses

 Introductory section of research reports in which previous research is

presented as a foundation for the current study

 Newspaper and magazine articles that report on previous research

- Meta-analyses

o

- The primary distinction between the two

o First hand versus second hand research results

- Examples:

- A journal article may not be a primary source. Instead, the article may be a review of other work (as in a review article or meta-analysis), a theoretical article that attempts to explain  or establish relationships between several previous studies, or a historical summary of the  research in a specific area. None of these is a primary source because none is a firsthand

report of research results.

- A book or book chapter can be a primary source. Occasionally, an individual or a group of  researchers will publish an edited volume that presents a series of interrelated research  studies. Each chapter is written by the individual(s) who actually conducted the research  and is, there- fore, a primary source.

- A journal article may be a firsthand report of research results, yet sections of the article  actually may be secondary sources. Specifically, most research reports begin with an  introductory section that reviews current research in the area and forms the foundation of  the study being reported. This review of current research is secondary because the  authors describe research conducted by others. Remember, to qualify as a primary source, the authors must describe their own research studies and results.

-

Conducting a literature search

- Start the literature search with only a general idea for your research topic  o Your purpose is to narrow down your general idea to a specific research question.  o And then to find all the published information necessary to document and support  that question

o Subject words: a list of correct subject words used to identify and describe the  variables in the study and the characteristics of the participants.

 Researchers often develop a specific set of terms to describe a topic area.   It is easier to locate related research articles if you use correct terms

o Author names: a small group of individual researchers are commonly responsible for the majority of the work being done in a specific area.

 Write down the names of researchers you repeatedly encounter as they are  typically the leading researchers in the field.

Purpose of a literature search

- The goal in conducting a literature search is to find a set of published research reports that define the current state of knowledge in an area and to identify an unanswered question.  o A gap in the knowledge base that your study will attempt to fill

- The purpose of the literature review is to provide the elements needed for an introduction  to your own research study.

- You need to find a set of research articles that can be organized into a logical argument  supporting and justifying your research purpose.

Using online databases

- The typical database contains millions of publications all cross-referenced by subject words and author names.

- So entering a single word or author name into a data base provides you with all of the  records and list of publications that are related to your subject or leading researcher. - Some data bases are full text

o Meaning that each record is complete, word-for-word copies of the original  publication

o Contain fewer publications than other databases

o The value of a full-text database is that whenever you find a research article you  would like to read, the entire article is immediately available

- Other databases provide only brief summaries of the publication.

o The summary includes a title, the name of the journal, a list of subject words that  describe the publication, and an abstract.

o Holds more items than a full-text data base’

- Limit the publication type to all journals or peer-reviewer journals

o This eliminates books and book chapters

o Eliminates dissertation abstracts

- Limit the methodology to empirical study

o This focuses your search on research reports and eliminate essays, discussions, and  general review articles

- If your research is focused on a specific age group or population group refine these to limit to these areas

Beginning a literature search

- Reviewing titles of journal articles that come up on a data base is the first level of  screening journal articles

o Most articles can be discarded at this stage

- When a title is relevant to your topic

o Obtain the detailed record, which contains additional information about the  publication including;

 Title

 Author

 Keywords

 Abstract

 Subjects

- The second level of screening is reading the abstract

o By reading the abstract, you get an idea about whether or not the article is related  to your topic

- Then check the list of key terms to determine whether there are specific terms that could  be used to improve your search.

Process of conducting a literature search

Ste

p

Explanation

1

Title

Decide if a title is relevant to your topic

2

Abstract

Read to determine if the article is interesting

3

Go to the

journal

Go to the journal that the article was published in. once you  find it, skim it and look specifically at the introduction and  discussion.

4

article

carefully

If after skimming the article, it is still relevant then read in  depth

Introduction

discusses previous research that forms the foundation for the  current research. This helps determine if the article eill be  helpful in the development of your research idea

Method

Information on participants and subjects – description of the  sample that participated in the study.

Procedures – description of how the study was conducted,  including a description of the questionnaires and equipment  used

results

Findings, statistical analysis,

Discussion

gives you ideas for future studies. You can use one of the ideas  mentioned

Reference

Lists all the publications that were cited in the text. These  publications can lead to a new subject term or author names for your search

5

Use the

references

Use the references from the aeticles you have found to expand  your literature search. Find the relevant articles and add  them to what you hace.

Searching forward

- l

Taking notes

- each time you find an article that is relevant to your research question, read the article  carefully and take notes or make a copy of the article for future reference. - If you are taking notes

o Get a compelte reference for the article.

 Author name

 Year of publication

 Title

 Source of the article

 DOI

o If it is a print journal

 Name of the journal

 Volume number

 Page numbers

- The DOI

o If the article is from an electronic source it will have a DOI

o Digital object identifier.

o This is important to take note of

Data presentation

- You need to remember that a picture is worth 1000 words and indeed this is true for  science and the picture can include illustrations and or graphs in science and it  helps us summarize data in the very visual way usually when we see a picture we  immediately capture the scene and we can immediately understand what the  author is trying to say and that can be more effective than if we describe the figure in words so first we need to decide if the data should be displayed in a table or a  graph

Data display mediums

- In deciding whether to use a table or a graph you want to consider multiple factors  1. Think about whether the independent variable and dependant variable are qualitative or  quantitative.

2. Think about how many data points are being shown and

a. are you trying to make comparisons between data points.

b. Is the individual value more important than the over all trend?

c. Or are you trying to emphasize the overall trend in a table that can be useful if  there are very few values to report.

3. You can use a table if both the independent variables are qualitative

4. In a graph it is useful is you are trying to show trends overtime and also when youre trying to visualise differences by comparing between groups.

a. This is because you get an illustrative/ visual representation of your data  b. Example: if looking at whether group A versus group B has a difference. You can  look at the height of the bars

Table

- You can use the table to show comparison

- There are instances where you can use a table if theres a lot of values and both those  values are qualitative.

- Example: in the table, it is only showing two values and you shouldn’t provide a graph in  this case because the data can be easily interpreted from the table.

o this table shows the number of cells that are counted using this condition on these  levels these two different modes types

- you can use a table to show numbers

o especially when there is a small number of values.

- You can use a table to show two qualitative pieces of information

o Example: the table below is showing the different brain regions and the expression  of these two items or proteins and the plus value shows the relative expression.

Table formatting

- The left is typically reserved for an

independent variable

- The dependant variables are in the

right columns.

Example:

- In this case the independent variable

is the grey region and the dependant

variable is the expression level

- This table type is typically seen in

clinical papers to describe participant

characteristics.

o What are the baselin

characteristics of your

participants

o What sample youre reporting

o At the top they’ve indicated:

 Mean plus or mean plus ot minus standard deviation.

o They show the range (smallest and largest value)

o and the characteristic here would be your all of your variables like age weight BMI  body fat blood pressure cholesterol levels etc

Graph

- Use graphs to illustrate trends

o Changes overtime if something is increasing or decreasing across different cohorts.

o You can use this to visualize differences between comparisons and it might contain the same information  as a graph but you can cpture the relationship by a line graph.

- You can then take the graph and put the data points and put them into a table.

- but when it's in a graph form you can clearly visualize what the differences are

Choosing what graphs to use

- bar graph versus a line graph

- bar graphs and line graphs are useful when showing trends or comparisons.  - Bar graph

o Best for discrete variables.

o If you only want to show at time point 15 weeks old what is the difference in body  weights

o You will only get one time point

- line graph

o best for using or showing or expressing continuous variables where you look at  changes overtime

o you can also use a line graph to make predictions and when you're trying to predict  a value within the graph (interpolation)

o allows you to multiple time point

- interpolation when you're trying to predict value outside the data

o example:

o the body weight of the mouse at every single week so starting from when they were five weeks old

o Interpolation by looking at the values in the graph:

 saying well what is the weight of the mouse when it say at 7.5 seven half  weeks

 so you're interpolating within data points

o extrapolating would be: youre going outside the values shown in the graph to  make inferences about what would happen according to the trend shown   what would be the weight of the mouse when it's at 20 weeks old so you're  going outside the parameters of the graph to make predictions

- Bar graph example 2: differences between cholesterol levels in three different groups  o So you can clearly see what the differences between each group is at one time point - show individual values if possible (the circles within the bars) o circles represent the individual animals that comprise the mean which showed  which represented by the bar.

o The height of the bar represents the mean value.

 The mean cholesterol level of the wildtype mouse

o The little white circles represent the individual mouse that comprise the  mean.

- The mean is captured by taking the average of these eight data points and.  o by taking the average of these eight data points and same is true for the Gray  squares mouse and that can be illustrated here as well so in this case the dark line  going across is the

o The dark line going across typically represents the standard deviation - Example of choosing between a bar and a line graph

o the researchers studied the effects of interrupted why on the excitability of a cell  researcher applies NPY to brain slice for five minutes and records action potentials  before during and after NPY application the results showed that NPY inhibits action  potential firing by 90%

o and you're seeing a picture of action potential firing in the application of a drug   You can see the membrane become more negative and then as the drug goes off you see the action potentials recovery or returning

 you can report this in the table where the

 left column is variable in the right column is dependent variable   you are measuring the action potential frequency at baseline which is  typically before drugs replied

 and then during the NPY applications

how should the researcher show the maximal change in action  potential firing over time?

- If you’re looking at things overtime you should use the line graph

How should the researcher show the maximal change in action  potential firing ?

- If you’re looking at one point and looking at the maximum change then the bar  graph should be used.

Anatomy of a table

- How to identify the independent and dependant variable given a table  - left column you will have the independent variable and on the right column you  have the dependent variable

o when you have to express a dependent variable to make sure you have units - Example: brain regions on the left column and the right columns express as much  as possible the dependent variable and the dependent variable here is expressed  using plus is the number of classes that the more the number of classes then the  higher the expression of cells within these great regions how to label

Anatomy of a graph

- it's important to know what all the components of the axes or at least  indicate the components on the graph

o Set up labels

o Use a legend

- Y-axis and sometimes X axis

o but X axis can be represented without the X axis title because it's understood that this is the condition treatments that are in.

o also include the units in brackets.

o a line graph also includes units express in brackets and it might indicate  seconds minutes days

- Your x-axis should have ticks

o You should also try to indicate the number of subjects or participants that  comprise the mean.

- your graph should also include statistical representation, social  representation this would be like inferential statistics looking at  comparisons between control and experimental

o you're making inferences as to whether or not these differences are  statistically significant

- sometimes you can show subject numbers in brackets (for example inside the bar)

- but in line graphs you have to express your legend

o the legend should tell you what the circles represent and there should be  numbers in brackets which represent the number of participants in each of  the data points

- use Red green blue RGB format or CMYK format and wherever possible  try not to use the colours there is one special consideration I want you to  pay attention to and that is know that

Use of colour in graphs

- traditional papers will stick to very few colour choices some modern papers have  colourful pictures and figures.

o You should try and avoid adding too much colour because then the reader  has a hard time tracking what the representation is.

o Sometimes different subjects have different guidelines for colours to use in  the formatting.

Colour – special consideration for illustrations.

- You should use magenta-green instead of red-green so that someone who is colour  blind can easily understand what you are showing in the figure.

o 10% of the population is colour blind so there are some tricks you can use to  make your science more inclusive.

- You can also use arrow heads to point to the cells you are referring to.  - Example:

o Arrowhead is pointing to a red cell and it's trying to say that this cell  expresses both the MCR one protein as well as dynorphin is indicated in this  figure legend

Why we communicate research

- If Scientists only focus on their own solutions and research, they might not be able  to advance as must as if they collaborated with other researchers. together they  would better be able to come up with a more effective or more creative solution, so we use research to encourage new collaborations

o Collaborations is the process where multiple groups and individuals work  together to come up with solutions and collaborations to help you gain new  ideas and it also shared the burden of tackling large research projects  together so you might not need to know everything.

- you want to collaborate and communicate your research because you might  be able to tap into other sources of funding

Posters

- poster presentations are the most common form of presentation  - the society for neuroscience because there's a lot of people attend a  conference and theres a team in your research area and you get to talk to  them directly one-on-one about how they did something and discuss what's  going on in your field and what are some of the latest findings

- highly interactive event

- in the paper poster it presents an overview of your recent research and it  typically contains unpublished research

Dynamic posters

- an electronic poster which is presented on a screen

o allow you to incorporate videos and slides interactive charts

- in neuroscience there's a lot of videos and simultaneous recordings of neural  activity with behavior and it's hard to capture that simultaneous coded coincident  activity on a 2 dimensional paper so that's where the dynamic poster comes in

Presentation

- Lecture: 1 hour

- Symposium: mini or nano symposium. You would have 20 minutes or 10 minute  time limit.

- data blitz or like a lightning talk and this could be one to three minutes and  you have to condense your 5-year-long research and summarize it into 3 minutes  o well-developed presentation

o One slide, one minute

o Get people interested in your research

o Get to the bottom line, fast

o General tip – no more than 2 graphs

Things to consider when writing a manuscript

- This is the final part of the writing communication format.  - Consider when writing a manuscript

o The purpose for writing a manuscript is to communicate research in the  written form. Typically involving writing some kind of scientific paper  - There are different types of papers in types of scientific article

o primary article, secondary article, and a systematic review article - Primary source

o Full research report containing all details needed to replicate study o traditional type of Journal article that has an introduction methods results  discussion etc. it's a full research approach that contains all the details that  you need to replicate that study and also the detailed findings

o Written by individuals who conducted the research

o what's happening within that project in depth and that primary article is  written by the individuals who completed that research

- Secondary source

o Summarizes information from primary sources

o Descriptions of other people’s work- like a review paper

 Textbooks

 Third person format

 Systematic review and summarizes information from different

primary sources so it might describe other primary literature.

(just gives you facts)

 now these are facts right so textbooks describe facts but all those facts are summarized from hundreds of literature that you know his you  know that has built up overtime to deliver that textbook knowledge  Review papers

 Provides recent overview of research field

 Subjective upon the opinion/interpretation of the author

 Is assembled by the author. Takes many different article and

summarized whats happening in the field. The review article

often summarizes (what this person thinks is going on in the

field)

o Systematic review

 Combines findings of all research articles that explored the same  question to see if the result is statistically significant

 Attempts to resolve differences between papers to get to the truth  systematic review combines findings from all the research articles that  have the same research question and sees if the result is statistically  significant

o example: to take all of those 10 papers and does some kind of statistical  analysis to see what the actual mean or overall effect of high fat diet is as  described in these 10 papers, so the individual paper is going to look at  within each authors data set what is the mean weight gain

o takes all the variability found between journal articles and does a statistical  analysis to determine whether or not there is actually an overall effect based on the findings from all these primary articles.

- the systematic review is different from the review paper  o because it's not just summarizing in words the trends that are appearing  within the data

o the systematic review is going to take specific data sets and tries to crunch  the numbers within those specific data sets and tell you what the overall  effect of this diet (example) across these 10 papers is actually that have  been shown so it attempts to resolve differences between papers

Impact factor

- neuroscience Journal has a rigorous peer review process

- a low impact factor doesn't mean that that paper is not very good it can still be  extremely good except for some reason that Journal doesn't have a high impact  factor

- the number of citations that an article gets will have an impact on the impact  factor of the Journal

Choosing an appropriate scientific journal

- Journal should be peer reviewed

o that your paper has been reviewed or scrutinize if you will by your peers or  by your supervisors peers

o helps you improve it. Critically analyze it for flaws. Looks to see whether or  not your rationale leads from one point to another.

o a good peer reviewer actually helps you improve your paper that helps you  improve the analysis your thought process

- the Journal you submit your paper two should be consistent with the aims and scope of the your paper

- select a journal that is appropriate for the topic of your research report. Most journals  focus on a few special topics. A journal’s website describes what kinds of manuscripts are  appropriate for that journal

o the journal should be consistent with the aims and scopes of your paper – found on  the journals website

o the aims and scope is where the Journal tells you what kind of paper is going  to accept

- but then it also tells you what it does not accept

o it says it does not accept the short communication because the expect your paper  to be a full length investigation

 short communications are where researchers try to publish a short  article on a hot topic (new up and coming type things)

 Journal of neurology is not interested in that it doesn't care about hot  topics

- Consult the journal’s Instructions to Authors for specific submission requirements.

Factors that go into choosing an appropriate journal article - Impact factor – average number of citations an article in that journal received in  the past two years

o impact factor is a number like it's 3.4 or 4.56 it gives you a sense of  prestigious a Journal is

o in essence the impact factor is the average number of citations that an  article in the Journal receives in the past two years

o If Journal of Neuroscience had an impact factor of 6 in 2015

- Debatable

o High impact factor may indicate important, cutting-edge work

o A high impact factor suggests that the higher number of times the papers  within the journal is being cited. the more top papers being cited in that  journal, the more impact it will have on that field.

o Consider: Proportion of review articles, scope of the journal

- Low impact factor may not necessarily indicate unimportant work

o Specialty journals naturally get less citations

- Citations – a reference to another scientific article

o Identifies the author(s) and year of publication of the specific fact/idea  mentioned in the research report

o What counts as a citation?

- Beware of predatory journals

The peer review process

- When a manuscript is received by a journal editor, the editor informs the author of its  receipt and distributes copies of the manuscript to reviewers.

- The reviewers are selected on the basis of their expertise in the research area of your  manuscript.

- Reviewers provide the editor with an evaluation of the manuscript, but, ultimately, the  editor makes the decision to accept it, reject it, or request its revision.

o they might criticize your analytical methods or it might request better  referencing or citations of your paper etc.

- the editor reviews these comments

o and then it will send back to you the comments from the peer reviewers  o then you have an opportunity to take the peer reviewers comments and  revise and resubmit your article for a second round of peer review

- if they still don't like it or they still have criticism

o then the cycle continues

- Then the reviewers accept it

o and at that point there's no more edits that need to be made and then you  get a published Journal article

- so this whole process is the peer review process how long does a peer review  process take well it depends on the Journal some journals have a strict policy for  making sure that public papers are reviewed in a timely manner

Trend of a preprint

- Research paper that is shared before peer-review

- any kind of like archive is a preprint server and the preprints can either be a  primary or secondary article

o Often has a DOI

o Can be primary or secondary article

- Why a preprint?

- Benefits of a preprint

o Credit

 You can get credit right away

 you're going to submit something at the peer review and it's taking so  long and you're in competition with another group to get this paper out first then you're kind of worried that as it's going through peer review  maybe that reviewer is like going to steal your ideas or something then you want you can submit this as art by archive so that you can claim  your steak in the pot so to speak can get credit for doing it first

o Feedback

 you can get feedback

o Visibility

 You can get more visibility so more people are seeing your results early - Concerns

o Its more likely to get accepted because the peer review process might be  influenced.

o it's not to review so if you have like some famous researcher puts in the  preprint to archive and it's not peer review but everyone just assumes that  because it's really famous person that it must be good then that's the danger

APA format of a research proposal

A research proposal

- A research proposal is a written report presenting the plan and underlying ra- tionale of  a future research study. A proposal includes a review of the relevant background literature, an explanation of how the proposed study is related to other knowledge in the area, a  description of how the planned research will be conducted, and a description of the  possible results.

Abstract

- there is usually a word limit the purpose of the abstract is to provide a one  paragraph summary of your whole paper it should include background information  it should tell you how you did somehow get the research it should have key results  and have the major conclusions

- you have to be able to summarize your paper into just a few sentences for your  abstract

- the Journal neuroscience definitely has a weight limit 650 words and here you want to provide enough background information to clarify

o why your research was completed

o you want to point out the objectives of the study

o tell me what you know about the field

o what is not known about the field

o then what did you actually test in your research study

o tell me briefly what you found (key results)

o this is also where you will find the hypothesis of the research

o methodology- inclide details such as the species studies, gender, general  methods

o

Introduction

- May have word limit

- Indicate the objectives of the study

- why you are doing this research

- Provide enough background information to clarify why the research was completed - State the hypotheses tested

Methodology

• Describe how the research study is conducted in depth with many details. • There is never a word limit on the methodologies because is important for other  reader or another researcher to replicate your findings.

• But you want to be as brief as possible but not leave out any details • Include animals used, strain, species, gender, etc.

• Tell the reader im using ___________

• have a statement indicating all of your protocols have been approved by  your animal care committee or the research ethics board at your  University

• reagents section

• software used

• equipment used

• identify where the company makes it and where is was produced  • important because example:

• you can buy substance P from a wide number of companies but strangely  enough sometimes in pharmacology experiments different companies  produce different priorities of the compound and for whatever reason

• not all compounds for every company have the same efficacy and  effectiveness

• example: don't change the source of my peptides especially within the  same research project that could be a confounding factor

• saying the company and the city and location is important because  you know you might have the company called back in that's in the United  States but then you might also have it in Germany and if they're being  produced in two different places it might also have a different quality  • the last part is the statistics/ data analysis section

• indicate that the software that you're using

• any types of statistical tests you use whether it's the task I swear test  • whether or not you were doing a factorial design

• indicate what your P level your P value indicates so is your statistical  significance indicating that P less than 0.35

• talk about mean, errors and standard deviation

• indicate whether or not your data is presented as mean standard  deviation or mean and error

Results

- then within the results of your paper you want to clearly and succinctly described  our findings and here is where you provide numerical data

Discussion

- the discussion will have a word limit

1. (first paragraph) summarize your findings

2. Provide an interpretation of what your findings mean

a. Connect with the literature

b. Use a lot of citations here so that you can explain whether or not your  findings make sense with what is known in the literature

c. compare and contrast

3. explain if there are any limitations with your work

4. end your discussion with a statement of why your work is important and how  your work contributed to the field

References

- another section within the Journal of manuscript you submit and is usually  attached at the end of the paper

- sometimes they want you to indicate or order it by alphabetical order - So what you need to know that is what goes into the reference the reference itself  contains

o the authors

o the year

o the title of the manuscript

o the journals name

o the volume of the Journal

o and the page range so within that Journal volume where you going to find it  so I want to explain to you what volume

- so if you were given a reference you should be able to identify that the first words  or authors last names the middle and first name initial the year that the  manuscript was published the title of the article and then the abbreviation for the  Journal name

Legends

- the figure of legend is a text paragraph that describes the figure so it's a  description of the figure and not an interpretation of the figure so it tells you what  the figure is but not what the figure means

- figure titles are really important because they should be able to stand alone so I  should be able to look at a figure without reading the paper and understand what  your results are

- also have statistical representation and tell me what these stars mean so in this  case these stars are in abbreviation for a P value that's less than 0.0001 cheers  another example of a figure in a figure legend so it's a theoretical one

Section

Summary

explanation

Information included

Abstract

Concise summary of the paper  that focuses on what was done and what was found.

Typically written last after the

paper is done

150-250-word limit

Subjects- how many and relevant characteristics  Research method and procedures

A report of the results

Statement about the conclusions or implications

Introductio n

Begins with a general introduction  to the topic of the paper.

Provides background

knowledge (theory) behind the  research study. Identifies the

problem and why it is

important.

The introduction presents a logical development of the research question, including a review of the relevant  background literature, a statement of the research  question or hypothesis, and a brief description of the  methods used to answer the question or test the  hypothesis

Method

Provides a relatively detailed

description of exactly how the

variables were defined and measured  and how the research study was  conducted. Other researchers should  be able to read your method section  and obtain enough information to  determine whether your research  strategy adequately addresses the  question you hope to answer. It also  allows other researchers to duplicate  all of the essential elements of your  research study.

Divided into sections: 1)

participants 2)procedure

Describe the sample that participated in the study.  The number of subjects,

For animals: describe their genus, species, and strain,  the supplier, how they were housed and handled, and  their specific characteristics including weight, age,  sex.

For humans: 1)eligibility, and exclusion criteria, 2) the  settings and location in which data were collected  3)any payments made, 4) ethical standards, 5) any  methods used to divide or assign participants into  groups or conditions, how many individuals in each  condition. 6) a description of instruments given to  participants 7) research design 8) any experimental  manipulation or intervention 9) apparatus or materials used

Results

Provides a complete and unbiased  reporting of the findings just with  facts no discussion of the findings. –  provides a summary of the data and  the statistical analysis

Tables or figures are included here

A statement of the primary outcome of the study. 2)  the basic descriptive statistics (the means and  standard deviation) 3) inferential statistics (results of  the hypothesis test/ P-value) 4)the measures of effect  size.

Discussion

offer interpretation, evaluation, and  discussion of the implications of your  findings

If your results support your original  hypothesis, it is now possible to test  the boundaries of your findings by  extending the research to new  environments or different populations. If the research results do not support  your hypothesis, then more research  is needed to find out why.

1)restate hypothesis and relate it to existing literature  2)restate major results and indicate if they support or  fail to support your primary hypothesis 3) relate your  results to the work of others explaining how your  outcome fits into the existing structure of knowledge  4)identify any limitations of the research especially  factors that affect the generalization of the results 5)  in the last paragraph you extrapolate beyond actual  results and consider their implications/ applications

References

is a listing of complete references for  all sources of information cited in the  report, organized alphabetically by  the last name of the first author.

Each item cited must appear in the references, and  each item in the references must have been cited in  the body of the report. One-author entries precede  multiple-author entries beginning with the same first  author.

Tables

Supplement the test. They do not  duplicate information that has already been presented in text form.

Each on their individual page. Must have a title. Can  add general notes which refer to the entire table.  Specific notes which refer to items in the table that  have been identified with superscript, lower-case  letters. Probability notes which identify the level of  significance for statistics reported in the table that  have been identifies with asterisks

Figures

Supplement the test. They do not  duplicate information that has already been presented in text form.

Each on their own page. Usually have a colour  specification for types of papers.

Appendix

may be included as a means of  presenting detailed information that is useful but would interrupt the flow of  text if it were presented in the body of the paper.

Examples of items that might be presented in an  appendix are a copy of a questionnaire, a computer  program, a detailed description of an unusual or  complex piece of equipment, and detailed instructions  to participants. Appendices each start on a new page

Responsible scientific conduct

Main ideas

- Increase knowledge of issues surrounding responsible conduct of research - Ability to make ethical or legal choice when faced with research conflict - Understand that there is a range of acceptable research practices

- Adopt a positive attitude involving research ethics and responsible conduct - Scientific articles are retracted infrequently

- Retracted articles continue to get cited!

Retracted articles

- Articles are retracted infrequently

o It is difficult for an article to be retracted

- Retracted articles are still cited after being retracted

o This is bad because the fraudulent information continues to be propagated  and cited as good and true information.

Key points

- Fraud: is the explicit effort of a researcher to falsify or misrepresent data

o Making up or changing data to make it support a hypothesis

o Plagiarism – the representation of someone else’s ideas or words as your own. - Safeguards against fraud

o Replication

 Is the repetition of a research study using the same basic procedures used in  the original.

 Either the replication supports the original study by duplicating the results

 OR it casts doubt on the original study by demonstrating that the original

result is not easily repeated.

o Peer review

 The editor of the journal and a few experts review the paper and critically

scrutinize every aspect.

Incorrect references

- Publication bias

o Positive outcomes are more likely to be published than null findings  (nonsignificant results – or the experimental treatment didn’t have any  effects)

o Nonsignificant findings are more likely to be submitted to journal articles with a lower impact factor

 This is because higher impact factor journals are less likely to publish  them

o This is bad because researchers need to know when a treatment didn’t have  an effect so they don’t continue to try the treatment and continue to get  nonsignificant results

- Citation bias

o Researchers are more likely to cite papers that report significant findings   Editors realize that researchers are less likely to cite articles with  nonsignificant results so this leads to a feedback loop and thus

nonsignificant findings are less likely to be published

o Women authors are less likely to be published than men

Responsible conduct of research

- Tri-council agency framework: responsible conduct of research

o The tri-council makes up a framework for responsible conduct of research  that researchers should always try to follow

o CIHR - Canadian Institutes of Health Research

o NSERC – Natural Sciences and Engineering Research Council of Canada o SSHRC – Social Science and Humanities Research Council

- a major source of research funding for Canadian institutions

o and thus researchers at those institutions who get funding from the tri council must follow their framework

- even if a researcher got private funding

o the funders may still require the researcher to follow the tri-council agency  framework for responsible conduct of research

o this is because the private funder will want the research they are given to be  conducted in the most ethical way

o and they want to receive a good research article which has produced good  results.

Tri-council research integrity principle

1. Fabrication

o Making up data/methods/findings

o Including graphs or figures

2. Falsification

o Manipulating or omitting data without acknowledging the omission   This leads to inaccurate findings or conclusions

o Includes figures and graphs

3. Destruction of research records

o Destroying your own or another person’s research data/ records  o Done to avoid detection of wrongdoing

4. Plagiarism

o Presenting another persons work as your own, without appropriate  referencing or permission

5. Redundant publications

o Re-publication of your own previously published work – without  justification

i. Trying to get credit for the same thing twice

o When you publish an article that you have already published but now you  change a couple things

6. Invalid authorship

o Inaccurate attribution of authorship – you don’t list all the authors of the  article.

o Fail to recognize the contributions of other people in a manner that is  consistent with how much they have contributed to the work

i. You have to give credit where credit is due.

o Failure to recognize the contributions/ authorship of other people in a manner that is consistent with the policies of relevant publications

i. Neglecting to add someone to the author list – they can then  write to the editor saying that you didn’t acknowledge them but you  contributed.

8. Mismanagement of conflict of interest

o Failure to manage any real, potential or perceived conflict of interest  i. Bribery

1. Bringing timbits could be misconstrued as bribery.

ii. Perceived conflicts of interest

1. If you are a stockholder in Coca-Cola and you do research on

hydration – if you don’t declare you are a stake holder in the

Coca-Cola company who might have a conflict of interest where

you might have a personal gain from your research

Plagiarism

- Is a form of scientific fraud

o You present someone else’s ideas or words as your own

o present another person’s ideas or words without referencing the person who  said it or without properly referencing.

 Any direct quotes should appear with quotation marks and should be  correctly cited. Otherwise this is plagiarism

- All references to previous studies should be correctly cited

o State who made the claim, where they made the claim, and when they made the claim

- Minor cases of plagiarism can be unintentional

o If you read an article and then time passes and you think of the information  you read in the article but you don’t remember you read it. You think that  this idea is your own. So you don’t reference it and you present it as your  own.

o To avoid this

 Make a habit of paraphrasing all information you read in an article right when you read it

o These breaches in plagiarism can happen easily so it is important to take  steps to prevent it from happening

- Self-plagiarism

o When you present information in a new article that you already published in  a previous article.

- There are differing ideas/ opinions of what plagiarism is/ isn’t o Different people think different things are plagiarism/ are not plagiarism  o People take misconduct into context. There are differing opinions on the  same act of plagiarism

Guidelines for authorship contributions

- Design study and interpret results

o If you created an original idea, helped planned it and had an intellectual  contribution = yes

o You had to have contributed to the actual subject + intellectual contribution  o If you’ve been trained to do basic things = no authorship.

 Basic is if anyone could do it

- Supervisory role

o Supervision of the project?

o Training, education, mentoring the first author

 The person training you must produce substantive contribution in order to deserve authorship.

o Resources: money, animals/reagents

o If you are the person funding the research you would be acknowledged but  not as an author.

o Supervisor

 Usually the supervisor contributes to the original idea, provides

direction to the study and sometimes does work in the experiment so  they would get authorship.

- Data acquisition

o Original experimental work = authorship

o Technical experimental work = no authorship

o Data analysis

 You get authorship for data analysis unless it is very basic

- Writing

o Manuscript

 The person writing the entire paper is the first author. If you are writing parts of the paper then you are going to be an author.

o Editing/ reading/ commenting on the manuscript

 Reading it over = no authorship

 Unless in the process of editing you are providing substantial edits then that can be acknowledged

Preventing authorship disputes

o Collaborate with someone who you are intellectually engaged with and share a passion for the research and science.

- Share ideas, reagents, expertise with no strings attached

o If they regard your contributions as valuable don’t be stingy about sharing.  By sharing you might end up making a significant contribution to the project. - Divide the labor at the start of the experiment

o Establish in writing the tasks that will be completed on your part of the  collaboration

 Know who is completing and contributing what to the research.

 In a collaboration where both parties are equally engaged, there is  mutual trust and such there is no need for this.

 Bring it up in a conversation if you aren’t sure

- Respect and communication

o Discuss progress frequently with the group.

o Anticipate and make corrections to problems in a timely manner.  o Be willing to share information and reagents

o May re-negotiate authorships as project develops

 Because as the project evolves there could be warranted renegotiation  of authorship.

How fraud gets through - Background information

- The 8 forms of misconduct

- Peer-review process

o Reviewer goes through your paper to try and point out flaws.

o Sometimes make a lot of edits.

- New information

o How can fraudulent data still get through

Spotting fraud

- Can be difficult to spot fraud

o This is one reason that fraudulent data gets through the peer-editing  process.

- Most research papers are peer-reviewed prior to being published  o Usually, the reviewer looks at specific things – they trust you and thus they  don’t look so much at fraud.

o They are not paid to peer-reviewed articles so they might not be spending as  much time.

o Publishing articles is oftentimes on a strict timeline and thus the editor may  not have enough time to comb through

- Example: 1997: Editors at the British Medical Journal took a short paper and  inserted 8 errors, then asked researchers to identify the mistakes – the researchers were also told that there were 8 errors in the paper

o 221 people took part in the study

o Most people only spotted 2 errors. No one found more than 5. And 16%  didn’t find any errors.

Videos and articles posted on CULearn for use as background knowledge.

Sample size outlier’s exclusion criteria

- A person was trying to reproduce results in their experiment, but they were not able to  reproduce the results.

- They were in contact with the original researcher about not being able to reproduce the  results and he sent over his raw data

- The original researcher said that the control samples he used have been in the freezer for  years (may have degraded over time)

o The original researcher failed to mention that they had kept the samples in the  freezer for years

o The new researchers had been preparing the control samples fresh.

- The original researcher was very open to their input and findings. They offered to run the  experiment again using a fresh control sample and then compare their results with the  new researcher.

- Dr. Chee’s comment

o Points out that there are some factors we need to pay attention to

o It is the responsibility of the author to include all of the methodology used so that  other researchers can replicate it with the same results.

The original author and the replicating author have a responsibility to complete  their work transparently.

Lack of transparency

- New researcher could not reproduce the results of the original researcher. - The new researcher was following the same experimental procedure  - One of the new researchers’ supervisors said to toss out anything greater than 10  because it is outside the original parameter.

- The other supervisor gave a suggestion for the data analysis

- Results were still inconclusive and were not consistent with the experimental  results.

- The supervisor looked over the original researchers lab note book  o Findings were that she excluded samples without a real reason.  o The original researcher had only used female mice.

o Rigorous research requires considering sex as a biological variable when  using any animal samples

- Dr. Chee’s comment

o Why you cannot exclude data points are you please

o You cannot change parameters in the middle of the experiment.  o The supervisor was expecting to see a certain set of results because the  original researcher provided results that they liked.

o The conclusion was that there was a mistake in the original paper and its  findings

o Don’t be a bully

 It makes it more likely for something to go wrong and increases the  pressure for someone to take a risk to falsify results in the research  field.

Article: “the science behind social science gets shaken up – again” Rogers A. The Science Behind Social Science Gets Shaken Up—Again. Wired. Published August 27, 2018.  Accessed December 5, 2020. https://www.wired.com/story/social­science­reproducibility/

- Research showed that “people see emotional impulsive people as inherently more  honest”

- The results did not replicate. New studies showed that the original study failed an  important step in science validity being replicability as you are supposed to be able to produce the same results if the experiment were to be redone.

- New research purpose was to study reproducibility – the question was whether or  not replicators could rule out some of the excuses for why the results weren’t  reproducible.

- 5 teams retested the results using larger groups

- Findings:

o 13 has a statistically significant effect in the same direction as in the original  o The effect was half as large as the original paper reported.

o The new papers showed zero effect

- Using Altmetrics it is possible to analyze the effects of published scientific articles.  - Comments

o The fact that the original paper wasn’t replaceable doesn’t mean the  conclusions were false.

o Experiments can fail to replicate for many reasons.

 Methodological problems.

 Resource constraints and methodological arguments

- “The solution to the reproducibility crisis is better training, statistics and  institutional practices that’ll stop these kinds of problems in research before they  make it to pages of a journal.”

- Dr. Chee’s comment

o Experiments fail to replicate for a variety of reasons

o Maybe better training, statistics etc. could prevent it

o If a paper failed to replicate, we need to make sure we look at all the  conditions, it is not always the case that the conclusions are false

NPR- science replication issue (podcast)

- Most experiments cant be reproduced with the same results.

- Reproducibility crisis

- The main effect was that stereotyping and priming has an effect on math  outcomes

- We cant be quick to make judgments when researchers fail to replicate a finding - Because there are multiple factors that come into play

o Methodology

o Sample size

o Geography

- Examples of stereotyping

o Diet drinks – people often think that women ordered the diet coke when it  could just as likely had been the man

- Replication must closely match the conditions

- If a study failed to replicate – does this mean that there is fraud and scientific  misconduct happening?

o If it does not hold in alternative studies that not necessarily a failure to  replicate, but may contribute a more nuanced view on the original finding  - Any individual study is not necessarily the truth

o There would always be more things to uncover

o Become more certain about our knowledge when we are able to replicate it.  Data reproducibility

- Transparency – accurately and openly providing all key information on the design, execution and analysis

- Adequate reproduction is possible only if the methods, rationale, any  pertinent information is accessible

o Human – gender, age, demographics, medical history

o Animals – gender, age, strain, background, N

o Reagents – where you sourced your resources (company, city, state),  antibodies

- Reporting of only positive findings

o If you're only reporting positive findings and ignoring the null findings then it  will make it hard for someone else to reproduce them because they might be seeing that the findings are not correct.

o This is the responsibility of both the original and replicating researchers

Principles to facilitate scientific rigor

- https://www.nih.gov/research-training/rigor-reproducibility/principles-guidelines reporting-preclinical-research

- Report the number of replicates and whether results substantiated by repetition - Statistics must be fully reported

o Test used, N, definition of center/dispersion measures

o What were the statistical tests used

- State whether samples were randomized and how

o Indicate if your samples were randomized

o If you were blinded to the scoring process

 State if experimenters were blinded to group assignment

o Did you state the inclusion/exclusion criteria

- Clearly state inclusion and exclusion criteria, including any results omitted  o Did you omit anything – if you did indicate what your criteria for exclusion  was

o How sample size is determined

o Power analysis

- Especially if results did not support main findings

How can you facilitate this? Lab notebook

- Your lab notebook is a legal document

- the scientific legacy of the lab –

o after you leave the lab your lab notebook remains and it is the legacy you  leave behind

- Complete record of procedures, reagents, data, thoughts to pass on to  others

o Explain why experiments were created

o How the experiments were performed

o And what the results of the experiment were

o Your lab notebook can be used to validate findings and defend your claims  o It is the evidence for the validity of your work

- The lab notebook is NOT:

o Journal

o Record of communications with PI/colleagues – not where you make notes  about conversations with the supervisor. Only record your findings from each day

o Yours to take home

 Belongs to the institution.

 You have the right to make photocopies or take pictures of your work  - Notebooks can come in different forms

Type of Notebook

Bound/Stitched

No lost pages, Numbered pages

Difficult to photocopy, organized by date  (not logically organized)

Loose leaf/Binder

Organize by experiment, data  stored together

Sheets fall out, difficult to authenticate

Computer/Electro nic

Requires electronic security, corrupted  files, software compatibility

Contents of a lab notebook

- Name, year, lab mailing address

o So it can be returned to the lab if lost

o Page number, date, subject/experiment

o A record of all the work that was done organized by date and experiment  type

- Body of notebook

o Indicate the experimental entries

o Observations  Everything that happened, planned or unplanned, raw data,  taped into page or data location

 Date, title, hypothesis/goal

 reagents, solutions, time course, concentrations

 Reagents you were using only if you are changing it for that day

 New bottles of the reagent

 What reagent you are using

 Otherwise you can just give reference to the lab procedure in the lab  notebook

o Data analysis

References

- Dr. Melissa Chee lectures posted to CULearn between November 1st and December  5th

Gravetter FJ, Forzano L­AB. Research Methods for the Behavioral Sciences. Wadsworth; 2012. ?

What is the purpose of a cause and effect relationship? The purpose of an experimental research strategy is to establish the  existence of a cause-and effect relationship

The research process

1. Develop a research idea

2. Formulate a hypothesis and prediction

3. Determine how to define and measure variables

4. Identify subjects for the study

5. Select a research strategy

What is a variable

- Independent variable

o What you manipulate

o Manipulation: of the independent variable creates two or more  conditions. Helps determine the cause

- Dependant variable

o What you measure

o Measurement of the dependant variable produces scores from  each condition

o Comparison of the sores in one condition to those from another  condition help indicate the effect

- Confound variable

o Variables that are not held constant and affects some groups  only

o Control of other conditions ensure that the other variables do  not influence the outcome on the dependant variable

- Obscuring variable

o Increases variation in the data and affects all groups

Cause

Effect

Independent variable

Measured

Manipulated

Dependant variable

Research scenario 1

A researcher is interested in determining whether a new drug, Drug A  decreases aggregation of AB plaque deposition in the hippocampus. 5XFAD  transgenic mice overexpressing mutant human amyloid precursor protein  were purchased from Jackson Laboratory. The 10 mice were divided into two  equal groups, Group A and Group B. Both groups were housed in the animal  care facility at a temperature of 23 degrees. The day before testing, the fire  alarm went off in the facility causing prolonged distress among both groups  of mice. On the day of testing, Group A mice received an injection of the

placebo cocktail. Unfortunately, the research felt sick and had to go home, so another researcher stepped in to inject Drug A into Group B mice. Two weeks  later, the mice were sacrificed, and the researchers conducted a histological

analysis to examine AB plaque deposition in the hippocampus. The results  showed no significant difference in amyloid plaques aggregation in the  hippocampus among mice that received Drug A.

Was there manipulation?

- Yes

- The researchers manipulated Drug administration by changing the  value to create a set of two treatment conditions (Drug A vs Placebo) Was there a control group?

- Yes

- The group of mice that received a placebo drug were used as a control  group in this experiment.

Did the change in researchers administering the drug act as a  confound?

- Yes because the change in researcher affected one group  Did the fire alarm act as an obscuring variable?

- Yes because the fire alarm affected both groups

Would this study have high internal or external validity  - Internal

- The researchers controlled all variables not being manipulated thus  increasing the internal validity of the study.

Is this an experimental research strategy

- Yes

Research scenario 2

A researcher is interested in determining the link between Instagram usage  and the development of depressive symptoms among adolescent females.  Participants were recruited via a paid advertisement on Instagram, Facebook, and Twitter from 2019 to 2020. The researchers collected survey data  measuring the average number of hours spent on Instagram a day and  depressive symptoms from 700 participants across Ontario. The average age of participants was 15 ± 2.3 years. Of the 700 participants, 40 were excluded from the study due to incomplete surveys. In the middle of the study, a  global pandemic shut down the country, requiring public spaces to close and  people to social distance. The mandatory safety measures enforced varied  across cities. Following the analysis, the researchers determined there was a  significant relationship between Instagram usage and depression scores, with higher Instagram use being associated with greater depression symptoms.

Was there manipulation

- No

- The researchers administered a survey and did not manipulate any  variable

Was there a control group

- No

- There was no control group that acted as a control, scores for  depressive symptoms and Instagram usage was taken from all  participants

Did the variation in mandatory safety measures across different cities  act as a confound

- Yes because the mandatory safety measures affected the participants  differently

- The mandatory saftey measures affected the participants differently depending on where they lived.  Individuals living in a city with strict saftey measures like social distancing and park closure bylaws may  report greater depressive symptoms than individuals living in a city with less restrictive measures. Did the pandemic act as an obscuring variable

- Yes because the pandemic affected all participants

- Both groups were affected by COVID­19 and this could have influenced the scores of all participants. Would this study have high internal validity or high external validity  - High external

- This study does not have many controls and mimics the real world, it is generalizable to the population in  which the study was conducted on.

Is this an experimental research strategy

- No

- The researchers were not interested in a cause­and­effect relationship, they simply wanted to understand  the relationship between two variables.

Learning module #2 – compare, contrast and describe the five research strategies

Objectives

- Recognize research questions

- Compare and contrast the five research strategies

- Apply our knowledge to real world examples

Research strategy

After the researcher has identified the research area, formed a hypothesis, decided how to define and measure the  vairables, determined which individuals should participate in the study and how to treat them ethically, the next step is to select the research strategy

- Defined as the general approach and goals of the research strategy

- Seleting the research strategy is typically determined by the question  the researcher wants to address and the kind of answer the researcher  hopes to obtain

Questi

on

numbe

r

Question

Explanation

1

What is the relationship between the  average number of hours spent

the relationship between two variables (average

studying and test performance  among second year neuroscience  students?

number of hours spent studying and test  performance). The type of research strategy  selected should provide a description of the  relationship.

2

Is the average number of hours  second year neuroscience students  spend studying more or less than the  average number of hours second  year political science students  spend studying?

This question is asking about a single variable (the number of hours spent studying). The  type of research strategy selected should  provide a description of the variable.

3

Can the type of media (electronic  vs paper) used to study for an  exam by second year

neursoscience students cause higher or lower grades?

relationship between two variables (type of  media and grades), however, this question  asks for an cause for the relationship. The  type of research strategy selected should  provide an explanation for the relationship

The five research strategies

Experimental

- Establish unambiguous cause-and-effect relationship

Quasi-experimental

- Compare pre-existing groups to obtains results. Attempt

to control treats to internal validity

Nonexperimental

- Compare pre-existing groups to obtain results. No

attempts to control treats to external validity

Correlational

- Describe the relationship between two variables and

measure the strength of the relationship

Descriptive

- Describe individual variables as they exist

Journal one

- Psychiatric symptoms and disorders among Yazidi children and

adolescents immediately after forced migration following ISIS attacks

Identify the research question

- What is the occurrence of psychiatric problems and disorders

experienced by Yazidi Kurd refugee children and adolescents

- This information can be found at the beginning of the summary section. The researchers were interested in  describing a single variable (psychiatric problems and disorders).

Identify the independent variable

- There are no independent variables in this study

Identify the dependant variables

- Psychiatric problems and disorders

- The researchers measured the occurrence of psychiatric problems and  disorders among Yazidi Kurd children and adolescents.

What research strategy was used

- Descriptive

- the researchers were describing psychiatric symptoms and disorders of a specific group of children who underwent forced migration and  related traumatic experiences. There was no manipulation, control, or  treatment conditions.

Suppose the researchers wanted to conduct a follow up study to  examine the relationship between the number of traumatic  experiences and the rates of psychiatric symptoms and disorders  among Yazidi Kurd children and adolescents. What research  strategy should they use?

- Correlational

- In this scenario, the researchers were interested in describing the  association/relationship between two variables. There was no  manipulation, control, or treatment conditions.

Journal 2 Experimental study of the differential effects of  playing versus watching violent video games on  children's aggressive behavior

Identify the research question

- Does the active involvement of playing a violent video game increase  aggression than the passive observation of watching the same violent  video games?

- Found in the last paragraph of the introduction

Identify the independent variables

- Video game conditions

- The researchers manipulated the video game conditions (Active violent condition vs passive violent vs active non-violent condition). Identify the dependant variable

- Aggressive behaviour

What research method was used

- Experimental

- This study satisfies the four basic elements of an experiment. The  independent variable is video game conditions and the dependent  variable is aggressive behavior.

Suppose the researchers wanted to conduct a follow up study to  determine the average number of hours a week the participants  play violent video games. What research strategy should they use? - Discretional

- on this scenario the researchers are interested in describing one  variable (average number of hours spent playing video games).

Assignment #3 –

1. A researcher tests the effect of stress on the tail suspension test. The  following data shows the percentage of time tha a mouse is immobile  in a tail suspension test.

a. State the null hypothesis

i. Stress has no impact on the percentage of time that a

mouse is immobile in a tail suspension test

b. What is the mean score for each condition

i. Nonstressed: 42.5

ii. Stressed: 45

c. Name the independent variable and dependant variable  i. Independent: stress

ii. Dependant: % immobile

d. The above data is best summarized by

i. Bar graph

e. The outome of the experiment yielded a p-value of p=0.42 f. What is the probability that there is no differene between non stressed and stressed mice?

i. 42% chance there is no difference

g. Can the researcher reject the null hypothesis

i. No

h. Is there a statistically significant difference in immobility time  between the stressed and non-stressed mouse?

i. No

2. With reference to a 3 x 2 x 3 factorial design answer the following  questions:

a. How many independent variables are in this factorial design  i. Three

b. How many effects are possible in this experimental design  i. 3

3. In the sample excerpt below, indicate (yes or no) if a citation is  required:

4. Autistic spectrum disorders (ASD) typically begin in childhood and are  characterized by difficulties with social interaction, communication,  and behavioral flexibility. There is a speculative association between  ASDs and violent crime offenders (Reference A). Hippler et al. (2010)  found that rather than being more likely to engage in violent behavior,  individuals with ASD have a higher risk of being the victim rather than  the perpetrator. It is necessary to stress these findings regarding  neurodevelopment and violent crime in order to avoid stigmatizing an  already vulnerable group (Reference B).

a. Reference A

i. Yes

b. Reference B

i. No

5. Use the abstract obtained from PubMed to answer the following  questions:

a. What is the volume

i. 44

b. What is the PMID for this publication

i. 12790887

c. Is this a primary or secondary article?

i. Primary

6. Use this description to answer the following questions A researcher  determined if caffeine can offset the debilitating effects of alcohol on

motor skills test. The factorial matrix below summarizes the results of  the rat experiment.

a. List the specific conditions for the experiment

i. Top left: Placebo+ water

ii. Top right: Placebo +alcohol

iii. Bottom left: caffine + water

iv. Bottom right: caffeine + alcohol

b. How many effects

i. 2

c. Is there an interaction between alcohol and caffine on motor  score

i. Yes

d. The researcher used a mixed group design for this experiment  and included 15 rats per group. How many rats were used in  total?

i. 30 rats

7. Illustrate the above factorial data as a graph. Remember to label axis,  legends, etc. Please draw the graph on a separate piece of paper and  upload a photo below.

Methods of acquiring knowledge summary

Method

What

Problems

tecxtbook

Tenacity

-Information is

accepted as fact  because it is

believed to be true  -Can become

superstition of habit

-This information could be outdated  and no longer true.

-Brains are hardwired to look for  patterns, it is then easy to turn  patterns into habits

holding on to ideas and beliefs simply  because they have been accepted as  facts for a long time or because of  superstition. Therefore, the method of  tenacity is based on habit or

superstition. Habit leads us to continue believing something we have always  believed. Often this is referred to as  belief perseverance.

Intuition

-Information is

accepted based on  a feeling something is right.

-Often based on  subtle cues that  you perceive

subconsciously.

- We can’t solely rely on intuition we also have to test the information. -Often times we think our gut  feeling is right

One problem with the method of  tenacity is that the information acquired might not be accurate. With regard to  the statement about old dogs not being  able to learn new tricks, the elderly can  and do learn

information is accepted as true  because it “feels right.” With intuition,  a person relies on hunches and  “instinct” to answer questions.  Whenever we say we know something  because we have a “gut feel- ing”  about it, we are using the method of  intuition. For example, at a casino, if  someone puts his money on the  number 23 at a roulette table because  he “feels” it is going to come up, then  that person would be using the  method of intuition to answer the  question of which number to play.

Authority

-When an expert in  the field gives

information and  they are trusted  -Quickest and

easiest way to get  an answer.

-You accept the information because you believe the authority figure. -Journal impact factor: Just because  it is published in a journal doesn’t  mean the information is always  correct.

It does not always provide accurate  information. For example, au- thorities  can be biased. We have all seen  examples of conflicting testimony by  “expert witnesses” in criminal trials.  Sources are often biased in favor of a  particular point of view or orientation.

a person finds answers by seeking out  an author- ity on the subject. This can  mean consulting an expert directly or  going to a library or a website to read  the works of an expert. In either case,

you are re- lying on the assumed  expertise of another person

Rationalis m

-Information is

accepted based on  use of reasoning or  logic

-The premise statement is assumed  to be true. If any part of the premise statement is incorrect then the  conclusion could be false.

- Predictions

- Used to create research idea

involves seeking answers by logical  reasoning. We begin with a set of  known facts or assumptions and use  logic to reach a conclusion or get an  answer to a question. Suppose a  clinical psychologist wanted to know  whether a client, Amy, had a fear of  darkness. A simple example of

reasoning that might be used is as  follows:

All 3-year-old children are afraid of the  dark. Amy is a 3-year-old girl.

Therefore, Amy is afraid of the dark.

the first two sentences are premise  statements. That is, they are facts or  assumptions that are known (or  assumed) to be true. The final  sentence is a logical conclusion based  on the premises. If the premise  statements are, in fact, true and the  logic is sound, then the conclusion is  guaranteed to be correct. Thus, the  answers obtained by the rational  method must satisfy the standards  established by the rules of logic before they are accepted as true.

Empirical/  Observatio nal

-Information is

accepted based on  personal

experience

-Observations you  make become

points of data and  your knowledge  becomes solely  based on that data.

- Data collection -  Used to create

research idea

-Variables are not operationally  defined.

-If using an abstract concept of  measurement (bigger/harder)  observations can be misconstrued. -if only based on observations  everyone would have different data  points. Fundamental flaw -Our  interpretations influence out  observations. Interpretations can be biased based on expectations.

It is tempting to place great confidence  in our own observations. Everyday  expressions such as, “I will believe it  when I see it with my own eyes,” reveal  the faith we place in our own

experience. However, we cannot  necessarily believe everything we see,  or hear and feel, for that matter.  Actually, it is fairly common for people  to misperceive or misinterpret the world around them.

attempts to answer ques- tions by  direct observation or personal  experience. This method is a product  of the empirical viewpoint in

philosophy, which holds that all  knowledge is acquired through the  senses. Note that when we make  observations, we use the senses of  seeing, hearing, tasting, and so on.

Tenacity

Messages such as “opposites attracted” or “you can’t teach an old dog new tricks” have been  repeatedly presented and accepted as true.

However, it is possible for older people to learn new skills. Similarly, research has shown that it is more common for people to be attracted to others who have similar interests.

Intuition

Often times, the decision is determined by what do I “feel like doing. Going out or staying in?”

If you don’t know the answer to a multiple-choice question and you have just answered 4 “d” in a row, you are less likely to guess “d” even though the answer could still be “d”

Authority

It does not always provide accurate information; authorities can be biased. A behavioral  psychologist will give you a different answer than a psychodynamic psychologist.

If parents ask a behavioural psychologist, why their child is throwing tantrums they will hear  “you’re reinforcing the behaviour by giving into the demands of the child” If parents were to ask a psychodynamic psychologist, they might hear “ because of a failure to  meet the child’s oral needs.”

Rationalism

People are not good at logical reasoning. Consider the following argument;

All psychologists are human. Some humans are women. Therefore, some psychologists are women.

Many people would view this as a sound, rational argument. However, this is not a valid argument;  specifically, the conclusion is not logically justified by the premise statements. Replace Women and  psychologist with apples and oranges All apples are fruits. Some fruits are oranges. Therefore, some apples are oranges.

Empirical/  Observation al

We cannot believe everything we  experience because sometimes we  want to believe something is true  simply because it is what we have  always done or thought to be true.

If someone told you that you could pick between gross potato chips and delicious  noodles, you would pick the noodles. But if  they told you the noodles were worms; you  would pick the chips. This is because you  believe that people do not eat worms  (method of tenacity).

Non-scientific methods of knowledge are not obsolete.

Non-scientific methods of knowledge can be used in conjunction with scientific methods of  knowledge.

But in order to rely on information founded on one of the non-scientific methods of knowledge  you must confirm the information using scientific methods of observation.

Non-scientific methods of knowledge are not obsolete.

Scientific methods of acquiring knowledge

 It has a systematic approach, which means it has a set of steps.

 Better quality answers with a higher confidence in validity can be achieved by

o A greater data representation. Meaning more participants/ subjects. o With more data, you can be sure your results are reproducible.

o Data reproduction is necessary and important in validity and confidence.  Steps in the Scientific Method

1. Identify a research area

a. How you can think of a good idea

1. Source of Ideas

i. Pick a general topic of interest

ii. Do research to find out what is known and what remains unknown.  iii. Do background research on what is unknown.

iv. Narrow it down to a specific idea.

 Past research, your observations, others observations, practical problems   Make sure the idea is interesting, novel, fundable, publishable.

o Novel: It must be a new idea that has not been researched already o Fundable: Will you be able to find someone to fund it?

o Publishable: Will other researchers want to read your article? Will other researchers want

the scientific method

The scientific method is a method of acquiring knowledge that uses observations to develop a  hypothesis, then uses the hypothesis to make logical pre- dictions that can be empirically tested by  making additional, systematic observations. Typically, the new observations lead to a new hypothesis, and  the cycle continues.

As you know, when we say that science is empirical, we mean that answers are obtained by making  observations. Although preliminary answers or hypoth- eses may be obtained by other means, science  requires empirical verification

Step 1: observe behaviour or other phenomena

o Introduction, or inductive reasoning, involves using a relatively small set of specific  observations as

the basis for forming a general statement about a larger set of possible outcomes   · 1.4 The research process

 · 1. Find a research idea: Select a topic and search the literature to find an unanswered  question.

o Identify a general topic you would like to explore and review the background literature to find a specific research idea or question.

· 2. Form a hypothesis and a prediction

o Form a hypothesis, or tentative answer to your research question, and use a specific research  prediction.

o Logical: A good hypothesis is usually founded in established theories or developed from the  results of previous research. Should be the logical conclusion to a logical argument.

o Testable: it must be possible to observe and measure all of the variables involved. Must  involve real situations, real events, and real individuals. A testable hypothesis is one for which all of the variables, events, and individuals are real, and can be defined and observed.

o Refutable: it must be possible to obtain research results that are contrary to the hypothesis. A refutable hypothesis is one that can be demonstrated to be false. That is, it is possible for the  outcome to be different from the prediction.

o Positive: it must make a positive statement about the existence of is a relationship, the  existence of a difference, or the existence of a treatment effect.

 Testable

 Another half mark for making it specific

 Diet soda volume or amount of weight gain

 Refutable

 Can be proved wrong

 If diet soda did not increase weight gain

 Predictions

 You have information and make a prediction about what will happen less.

 You predict something based on specific examples and give a general statement of  all

- systematic Plan to collect data

o once you collect the data, you have to analyze the data to see if it supports your  hypothesis. If not you have to make a new observation in order to continue your research.

PREDICTION: Deduction, or deductive reasoning, uses a general statement as the basis for reaching  a conclusion about specific examples.

- Deductive reasoning uses a general hypothesis or premise to generate a prediction about specific  observations.

HYPOTHESIS: a hypothesis is a statement that describes or explains a relationship between or  among variables. A hypothesis is not a final answer but rather a proposal to be tested and evaluated.

- Inductive reasoning uses a few limited observations to generate a general hypothe- sis.  · 3. Define & measure variables

o Identify the spefic procedures that will be used to define and measure all variables. Plan to  evaluate the validity and reliability of your measurement procedure.

· 4. Identify and select participants or subjects

o Decide how many participants or subjects you will need, what characteristics they should have  and how they will be selected. Also plan for their ethical treatment.

o Participants in research studies are called participants if they are human and subjects if they  are not human.

· 5. Select a research strategy

o Consider internal and external validity and decide between an experimental (cause-effect), or a nonexperimental strategy.

· 6. Select a research design

o Decide among between-subjects, withing-subjects, factorial, or single-subject designs.  · 7. Conduct the study

o Collect the data

· 8. Evaluate the data

o Use the appropriate descriptive and inferential statistics to summarize and interpret the  results.

you must use various statistical methods to examine and evaluate the data. This involves drawing graphs,  computing means or correlations to describe your data, and using inferential statistics to help de- termine  whether the results from your specific participants can be generalized to the rest of the population.

· 9. Report the results

o Use the established guidelines for format and style to prepare an accurate and honest report  that also protects the anonymity and confidentiality of the participants.

· 10. Refine or reformulate your research idea

o Use the result to modify, refine, or expand your original research ide, or to generate new ideas. ·

1. Test the boundaries of the result: Suppose your study demonstrates that higher levels of academic  performance are related to higher levels of self- esteem for elementary school children. Will this  same result be found for adolescents in middle school? Perhaps adolescents are less concerned

about respect from their parents and teachers, and are more concerned about respect from peers.  Perhaps academic success is not highly valued by adolescents. In this case, you would not  necessarily expect academic success to be related to self-esteem for adolescents. Alternatively, you might want to investigate the relationship between self-esteem and success outside academics. Is  there a relationship between success on the athletic field and self-esteem? Notice that the goal is to determine whether your re- sult extends into other areas. How general are the results of your  study?

2. Refine the original research question: If your results show a relationship between academic success  and self-esteem, the next question is, “What causes the relationship?” That is, what is the  underlying mechanism by which success in school translates into higher self-esteem? The original  question asked, “Does a relationship exist?” Now you are asking, “Why does the relationship exist?”

Examples of hypotheses

Drinking diet soda “affects” body weight gain

- You’re not showing its direction

Make a hypothesis example Nicotine decreases cholesterol

- Variable

o Identify the independent and dependant variable in the hypothesis

o Independent variable would be the thing that youre manipulating = nicotime o Dependant  variable is the thing you are observing = cholesterol

- Directional

Nicotine affects cholesterol o Is it good/bad increase/decrease

- Testable

o Can measure the variables

- Detal

o Must be a bit more specific than just nicotine

What type, what form o Or just cholesterol

What type of cholesterol HDL/LDL, etc.

MINE: Participants who use nicotine patches will show a decrease in total cholesterol levels  compared to participants who do not use nicotine patches.

HERS: Participants wearing a nicotine patch will have reduced levels of total cholesterol  3. Define and measure variables 3A- Types of variables

 There are two main types of variables

 Independent variable (X-AXIS)

o Manipulated variable

o Determined by the experimental design/ researcher o Known in advance o Determined by the treatment conditions

• Dependant variable (Y-AXIS) o Responding variable

o Measured in each of the treatment conditions

o Determined during the course of the experiment

3B- define what you are measuring

 Be specific and unambiguous

 Must be measurable and defined qualities. You need an operational definition to be able to

measure something.

 Break down concepts into something that can be measured

• Examples of things that you can measure.

o Health: cholesterol, blood pressure, BMI, heart rate o Intelligence: Exam scores, IQ test scores,  transcripts

3C- Clarify how to measure variables

Measurements must have the following characteristics

• Validity

o You are measuring what you claim to be

• How presice can your measurement be o Property of your instrument

Calibration, precision o How much error is there?

How much room for error can you have in what you’re measuring?

• Reliability

o Measurements are consistent, produce nearly identical results when used repeatedly

When you step on a scale and you get 150 the first time you should get close to this amount  when done repeatedly. If you don’t than that instrument is not reliable.

o Relates to replicability

4. Identify research participants/ subjects

 Participant= Human

 Subject= nonhuman

 Neurons, cells, tissues

 Ethical considerations

o human, animal, cell model • Demographics

o Species, sex, age, weight, etc.

o You can’t say that a vaccine protects from death if you only tested it on tissue. You have  to prove it is safe for animals and then move on to humans. • Sample size

SUMMARY

2. Formulate a hypothesis and prediction

1. Hypothesis – is something that is a generalization, generalizations use the inductive  reasoning

2. Predictions- is something that is general and then you form something more specific using deductive reasoning

3. Inductive reasoning generates hypothesis because you’re making a general  statement

4. Deductive reasoning forms prediction because you’re producing more specific  outcomes

3. Determine how to define and measure variables

1. Independent variables is what you manipulate

2. The variable that you measure is the dependant variable

4. Identify subjects for the study

1. Participant is human

2. Subject is nonhuman

Defining and Measuring Scientific Variables

The Research Process

Basic Terminology: factor vs condition vs level

Factor

 → Differentiates between a set of groups being compared in an experiment.   → Independent variables of an experiment

o Animal handling

o In an animal experiment “does drug A increase locomotor activity of a mouse” The factor would be the  type of drug being used

The factor could also be the type of locomotor activity that is used.

→ A factor is ultimately different categories of independent variables that can occur in your experiment.  Condition

→ How is the group treated in an experiment?

o Handled for 10 minutes and given 10 mg of drug o Not handled and given 10mg of drug.  Levels

 → Different values of the independent variable that are selected to create the treatment condition   → What is the mouse experiencing, how long is the mouse experiencing that for.

o Animal handling

Handling vs no handling of animals

Handling for 10, 20, 30 minutes o Drug administration

Drug dosage: 10, 30, 100 mg of dosage Time course: 1, 2, 3, 4 hours

Variables

 → Variables are any condition that can change or have a different values for different individuals.   → Goal of experiment - Is to find out how variables can change under different conditions  o Example: “how fast is a neuron firing during sleep and wake?” Variable is the neuron firing  o Example: how much do mice move after amphetamine treatment?

Variable is how much the mouse moves

o In order to answer the questions we have to be able to define a variable

→ It is important to define the variable

o Defining a variable ensures a clear understanding of how your question is being answered.

How is neuronal firing measured frequency, or counting the number of spikes over a set period of time. How is locomotor activity measured distance traveled in cm, # beam breaks, # of wheel revolutions

o Without defining the variables it is unclear to the reader how you are quantifying the variables.  Characteristics of a variable

→ Observable

o Can be measured directly or indirectly

→ Replicable

o Can be measure consistently

 → In order for the above to be true, the variable must be specific and unambiguous   → Must have at least 2 levels per value

o Sleep/ wake (also known as the arousal state) o Glasses/ no glasses

o Drug dose: 1,5,10 mg

→ If you give someone a drug you test if it works by also observing participants without the drug.  Operational definitions

 → Some variables are not easily observed or quantified

 → Converts an abstract

 → Example: constructs are variables that are more abstract (intelligence or health). Providing a  definition for

the construct is the operational definition.

 → Good operational definitions are:

o Clear, precisely articulated

o Makes replication possible

→ Example of formulating a good operational definition

o Construct anxiety

o Variable heart rate

o Operational definition heart rate exceeding x bpm

 → Improve the validity and reliability of your measurement.

 → Validity

o Accurate measurement

o Whether or not what you’re measuring is even accurate → Reliability

o Consistent measurements

o You consistently get the same measure

o If you put a mouse on a scale, how consistent is your measurement.

Situational variables

 → A variable that describes the situation or environment.

 → These variables are characteristics within the experiment, but they do not have to be an  independent

variable. It can be something that you want to control

o What aspects of the environment changes for the subjects?

→ For example

o Amount of drug

Mice injected with variable amounts of drug o Time of day

Firing rate of a neuron in the light and dark cycle.

Response variables

 → Response variable is almost always a dependant variable

 → We can measure the effect of a situational variable on a response variable. You often manipulate  a

situation to record a change in the response.

 → Example: does caffeine boost memory performance, weight loss, athletic performance?

o Situational variable: Whether or not the participant is drinking caffeine

o Response variable: weight loss, athletic performance, memory performance.

Participant variables

 → Differences between individuals

 → Constant within individuals, variable between individuals

o Gender

o Height

o Genetic composition

Mediating Variables

 → Helpo explain why a relationship exists between two other variables

 → A mediating variable facilitates the interaction between your independent and dependant  variable.

 → Typically found in psychological theories and in treatment/ prevention

Mediating variables in psychological theories

→ Example: whether or not social support will lead to a patient taking home more positive health practices  o Situational variable: amount of social support available to a participant

o Mediating variable: how many positive health practices is that participant practicing. o They determined that increasing social support increases positive health practices.

o Mediating relationship: This relationship is mediated with the patient feeling less lonely and increasing  hopefulness.

Mediating variables in treatment/ prevention

→ Example: COVID-19 - non-white Americans with coloured Americans. The infection rate and death in the  coloured community is experiencing a higher rate of morbidity and infection.

o We don’t see an equal result between population and percentage of total cases. o Situational variable:  race

o Mediating variable: living environment

o Response variable: COVID severity

Qualitative vs Quantitative variables

Qualitative Variable- described in words

 → Described in words

o Non-numeric

o Categories

 → Important when observing changes

o Behaviour change

o Depression (bipolar/unipolar) o Drug use (

Qualitative Variables – described in numbers

→ Can be counted o Age

o Salary

o Caloric intake

o Number of food pieces

Continuous vs. Discrete variables

Discrete Variable

 → The reference of discrete variable for a quantitative variable is typically a description of its  category

 → Measured in categories

o Distinct, separate values

 → Usually measured as a whole number

 → Is obtained by counting

o Number of pieces of chocolate o Number of children

o Number of cells

Continuous Variable

→ Measured on a continuum

o Any value in a range

→ Whole unit or fraction

o Can have decimal placed

→ Obtained by measuring

o Calories of chocolate

o Is measured on a continuum o Weight, height, age, time

Can have decimal places o Temperature

Can have decimal places o Duration of drug exposure.

Introduction to measurement

 → Aim for variables that are well defined, easily observed, easily measured

 → Some aspects of research, typically neuropsychology, examine intangible abstract phenomena variables

that cannot be seen / measured directly = constructs

o Motivation, anxiety, intelligence

→ Create operational definitions to measure these constructs o Identify behaviours associated with your  construct

Tell the reader what you are defining as a behaviour o Measure that behaviour  o Resulting measurements helps define your construct

o This process = operational definition

→ A systematic or step wise process in defining what you are measuring and how you are going to  measure it.

Page Expired
It looks like your free minutes have expired! Lucky for you we have all the content you need, just sign up here
References: