Do Speed Daters Become Pickier the Later It Gets?

Introduction

Chapter 12

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You must read each slide, and complete any questions on the slide, in sequence.

Nonexperimental Design

A design in which there is no control or manipulation of the independent variable; cause-and-effect relationships between variables cannot be established.

Experimental Design

A research method in which the experimenter controls and manipulates the independent variable, allowing the establishment of cause-and-effect relationships between the independent and dependent variables.

Independent Variable (IV)

The variable that influences the dependent variable. In experiments, the researcher manipulates or controls this variable.

Dependent Variable (DV)

The variable measured in association with changes in the independent variable; the outcome or effect.

Between-subjects Design

A data collection method in which each participant or subject is only assessed on the dependent variable once.

Within-subjects Design

A data collection method in which each participant or subject is assessed on the dependent variable more than once.

Repeated-measures Design

A within-subjects design where participants are exposed to each level of the independent variable and are measured on the dependent variable after each level.

Factorial Design

An experimental design that has more than one independent variable.

Mixed Design

An experimental design that combines within-subjects and between-subjects methods of data collection.

Experimental Realism

The degree to which a study participant becomes engrossed in the manipulation and truly influenced by it.

Mundane Realism

The degree to which a study parallels everyday situations in the real world.

Reliability

The stability or consistency of a measure.

Validity

The degree to which a tool measures what it claims to measure.

Sensitivity

The range of data a researcher can gather from a particular instrument.

Main Effect Hypothesis

A prediction that focuses on one independent variable at a time, ignoring all other independent variables.

Interaction Effect Hypothesis

A prediction about how the levels of one independent variable will combine with another independent variable to impact the dependent variable in a way that extends beyond the sum of the two separate main effects.

IRB

A board that reviews the ethical merit of all the human research conducted within an institution.

Descriptive

Describes or summarizes what is happening in a meaningful way.

Inferential

Tests a specific prediction about why something occurs.

Mixed Design

In this activity, you will create a design to test the impact of gender and time on perceived attractiveness. Within your design, you will investigate the combined effect of a between-subjects variable and a within-subjects variable on another variable.

Dr. Melanie Maggard

Dr. Natalie J. Ciarocco, Monmouth University

Dr. David B. Strohmetz, University of West Florida

Dr. Gary W. Lewandowski, Jr., Monmouth University

Something to Think About…

Scenario: Dating has changed drastically over the years, from chaperoned visits with family members to computerized matchmaking on smartphone apps. Speed dating, in particular, has exploded in popularity since its creation in 1998, when a rabbi started this event in a Los Angeles coffee shop as a way for busy adults to meet many potential partners in a short period of time. Although the effectiveness of speed dating for finding long-term mates is questionable, it does present us with an interesting opportunity in today’s fast-paced world.

Something to Think About…

We might wonder how exposure to so many potential partners, whether good or bad, might change how we perceive individuals as the night progresses. Similar to the “beer goggle effect,” where, the more alcohol we drink, the more likely we are to view people as attractive, is it possible that we become less picky or rate individuals as more attractive as the night goes on? Could fear of not finding a match at the end of the night make us judge those we meet as more attractive than we normally would?

Our Research Question

These questions prompt us to develop a research study that examines the relationship between gender, time, and attractiveness during a heterosexual speed dating event. But first, you will need a framework to help you explore this topic. Research studies all start with a question, so here is your chance to ask one of your own.

Question 1.1

Which of the following research questions would be best to ask given the goal of our study?

As a speed-dating event progresses, do men get pickier?

Do gender and perceived attractiveness change during a speed-dating event?

How do gender and time of rating during a speed-dating event influence perceived attractiveness?

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Correct.

Incorrect.

Picking the Best Design

Now that you have a research question (“How do gender and time of rating during a speed-dating event influence perceived attractiveness?”), you must decide which type of research design will best answer your research question. To narrow things down, consider the following:

Question 1.2

Does your research question require a nonexperimental design or an experimental design?

Nonexperimental

Experimental

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Picking the Best Design

Having decided that your research question requires a comparison between groups, you must determine the best comparison to make. However, keep in mind that there are multiple comparisons to be made: between genders and times of rating.

Question 1.3

Given the research question (“How do gender and time of rating during a speed-dating event influence perceived attractiveness?”), which of the following would be best to use for the gender comparison?

Males vs. Females

Males early in the evening vs. Females late in the evening

Perceived Attractiveness vs. Males vs. Females

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Correct.

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Picking the Best Design

Question 1.4

Early vs. Late

Early vs. Middle vs. Late

Perceived Attractiveness vs. Early vs. Late

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Correct.

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Picking the Best Design

Now that you have selected an experimental design that compares males’ and females’ ratings of perceived attractiveness at early, middle, and late points in the speed-dating event, you can identify your independent and dependent variables.

Question 1.5

Given the research question (“How do gender and time of rating during a speed-dating event influence perceived attractiveness?”), what are your independent variables?

Gender and perceived attractiveness

Gender and time of rating

Time of rating and perceived attractiveness

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Picking the Best Design

Question 1.6

Given the research question (“How do gender and time of rating during a speed-dating event influence perceived attractiveness?”), what is your dependent variable?

Speed-dating event

Age

Which of the following options would be the most reasonable times for the ratings to occur?

Early = Before the event; Middle = 1 hour into the event; Late = At the end of the dating event

Early = 15 minutes into the event; Middle = 1 hour into the event; Late = At the end of the dating event

Early = 15 minutes into the event; Middle = 2 hours into the event; Late = 1 hour after the dating event

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Operationally Defining the Independent Variables

The following table illustrates the 6 combinations in our 2 x 3 mixed design, where each person will be assigned to 1 of the gender conditions and all 3 of the time of rating conditions:

A: Males + Early
B: Males + Middle
C: Males + Late
D: Females + Early
E: Females + Middle
F: Females + Late

Summary of Our Factorial Study
		Time of Rating (Within-subjects)
		Early	Middle	Late
Gender (Between-subjects)	Males	A	B	С
Gender (Between-subjects)	Females	D	E	F

Operationally Defining the Dependent Variable

You have now established the key comparisons between the various conditions created by the 2 independent variables. Next, we need to specify the exact nature of our dependent variable, perceived attractiveness. First, consider the following:

Question 1.12

Which type of measure is better for assessing perceived attractiveness?

Self-report measure

Behavioral measure

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Choosing the Best Measure

We know we want to use a self-report measure to measure perceived attractiveness. Now it is time to determine which type of self-report measure to use. Keep in mind how many and what types of questions, reliability, validity, and sensitivity would be appropriate for our study.

Question 1.13

Which of the following would be the best self-report measure of perceived attractiveness in this study?

Sexual Desire Inventory (SDI, 1996): 11 items focusing on interest in or wish for sexual activity; has acceptable levels of reliability and validity and a score that remains stable over time

Attractiveness Rating: 1 question measuring perception of attractiveness of others on a scale of 1(very unattractive) to 10 (highly attractive); has some reliability and validity, as this simple rating has been used in many studies

Self-Perception of Attractiveness Rating: 1 question measuring self-perception of attractiveness on a scale of 1 (very unattractive) to 7 (very attractive); has some reliability and validity, as this simple rating has been used in many studies

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Determining Your Hypotheses

Now that you have determined what you will manipulate and measure, you must formulate an experimental hypothesis. Because we have multiple independent variables, we will also need multiple hypotheses: main effect hypotheses and interaction effect hypotheses.

Question 1.14

Given the nature of your study, which of the following is the best experimental hypothesis for the main effect of gender?

Gender leads to higher ratings of attractiveness.

Male ratings of female attractiveness will be higher than female ratings of male attractiveness.

Male ratings of female attractiveness will be higher at all 3 times than female ratings of male attractiveness at all 3 times.

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Correct.

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Determining Your Hypotheses

Question 1.15

Given the nature of your study, which of the following is the best experimental hypothesis for the main effect of time of rating?

Ratings of attractiveness will increase as the speed-dating event progresses from early, to middle, to late.

Male ratings of female attractiveness will be higher early in the speed-dating event than female ratings of male attractiveness late in the speed-dating event.

Participants will provide similar ratings of attractiveness throughout the speed-dating event.

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Correct.

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Determining Your Hypotheses

Now that we have our main effect hypotheses, we must create the interaction effect hypothesis.

Question 1.16

Which of the following is the best experimental hypothesis for the interaction effect of gender and time of rating?

Male ratings of female attractiveness will be higher late in the speed-dating event than female ratings of male attractiveness at the same time.

The impact of the perceived attractiveness on gender will be greater later in the speed-dating event than earlier.

Male ratings of female attractiveness will be higher late in the speed-dating event than any other combination of gender and time of rating.

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Correct.

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Finding a Sample

Before you can conduct your experiment, you need to determine exactly whom you want to study and where you can find this target sample.

Question 1.17

Which of the following samples would be best for your experiment?

A national random sample of single adults.

A random sample of adults who are registered to participate in a local speed-dating event.

A random sample of adults who are dating individuals they met during a local speed-dating event.

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Correct.

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Submitting to the IRB

Now that you have determined how you will collect your data and your intended sample, you must submit your research procedure to the Institutional Review Board (IRB) for ethical approval. The IRB will determine whether or not your study meets all ethical guidelines.

Each IRB has its own protocol that conforms to the national standard when a researcher submits an application for proposed research. In addition to the appropriate paperwork and other information submitted to the IRB, the board considers the following description during their evaluation of your proposed experiment:

The purpose of this research is to determine whether there is an interaction effect of gender (males or females) and time of rating (early, middle, or late) on perceived attractiveness. To study this topic, participants who are registered for a local speed-dating event will be told upon arrival that a study is being conducted on the attractiveness of individuals who participate in speed-dating events. Participants will be asked to rate the attractiveness of the daters they have met thus far 15 minutes after the event starts (early), 1 hour after the event starts (middle), and at the end of the 2-hour event (late). Participants will be debriefed at the end of the event regarding the true nature of the study.

Responding to the IRB

The IRB reviewed your submission and has 1 concern. Although the study appears to present less than minimal risk to participants, there is no mention of informed consent and voluntary participation.

You must now determine how to respond to the IRB, keeping in mind the ethics of respect for persons and autonomy.

Question 1.18

Which of the following is the best response to the IRB’s concern?

You respond by stating that participants have already consented by signing up for the speed-dating event; thus, no more confirmation of informed consent is required.

You respond by stating that verbal assent will be obtained from all participants before the study.

You respond by telling the IRB that you will have all participants sign an informed consent form before the speed-dating event starts.

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Correct.

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Running the Study

chapter_12_multiple_choice

Now that we have secured the IRB’s approval, we should determine what the entire study will look like. Below are the steps of the study. Can you place them in the proper order?

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Slide 25

Collecting Data

Now that you have a sense of how to conduct this study, it is time to see what data from this study might look like.

If you were to run a full version of this study, you would want to have at least 30 participants in each between-subjects group (males and females). Because you have a within-subjects design, each participant will be exposed to all levels of the time of rating independent variable. Thus, we need 60 participants for our study.

Example Data Set

This is an example of what your data set would look like. The top row shows the variable names; the other rows display the data for 10 participants.

In the “Gender” column, a 1 = Male, and a 2 = Female. The ratings of attractiveness are located under “Early” (after 15 minutes into the speed-dating event), under “Middle” (after 1 hour), and under “Late” (after 2 hours). Ratings range from 1(very unattractive) to 10 (highly attractive).

Participant Number	Gender	Early	Middle	Late
101	1	6	5	7
102	1	5	5	8
103	1	6	6	9
104	1	4	6	9
105	1	6	7	7
131	2	5	5	7
132	2	5	6	7
133	2	5	5	7
134	2	5	6	6
135	2	5	6	7

Selecting the Proper Tool

Now that you have collected your data, you must determine the best way to summarize your findings. The decisions you made about how to collect your data dictate the statistics you can use with your data now. First, you need to consider if your study is descriptive or inferential.

Question 1.19

Given the nature of your experiment, which of the following statistical methods is more appropriate?

Descriptive statistics

Inferential statistics

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Correct.

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Tutorial: Evaluating Output

chapter_12_table_activity

The following is an example of output for another mixed design ANOVA where participants experienced multiple conditions This study was about how hours slept at night (6 hours, 8 hours, and 10 hours) and gender (male or female) influence self-reported happiness. In this study, we recorded happiness after each night of sleep for each participant; thus, hours slept is a within-subjects variable and gender is a between-subjects variable.

Go through slides to learn more about each element of the output in the table.

Tests of Within-Subjects Effects
Measure: MEASURE_1
Source		Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
sleep	Sphericity Assumed	41.344	2	20.672	96.090	.000	.624
	Greenhouse-Geisser	41.344	1.855	22.284	96.090	.000	.624
	Huynh-Feldt	41.344	1.947	21.233	96.090	.000	.624
	Lower-bound	41.344	1.000	41.344	96.090	.000	.624
sleep * Gender	Sphericity Assumed	1.033	2	.517	2.402	.095	.040
	Greenhouse-Geisser	1.033	1.855	.557	2.402	.099	.040
	Huynh-Feldt	1.033	1.947	.531	2.402	.097	.040
	Lower-bound	1.033	1.000	1.033	2.402	.127	.040
Error(sleep)	Sphericity Assumed	24.956	116	.215
	Greenhouse-Geisser	24.956	107.611	.232
	Huynh-Feldt	24.956	112.934	.221
	Lower-bound	24.956	58.000	.430

Tests of Between-Subjects Effects
Measure: MEASURE_1
Transformed Variable: Average
Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
Intercept	2006.672	1	2006.672	6143.595	.000	.991
Gender	48.050	1	48.050	147.109	.000	.717
Error	18.944	58	.327

Question

Slide 29

Tutorial: Evaluating Output

chapter_12_table_activity_1

To report these numbers in a results section, enter the numbers as follows:

This should be done for each main effect and the interaction.

Go through slides to learn more about each element of the output in the table.

Pairwise Comparisons
Measure: MEASURE_1
(I) sleep	(J) sleep	Mean Difference (I-J)	Std. Error	Sig.^b	95% Confidence Interval for Difference^b
(I) sleep	(J) sleep	Mean Difference (I-J)	Std. Error	Sig.^b	Lower Bound	Upper Bound
1	2	-1.017*	.076	.000	-1.204	-.829
1	3	.000	.095	1.000	-.235	.235
2	1	1.017*	.076	.000	.829	1.204
2	3	1.017*	.081	.000	.816	1.217
3	1	.000	.095	1.000	-.235	.235
3	2	-1.017*	.081	.000	-1.217	-.816
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Bonferroni.

Question

Slide 30

Tutorial: Evaluating Output

chapter_12_table_graph

Go through slides to learn more about each element of the output in the table.

Estimates
Measure: MEASURE_1
Gender	Mean	Std. Error	95% Confidence Interval
Gender	Mean	Std. Error	Lower Bound	Upper Bound
Male	2.822	.060	2.702	2.943
Female	3.856	.060	3.735	3.976

Descriptive Statistics
Measure: MEASURE_1
Source	Gender	Mean	Std. Deviation	N
Six	Male	2.50	.509	30
	Female	3.50	.509	30
	Total	3.00	.713	60
Eight	Male	3.40	.498	30
	Female	4.63	.490	30
	Total	4.02	.792	60
Ten	Male	2.57	.504	30
	Female	3.43	.504	30
	Total	3.00	.664	60

Happiness

Hours of Sleep

This graph shows us that females had higher happiness ratings, regardless of the amount of sleep, than males. Also, the same pattern was observed in higher happiness after 8 hours of sleep and lower happiness after both 6 hours and 10 hours of sleep, regardless of gender. However, there was no interaction between gender and hours of sleep, as the pattern was consistent across levels of each variable.

Question

Slide 31

Your Turn: Evaluating Output

Below is the output from your study:

Tests of Within-Subjects Effects
Measure: MEASURE_1
Source		Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
time	Sphericity Assumed	196.078	2	98.039	199.130	.000	.774
	Greenhouse-Geisser	196.078	2.000	98.042	199.130	.000	.774
	Huynh-Feldt	196.078	2.000	98.039	199.130	.000	.774
	Lower-bound	196.078	1.000	196.078	199.130	.000	.744
time * Gender	Sphericity Assumed	8.811	2	4.406	8.948	.000	.134
	Greenhouse-Geisser	8.811	2.000	4.406	8.948	.000	.134
	Huynh-Feldt	8.811	2.000	4.406	8.948	.000	.134
	Lower-bound	8.811	1.000	8.811	8.948	.004	.134
Error(time)	Sphericity Assumed	57.111	116	.492
	Greenhouse-Geisser	57.111	115.997	.492
	Huynh-Feldt	57.111	116.000	.492
	Lower-bound	57.111	58.000	.985

Tests of Between-Subjects Effects
Measure: MEASURE_1
Transformed Variable: Average
Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
Intercept	6528.089	1	6528.089	16936.692	.000	.997
Gender	35.556	1	35.556	92.247	.000	.614
Error	22.356	58	.385

Pairwise Comparisons
Measure: MEASURE_1
(I) time	(J) time	Mean Difference (I-J)	Std. Error	Sig.^b	95% Confidence Interval for Difference^b
(I) time	(J) time	Mean Difference (I-J)	Std. Error	Sig.^b	Lower Bound	Upper Bound
1	2	-1.117*	.128	.000	-1.433	-.800
1	3	-2.550*	.128	.000	-2.865	-2.235
2	1	1.117*	.128	.000	.800	1.433
2	3	-1.433*	.128	.000	-1.749	-1.117
3	1	2.550*	.128	.000	2.235	2.865
3	2	1.433*	.128	.000	1.117	1.749
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Bonferroni.

Descriptive Statistics
Measure: MEASURE_1
Source	Gender	Mean	Std. Deviation	N
Early	Male	5.03	.850	30
	Female	4.57	.504	30
	Total	4.80	.732	60
Middle	Male	6.27	.691	30
	Female	5.57	.504	30
	Total	5.92	.696	60
Late	Male	8.10	.885	30
	Female	6.60	.498	30
	Total	7.35	1.039	60

Estimates
Measure: MEASURE_1
Gender	Mean	Std. Error	95% Confidence Interval
Gender	Mean	Std. Error	Lower Bound	Upper Bound
Male	6.467	.065	6.336	6.598
Female	5.578	.065	5.447	5.709

Your Turn: Evaluating Output

chapter_12_multiple_choice_2

Based on the results of your statistical analyses, provide the value for each prompt.

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Slide 33

Activity: Graphing Results

chapter_12_graph_activity_1

In order to visualize your data, use the values on the previous screens to input the mean that corresponds to each condition listed in the output. Then, check out the graphic representation of your data, below.

Descriptive Statistics
Measure: MEASURE_1
Source	Gender	Mean	Std. Deviation	N
Early	Male	5 5.03 3 4 5.5 6	.850	30
	Female	4.57 4 4.3 6 5 4.9	.504	30
	Total	4.80	.732	60
Middle	Male	6 6.7 6.27 7 5.9 6.1	.691	30
	Female	5.1 6 4 5.9 5 5.57	.504	30
	Total	5.92	.696	60
Late	Male	8.3 8.5 8.4 8 8.10 7	.885	30
	Female	6.60 6 6.9 4 5 6.08	.498	30
	Total	7.35	1.039	60

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Question

Slide 34

Your Turn: Results

Now that you have worked with your data, you must determine the best way to express your findings in written form. You must be sure that how you describe your findings accurately represents the data.

Question 1.20

Based on the statistical analysis, which of the following results sections best fits the data and analyses from your study?

As hypothesized, males (M = 6.47) gave higher ratings than females (M = 5.58), ratings were higher late in the event (M = 7.35) than early (M = 4.80) or in the middle (M = 5.92), and male ratings increased more over the course of the event than female ratings.

Contrary to the hypothesis, females (M = 6.47) gave higher ratings than males (M = 5.58), ratings were higher early in the event (M = 7.35) than middle (M = 4.80) or late (M = 5.92), and female ratings increased more over the course of the event than male ratings.

As hypothesized, males (M = 6.47) gave similar ratings to females (M = 5.58), ratings were higher late in the event (M = 7.35) than early (M = 4.80) or in the middle (M = 5.92), and male ratings changed the same amount over the course of the event as female ratings.

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Correct.

Incorrect.

Take Home Message

Now that you have determined how to express your findings in a scientifically responsible way, you need to be able to talk about what your findings mean in everyday terms so that the world can benefit from your science.

Question 1.21

How would you explain what you found about perceived attractiveness over the course of a speed-dating event, based on gender and time of rating, to a friend? Select the best option.

Females who want to be seen as more attractive should make sure they enter an event late or near the end of the evening.

Males should be told to be pickier as speed-dating events progress so as to counteract an unconscious increase in their ratings.

More so than females, males seem to become less stringent on ratings of attractiveness as speed-dating events progress.

More so than males, females become pickier about attractiveness as speed-dating events progress.

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Correct.

Incorrect.

Congratulations!

Congratulations! You have successfully completed this activity.

Participant Number	Gender	Early	Middle	Late
101	1	6	5	7
102	1	5	5	8
103	1	6	6	9
104	1	4	6	9
105	1	6	7	7
131	2	5	5	7
132	2	5	6	7
133	2	5	5	7
134	2	5	6	6
135	2	5	6	7

Participant Number	Gender	Early	Middle	Late
101	1	6	5	7
102	1	5	5	8
103	1	6	6	9
104	1	4	6	9
105	1	6	7	7
131	2	5	5	7
132	2	5	6	7
133	2	5	5	7
134	2	5	6	6
135	2	5	6	7

Participant Number	Gender	Early	Middle	Late
101	1	6	5	7
102	1	5	5	8
103	1	6	6	9
104	1	4	6	9
105	1	6	7	7
131	2	5	5	7
132	2	5	6	7
133	2	5	5	7
134	2	5	6	6
135	2	5	6	7