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Matching Hypothesis

The matching hypothesis is a theory of interpersonal attraction which argues that relationships are formed between two people who are equal or very similar in terms of social desirability. This is often examined in the form of level of physical attraction. The theory suggests that people assess their own value and then make ‘realistic choices’ by selecting the best available potential partners who are also likely to share this same level of attraction.

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Relationships: Physical Attractiveness

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Matching Hypothesis

Matching hypothesis definition.

The matching hypothesis refers to the proposition that people are attracted to and form relationships with individuals who resemble them on a variety of attributes, including demographic characteristics (e.g., age, ethnicity, and education level), personality traits, attitudes and values, and even physical attributes (e.g., attractiveness).

Background and Importance of Matching Hypothesis

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There is ample evidence in support of the matching hypothesis in the realm of interpersonal attraction and friendship formation. Not only do people overwhelmingly prefer to interact with similar others, but a person’s friends and associates are more likely to resemble that person on virtually every dimension examined, both positive and negative.

The evidence is mixed in the realm of romantic attraction and mate selection. There is definitely a tendency for men and women to marry spouses who resemble them. Researchers have found extensive similarity between marital partners on characteristics such as age, race, ethnicity, education level, socioeconomic status, religion, and physical attractiveness as well as on a host of personality traits and cognitive abilities. This well-documented tendency for similar individuals to marry is commonly referred to as homogamy or assortment.

The fact that people tend to end up with romantic partners who resemble them, however, does not necessarily mean that they prefer similar over dissimilar mates. There is evidence, particularly with respect to the characteristic of physical attractiveness, that both men and women actually prefer the most attractive partner possible. However, although people might ideally want a partner with highly desirable features, they might not possess enough desirable attributes themselves to be able to attract that individual. Because people seek the best possible mate but are constrained by their own assets, the process of romantic partner selection thus inevitably results in the pairing of individuals with similar characteristics.

Nonetheless, sufficient evidence supports the matching hypothesis to negate the old adage that “opposites attract.” They typically do not.

References:

• Berscheid, E., & Reis, H. T. (1998). Attraction and close relationships. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (4th ed., pp. 193-281). New York: McGraw-Hill.
• Kalick, S. M., & Hamilton, T. E. (1996). The matching hypothesis re-examined. Journal of Personality and Social Psychology, 51, 673-682.
• Murstein, B. I. (1980). Mate selection in the 1970s. Journal of Marriage and the Family, 42, 777-792.

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An Analysis of the Matching Hypothesis in Networks

1 Social Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA

2 Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA

3 Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA

Robert F. Spivey

4 Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708 USA

Boleslaw Szymanski

5 Społeczna Akademia Nauk, Łódź, Poland

Gyorgy Korniss

Conceived and designed the experiments: TJ RS BS GK. Performed the experiments: TJ. Wrote the paper: TJ RS BS GK.

Associated Data

All relevant data are within the paper.

The matching hypothesis in social psychology claims that people are more likely to form a committed relationship with someone equally attractive. Previous works on stochastic models of human mate choice process indicate that patterns supporting the matching hypothesis could occur even when similarity is not the primary consideration in seeking partners. Yet, most if not all of these works concentrate on fully-connected systems. Here we extend the analysis to networks. Our results indicate that the correlation of the couple’s attractiveness grows monotonically with the increased average degree and decreased degree diversity of the network. This correlation is lower in sparse networks than in fully-connected systems, because in the former less attractive individuals who find partners are likely to be coupled with ones who are more attractive than them. The chance of failing to be matched decreases exponentially with both the attractiveness and the degree. The matching hypothesis may not hold when the degree-attractiveness correlation is present, which can give rise to negative attractiveness correlation. Finally, we find that the ratio between the number of matched couples and the size of the maximum matching varies non-monotonically with the average degree of the network. Our results reveal the role of network topology in the process of human mate choice and bring insights into future investigations of different matching processes in networks.

Introduction

The process of pairing and matching between members of two disjoint groups is ubiquitous in our society. The underlying mechanism can be purely random, but in general decisions on selections are guided by rational choices, such as the relationship between advisor and advisee, the employment between employer and employee and the marriage between heterosexual male and female individuals. In many of these cases, similarities between the two paired parties are widely observed, such as similar research interests between the advisor and advisee and matched market competitiveness between the executives and the company. The principle of homophily, the tendency of individuals to associate and bond with others who are similar to them, can be applied to explain such similarities [ 1 ]. Yet, in some cases different mechanisms may be at work in addition to simply seeking similarities. For example, it has been discovered that people end up in committed relationship in which partners are likely to be of similar attractiveness, as predicted by the matching hypothesis in the field of social psychology [ 2 , 3 ]. However, if the closeness in attractiveness is the goal when searching for partners, one needs an objective self-estimation of it, which is rarely the case [ 4 ]. Furthermore, it is found in social experiments that people tend to pursue or accept highly desirable individuals regardless of their own attractiveness [ 3 , 4 ]. These findings suggest that the observed similarities may not be solely caused by explicitly seeking similarities. In some previous works, stochastic models are applied to simulate the process of human mate choice [ 5 – 10 ]. By simply assuming that highly attractive individuals are more likely to be accepted, the system generates patterns supporting the matching hypothesis even when similarity is not directly considered in the partner selection process [ 5 ]. Nevertheless, most if not all of these works (with a few recent exceptions [ 11 – 13 ]) concentrate on systems without topology, also known as fully-connected systems, in which one connects to all others in the other party and competes with all others in the same party. In reality, however, one knows only a limited number of others as characterized by the degree distribution of the social network. Hence a simple but fundamental question arises: what is the outcome of the matching process when topology is present?

In this work, we aim to address this question by analyzing the impact of network structure on the specific example of the process of matching, namely, human mate choice. Our motivation to address this question is caused not only by the limited knowledge on this matter, but also by the fact that topology could fundamentally change properties of the system and further affect its dynamical process. We have witnessed evidence of such impact, accumulated in the last decades from the advances towards understanding complex networks: a few shortcuts on a regular lattice can drastically reduce the mean separation between nodes and give rise to the small-world phenomenon [ 14 , 15 ], the power-law degree distribution of scale-free networks can eliminate the epidemic threshold of epidemic spreading [ 16 , 17 ] and synchronization can be reached faster in networks than in regular lattices [ 18 – 20 ]. Indeed, numerous discoveries have been made in different areas when considering topology in the analysis of many classical problems [ 21 – 30 ]. Hence it is fair to expect that the network topology would also bring new insights on the matching process that we are interested in.

We start with a bipartite graph with 2 N nodes. The bipartite graph consists of two disjoint sets m and f of equal size, representing two parties, each with N members. While our model can be more general, for simplicity, we consider the two parties as collections of heterosexual male and female individuals ( Fig 1a ). Each node, representing one individual, has k links drawn from the degree distribution P ( k ), randomly connecting to k nodes in the other set. On average, a node has ⟨ k ⟩ = ∑ kP ( k ) links, referred to the average degree of the network. To characterize the process of human mate choice, each node is assigned a random number a as its attractiveness drawn uniformly from the range [0,1). Combining features in some previous works [ 5 , 8 ] with the network structure, we consider the process of human mate choice as a two-step stochastic process which generates the numerical model as follows ( Fig 1b ):

• At each discrete time step, randomly pick a link. Let’s denote the nodes connected by this link as node i and node j and their attractiveness as a i and a j , respectively.
• Draw two random numbers independently and uniformly from the range [0,1), denoted by r i and r j . Check the matching condition defined as a i > r j and a j > r i .
• If the matching condition is satisfied and nodes i and j are not in a relationship with each other, pair them into intermediate pairing and dissolve them from any previous intermediate pairing with other nodes, if there are any.
• If the matching condition is satisfied and nodes i and j are already in the intermediate pairing with each other, join them into the stable couple. Make nodes i and j unavailable to others by removing them from the network together with all their links.
• Repeat from step 1 until there is no link left.

(a) An example of a bipartite graph, which is composed of two disjoint sets of nodes m and f . There is no link between nodes in the same set and the connection between sets is characterized by degree distribution P ( k ). (b) The action scheme of the mate choosing process. Two nodes i and j have to undergo an intermediate stage to reach the stable long term relation. During the intermediate stage nodes i and j are also available to build relationship with other nodes. If this happens they break and their relationship is back to the initial state.

The matching condition in step 2 ensures that individuals mutually accept each other. The decision making is probabilistic: the probability that node i accepts node j is a j (independent of its own attractiveness a i ). A pairing is successfully established only when both individuals decide to accept each other. The intermediate pairing created in step 3 corresponds to the tendency of people not to fully commit to a relationship at the beginning and to form a stable couple only after such unstable intermediate stage. The removal of nodes and links in step 4 merely accelerates the simulation, as these links should not be considered by others and the corresponding nodes in the stable state are not available for matching. Undoubtedly our model only captures a very small fraction of features in the matching process. The goal of this work is not to propose a sophisticated model that is able to regenerate all observations in reality. Instead, we focus on attractiveness and popularity (degree) that are essential in this process, hence this model could be the simplest to study the interplay between these two factors, shedding light on the effect of topology on this process.

To study the effects of topology, we focus on three most commonly used network structures with different degree distributions. 1) random k-regular graph (RRG) whose degree distribution follows a delta function P ( k ) = δ ( k −⟨ k ⟩), where ⟨ k ⟩ is the average degree of the network, corresponding to an extreme case that each person knows exactly the same number of others; 2) Erdős-Rényi network (ER) with a Poisson degree distribution P ( k ) = e −⟨ k ⟩ ⟨ k ⟩ k / k !, representing the situation that most nodes have similar number of neighbors and nodes with very high or low degrees are rare [ 31 ]; 3) scale-free network (SF) generated via static model whose degree distribution has a fat-tail P ( k ) ∼ k − γ , featuring a large number of low degree nodes and few high degree hubs [ 32 , 33 ]. The constructions of these networks are as follows.

Constructing a random k-regular graph. We start from two sets (sets m and f ) of N disconnected nodes indexed by integer number i ( i = 1,… N ). For each node i in the set m , connect it to nodes i , i +1, … and i + k −1 in the set f (using periodic boundary condition such that node N in the set m connects to node N , 1, … and k −2 in the set f , and so on). Then randomly pick two links, assuming that one link connects nodes i in the set m and j in the set f and the other connects nodes i ′ in the set m and j ′ in the set f . Check if there is a connection between nodes i and j ′ and nodes i ′ and j . If not, remove original links and connect nodes i and j ′ and nodes i ′ and j . Repeat this process sufficiently large number of times such that connections of the network are randomized.

Constructing an Erdős-Rényi network. We start from two sets (sets m and f ) of N disconnected nodes indexed by integer number i ( i = 1,… N ). Randomly select two nodes i and j respectively from sets m and f . Connect nodes i and j if there is no connection between them. Repeat the procedure until N ⟨ k ⟩ links are created.

Constructing a scale free network. The scale-free networks analyzed are generated via the static model. We start from two sets (sets m and f ) of N disconnected nodes indexed by integer number i ( i = 1,… N ). The weight w i = i − α is assigned to each node, where α is a real number in the range [0,1). Randomly selected two nodes i and j respectively from sets m and f , with probability proportional to w i and w j . Connect nodes i and j if there is no connection between them. Repeat the procedure until N ⟨ k ⟩ links are created. The degree distribution under this construction is P ( k ) = [ ⟨ k ⟩ ( 1 − α ) / 2 ] 1 / α α Γ ( k − 1 / α , ⟨ k ⟩ ( 1 − α ) / 2 ) Γ ( k + 1 ) where Γ( s ) the gamma function and Γ( s , x ) the upper incomplete gamma function. In the large k limit, the distribution becomes P ( k ) ∼ k − ( 1 + 1 α ) = k − γ .

Introducing correlations between the attractiveness and the degree. We generate 2 N random numbers drawn between 0 and 1 and sort them in ascending order and index them by integer number i ( i = 1, … 2 N ). We sort nodes of networks in ascending order of their degrees and index them by integer number j ( j = 1, … 2 N ). For positive correlation between the degree and attractiveness, assign i th random number as the attractiveness of node j = i . For negative correlation between the degree and attractiveness, assign i th random number as the attractiveness of node j = 2 N − i +1.

Effects of Network Topology on the Correlation in Attractiveness

The matching hypothesis suggests similarities in attractiveness between the two coupled individuals. To test it, we employ the Pearson coefficient of correlation ρ as a measure of similarity, that is defined as

where a m , i and a f , i are the attractiveness of the individuals in sets m and f of the i th couple, a ¯ m and a ¯ f are the average attractiveness of the matched individuals in sets m and f and n is the number of matched couples in the network. The Pearson coefficient of correlation ρ varies from -1 to 1, where 1 corresponds to the strongest positive correlation when two quantities are perfectly linearly increasing with each other, whereas -1 is the strongest negative correlation when two quantities are perfectly linearly dependent and one decreases when the other increases.

We first check the scenario studied in most of the previous works, when topology is not considered and each node is potentially able to match an arbitrary node in the other set. Our model generates a high correlation of the couple’s attractiveness with the average ρ ≈ 0.56 ( Fig 2a ). This value is similar to the result generated in the previously proposed model which accounts also for attractiveness decay [ 5 ] even though this feature is not present in ours. It is noteworthy that similarity is not explicitly considered when establishing a matching in this model and each individual only seeks attractive partners. However, the mutual agreement between two individuals effectively depends on the joint attractiveness of both. Hence individuals with high attractiveness will have the advantage in finding highly attractive partners, causing them to be removed from the dynamics soon, while less attractive individuals find their matches later. Therefore, as time goes on, only less and less attractive individuals are available to form a couple, thus they are more likely to get a partner with similar attractiveness.

(a) The Pearson coefficient of correlation ρ of the attractiveness between the two coupled individuals in different systems. ρ is strongest in fully-connected systems. In sparse networks, ρ increases monotonically with the average degree ⟨ k ⟩ and decreases with the degree diversity. For all cases investigated, system size is 2 N and N = 10,000. (b) The average attractiveness a ¯ f of individuals in the set f who are matched with those in a subset of m with attractiveness in the range [ a m −0.05, a m +0.05) for a series of points a m . In fully-connect systems, the less attractive individuals are bound to be coupled with ones who are also less attractive. In sparse networks, however, they are coupled with ones who are more attractive. (c) The attractiveness contour figure of the coupled individuals in Erdős-Rényi networks with average degree ⟨ k ⟩ = 5. A pattern emerges even when similarity is not the motivation in seeking partners. a m and a f are the attractiveness of nodes in sets m and f , respectively. (d) The attractiveness contour figure of the coupled individuals in fully-connected systems. The correlation is strongest towards the less attractive individuals (the circled part).

The positive correlations in attractiveness are also observed in all three classes of networks studied. They are lower than the correlation observed in the fully-connected systems but increase monotonically with the average degree ⟨ k ⟩. Furthermore, as the network degree distribution varies from a delta function to a Poisson distribution and to a fat-tail distribution, the variance in the degree distribution increases. Our results indicated that for a given ⟨ k ⟩, ρ decreases with the increased degree diversity ( Fig 2a ). In other words, the broader the degree distribution is, the lower the correlation in attractiveness between the two coupled individuals will be. The reason is that as the degree diversity increases, more and more links are connected to a few high degree nodes. The majority of nodes have lower degrees compared to the network with the same degree but smaller degree diversity. Hence the majority of nodes have less opportunities in selecting partners and therefore smaller chance to find a partner with closely matched attractiveness. As the result the attractiveness correlation decreases.

While the correlation in attractiveness is strongest when the system is fully-connected, we find that the difference in the correlations is caused mostly by the matched individuals with low attractiveness. Indeed, the average attractiveness of those who are coupled with highly desired individuals does not depend much on the presence of the network structure (Fig ​ (Fig2b 2b – 2d ). In fully-connected systems, less attractive individuals are bound to be coupled with partners of low attractiveness, which contributes significantly to the total correlation ρ . In sparse networks, however, if they successfully find partners, their partners are likely to be more attractive than them. Therefore, the limited choice in sparse networks reduces competitions among individuals, especially for those with low attractiveness, hence giving rise to lower attractiveness correlations between the two coupled individuals.

In fully-connected systems all individuals are able to find their partners. But in networks one faces a chance of failing to be matched. How often it occurs depends on one’s popularity (degree) and attractiveness. Here we consider P not ( a , k ) defined as the probability of failing to be matched conditioned on degree k and attractiveness within the range [ a −0.05, a +0.05). We find that P not ( a , k ) drops exponentially with both degree k and attractiveness a . This implies that getting more popular brings the similar benefit as being more attractive in terms of finding a partner ( Fig 3 ).

So far we have concentrated only on cases where there is no correlation between one’s popularity (degree) and attractiveness. In reality these two features are often correlated. On one hand, the positive correlation is somewhat expected as a highly attractive person can potentially be also very popular hence having a larger degree. On the other hand, negative correlation could also occur when those with low attractiveness are more active in making friends to balance their disadvantage in attractiveness. We extend our analysis to two extreme cases when degree and attractiveness are correlated (see Method ). For a given network topology, the correlation of attractiveness ( ρ ) is strongest when the degree and the attractiveness are positively correlated and weakest when they are negatively correlated. It is noteworthy that with negative degree-attractiveness correlation, ρ can become negative in networks with low ⟨ k ⟩, suggesting that the matching hypothesis may not hold in such networks even though the underlying mechanism does not change ( Fig 4 ).

ρ increases monotonically in all three cases analyzed. However, ρ is largest in networks where the degree and the attractiveness are positively correlated. When they are negatively correlated, ρ is weakest and can even be negative.

Number of Couples Matched

Another quantity affected by topology and typically studied is the number of couples a system can eventually match n [ 13 , 34 ]. When the system is fully-connected, everyone can find a partner and the number of couples is n = N . In sparse networks, typically there are fewer matched couples than N and the highest number of matched couples n max is given by the maximum matching which disregards the attractiveness [ 35 , 36 ]. To measure the performance of the system in terms of the matching, we focus on the quantity R = n / n max defined as the ratio between the number of couples matched and the size of the maximum matching. While both the number of the couples matched and the size of the maximum matching increase monotonically as the network becomes denser (Figs ​ (Figs5a, 5a , ​ ,5b), 5b ), their ratio R changes non-monotonically with ⟨ k ⟩ ( Fig 5c ). The system’s performance can be relatively good when the network is very sparse or very dense, but relatively poor for the intermediate range of density. This is mainly because when more links are added to the system, the number of couples matched increases slower than the size of the maximum matching; only when this size becomes saturated to N the ratio R starts to increase with ⟨ k ⟩.

(a) The size of the maximum matching n max increases monotonically with the average degree ⟨ k ⟩ in different networks. (b) The number of matched couples n increases monotonically with the average degree ⟨ k ⟩ in different networks. (c) The ratio between the number of matched couples and the size of the maximum matching ( R = n / n max ) varies non-monotonically with the average degree ⟨ k ⟩. (d) Different behaviors of R in Erdős-Rényi networks where the correlation between degree and the attractiveness varies. Negative correlation between the degree and the attractiveness yields the largest R while positive correlation between the degree and the attractiveness results in the smallest R . Networks tested in all cases are with size 2 N ( N = 10,000).

Correlation between the degree and attractiveness also plays a role in the value of R achieved by a network. The maximum matching n max depends only on the topology of the network and does not depend on the attractiveness. A successful matching between two nodes in our model, however, depends on both their attractiveness and their degrees. Therefore, R depends on the degree-attractiveness correlation. In both cases when either positive or negative correlation between degree and attractiveness is present, R varies non-monotonically with ⟨ k ⟩ just like in the case when there is no degree-attractiveness correlation ( Fig 5d ). However, negative correlation between degree and attractiveness yields more while positive correlation yields fewer matched couples than that when degree and attractiveness are uncorrelated. Considering the fact that the similarity between the two coupled individuals ( ρ ) is largest in networks with positive degree-attractiveness correlation and smallest with negative degree-attractiveness correlation, such a dependence of R on degree-attractiveness correlation implies that the system’s performance in terms of the number of matched couples is better when it is less selective.

In summary, we studied the effect of topology on the process of human mate choice. In general, our findings support the conclusion of the previous works that similarities in attractiveness between coupled individuals occur even though the similarity is not the primary consideration in searching for partners and each individual only seeks attractive partners, in agreement with the matching hypothesis. When topology is present, the extent of such similarity, measured by Pearson coefficient of correlation, grows monotonically with the increased average degree and decreased degree diversity of the network. The correlation is weaker in sparse networks because in them the less attractive individuals who are successful in finding partners, are likely to be coupled with more attractive mates. In fully-connected systems, however, they are almost certain to be coupled with partners also less attractive, contributing significantly to the total attractiveness correlation.

Another effect of the topology is that one faces a chance of failing to find a partner. Such the chance decays exponentially with one’s attractiveness and degree, therefore being more popular can bring benefits in terms of finding a partner similar to being more attractive. The correlation of couple’s attractiveness is also affected by the degree-attractiveness correlation, which is strongest in networks where attractiveness and popularity are positively correlated and weakest when they are negatively correlated. In networks with negative degree-attractiveness correlation, the attractiveness correlation between coupled individuals can be negative when the average degree is low, implying that matching hypothesis may not hold in such systems. Finally, the number of couples matched also depends on the topology. The ratio between the number of matched couples and the maximum number of couples that can be matched, denoted as R , changes non-monotonically with the average degree. R is largest in networks with negative degree-attractiveness correlation and smallest when the attractiveness and the popularity are positively correlated.

The non-monotonic behavior of the matching ratio R is also interesting from a stochastic optimization viewpoint: the simple trial-and-error matching process, governed and constrained by individuals’ attractiveness, fares reasonably well everywhere (against the maximum attainable matching on a given bipartite graph), except for a narrow intermediate sparse region ( Fig 5 ). The “worst-case” average degree depends strongly on network heterogeneity but not on degree-attractiveness correlations.

Our results revealed the role of topology in the process of human mate choice and can bring further insights into the investigations of different matching processes in different networks [ 13 , 34 , 37 – 39 ]. Indeed, in this work we focused only on the basic model of the mate seeking process in random networks. However, different variations can be considered. For example, there is no degree correlation between the two coupled individuals observed in our model, simply because the networks we studied are random with no assortativity. In reality, the connection may not be random and then assortativity can be considered. Furthermore, the networks in our model are static and the degree of a node does not change with time. In reality, a node may gain or lose friends and consequently its degree may change. Likewise, stable matching between individuals does not have to last forever, it just needs to be an order of magnitude longer than unstable matching. It is possible to establish certain rates to stable matching dissolution and analyze the steady state behavior of so generalized system. Finally, here we considered the attractiveness as a one dimensional attribute of individuals. In more realistic scenarios, attractiveness can be a multi-dimensional variable with different merits [ 9 , 40 , 41 ]. Investigations of such more complicated cases are left to future work.

Funding Statement

This work was supported in part by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 and by the Office of Naval Research (ONR) grant no. N00014-09-1-0607. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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8.1 Inference for Two Dependent Samples (Matched Pairs)

Learning Objectives

By the end of this chapter, the student should be able to:

• Classify hypothesis tests by type
• Conduct and interpret hypothesis tests for two population means, population standard deviations known
• Conduct and interpret hypothesis tests for two population means, population standard deviations unknown
• Conduct and interpret hypothesis tests for matched or paired samples
• Conduct and interpret hypothesis tests for two population proportions

Studies often compare two groups. For example, maybe researchers are interested in the effect aspirin has in preventing heart attacks.  One group is given aspirin and the other a placebo , and the heart attack rate is studied over several years.  Other studies may compare various diet and exercise programs.  Politicians compare the proportion of individuals from different income brackets who might vote for them. Students are interested in whether SAT or GRE preparatory courses really help raise their scores.

You have learned to conduct inference on single means and single proportions .  We know that the first step is deciding what type of data we are working with.  For quantitative data we are focused on means, while for categorical we are focused on proportions.  In this chapter we will compare two means or two proportions to each other.  The general procedure is still the same, just expanded.  With two sample analysis it is good to know what the formulas look like and where they come from, however you will probably lean heavily on technology in preforming the calculations.  

To compare two means we are obviously working with two groups, but first we need to think about the relationship between them. The groups are classified either as independent or dependent.  I ndependent samples consist of two samples that have no relationship, that is, sample values selected from one population are not related in any way to sample values selected from the other population.  Dependent samples consist of two groups that have some sort of identifiable relationship.

Two Dependent Samples (Matched Pairs)

Two samples that are dependent typically come from a matched pairs experimental design. The parameter tested using matched pairs is the population mean difference .  When using inference techniques for matched or paired samples, the following characteristics should be present:

• Simple random sampling is used.
• Sample sizes are often small.
• Two measurements (samples) are drawn from the same pair of (or two extremely similar) individuals or objects.
• Differences are calculated from the matched or paired samples.
• The differences form the sample that is used for analysis.

Confidence intervals may be calculated on their own for two samples but often, especially in the case of matched pairs, we first want to formally check to see if a difference exists with a hypothesis test.  If we do find a statistically significant difference then we may estimate it with a CI after the fact.

Hypothesis Tests for the Mean difference

In a hypothesis test for matched or paired samples, subjects are matched in pairs and differences are calculated, and the population mean difference, μ d , is our parameter of interest.  Although it is possible to test for a certain magnitude of effect, we are most often just looking for a general effect.  Our hypothesis would then look like:

H o : μ d =0

H a : μ d (<, >, ≠) 0

The steps are the same as we are familiar with, but it is tested using a Student’s-t test for a single population mean with n – 1 degrees of freedom, with the test statistic:

A study was conducted to investigate the effectiveness of hypnotism in reducing pain. Results for randomly selected subjects are shown in the figure below. A lower score indicates less pain. The “before” value is matched to an “after” value and the differences are calculated. The differences have a normal distribution. Are the sensory measurements, on average, lower after hypnotism? Test at a 5% significance level.

A study was conducted to investigate how effective a new diet was in lowering cholesterol. Results for the randomly selected subjects are shown in the table. The differences have a normal distribution. Are the subjects’ cholesterol levels lower on average after the diet? Test at the 5% level.

Confidence Intervals for the Mean difference

If we are using the t distribution, the error bound for the population mean difference is:

• use df = n – 1 degrees of freedom, where n is the number of pairs
• s d =  standard deviation of the differences.

A college football coach was interested in whether the college’s strength development class increased his players’ maximum lift (in pounds) on the bench press exercise. He asked four of his players to participate in a study. The amount of weight they could each lift was recorded before they took the strength development class. After completing the class, the amount of weight they could each lift was again measured. The data are as follows:

The coach wants to know if the strength development class makes his players stronger, on average.

Using the differences data, calculate the sample mean and the sample standard deviation.

Using the difference data, this becomes a test of a single __________ (fill in the blank).

Calculate the p -value:

What is the conclusion?

A new prep class was designed to improve SAT test scores. Five students were selected at random. Their scores on two practice exams were recorded, one before the class and one after. The data recorded in the figure below. Are the scores, on average, higher after the class? Test at a 5% level.

Image Credits

Figure 8.1: Ali Inay (2015). “Brunching with Friends.” Public domain. Retrieved from https://unsplash.com/photos/y3aP9oo9Pjc

Figure 8.3: Kindred Grey via Virginia Tech (2020). “Figure 8.3” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Figure_8.3.png . Adaptation of Figure 5.39 from OpenStax Introductory Statistics (2013) (CC BY 4.0). Retrieved from https://openstax.org/books/statistics/pages/5-practice

Figure 8.6: Kindred Grey via Virginia Tech (2020). “Figure 8.6” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Figure_8.6.png . Adaptation of Figure 5.39 from OpenStax Introductory Statistics (2013) (CC BY 4.0). Retrieved from https://openstax.org/books/statistics/pages/5-practice

An inactive treatment that has no real effect on the explanatory variable

The facet of statistics dealing with using a sample to generalize (or infer) about the population

The arithmetic mean, or average of a population

The number of individuals that have a characteristic we are interested in divided by the total number in the population

Numerical data with a mathematical context

Data that describes qualities, or puts individuals into categories

The occurrence of one event has no effect on the probability of the occurrence of another event

Very similar individuals (or even the same individual) receive two different two treatments (or treatment vs. control) then the difference in results are compared

The mean of the differences in a matched pairs design

The probability distribution of a statistic at a given sample size

The value that is calculated from a sample used to estimate an unknown population parameter

Significant Statistics Copyright © 2020 by John Morgan Russell, OpenStaxCollege, OpenIntro is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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The matching hypothesis

The Matching Hypothesis

Introduction

There are many factors involved in the formation of relationships, proximity, exposure and family, similarity, physical attractiveness, complementarily, competence and reciprocal liking. In this investigation, the research will explore attractiveness, specifically the match hypothesis.

 Zuckerman et al (1995) reported that the more attractive a person seemed to be, the more positive was another’s overall impression of that person.

Symons (1979) showed that a woman’s physical health, age, and uniqueness are attractive to men whereas a man’s status, height, skills, and abilities are attractive to women. Berry and Miller (2001) found that males rated physical attractiveness as the best predictor for higher quality interactions with woman, while woman rated sociability as the most important factor for men.

A study was carried out by Walster et al in 1966 known as the ‘computer dance study’. 752 ’freshers’ took part. First they were told to fill in a questionnaire, after which they were told that they had been allocated an ideal partner for the evening of the dance. These pairings however, had been made at random on basis of their physical attractiveness. Students were asked how much they liked their date and if they wanted to see them again. They found that physical attractiveness was the single biggest predictor of how much each date had been liked by both male and the female participants. The desire of another date was determined by the attractiveness of the female, irrespective of the attractiveness of the male.

When we see a person in the street we automatically rate that person’s attractiveness, whether we do it consciously or unconsciously. The matching hypothesis is a popular psychological theory proposed by Walster et al. 1966, on what causes people to be attracted to their partners. It claims that people are more likely to form long- lasting relationships with people who are roughly equally as physically attractive as themselves. This investigation is going to replicate this study.

‘The matching phenomenon of physical attraction between marriage partners is stable within and across generations’, Price and Vandenberg 1979.

Several studies have tested the matching hypothesis. These studies generally show that people rated as being of high, low or average attractiveness tend to choose partners of a corresponding level of attractiveness.

Several studies have been carried out that explore this field of interest for, Murstein (1972) who also supports the matching hypothesis did a study with photographs of the faces of ‘steady or engaged’ couples were compared with random couples. The real couples were consistently judged to be more similar to each other in levels of physical attractiveness than the random pairs. Murstein summarised the findings of the study as: ‘Individuals with equal market value for physical attractiveness are more likely to associate in an intimate relationship such as engagement that individuals with disparate values.’

In simple terms, he found that people with roughly equal attractiveness are more likely to establish an intimate relationship, than if one person out of the couple was seen as being ‘unattractive’ and the other ‘attractive’.

This investigation focuses on couples’ separate attractiveness and their attractiveness as a couple, analysing singular attractiveness and coupled attractiveness.

The aim of the study is to investigate the matching hypothesis and to test whether there is a positive correlation between the scores of perceived attractiveness of the male and female of the married couples and also as a couple. This investigation differs to previous studies carried out in this area of interest, as the photographs are not separated and the males and females are rated separately in terms of attractiveness. Participants were also asked to rate the photos as a couple.

The hypothesis:

There would be a positive correlation between participants perceived scores of attractiveness of photographs of married couples.

Null hypothesis:

There would be no correlation between participants perceived scores of attractiveness of photographs of married couples.

This is a preview of the whole essay

The method chosen for this study was a correlational research method, as a relationship between the two variables was being investigated. The co-variance is the male and female scores. All the photos used throughout the procedure are obtained from articles from a local newspaper. After the photos are obtained, record sheets will be produced on which the participants will rate the couples. The photos used will be kept together (i.e. they will not be cut into separate male and female sections) for the simple reason that I am also asking the question; do the couples match each other? This would be impossible to do so if the photographs were separated. This also makes my investigation more original. The participants will then be presented with forms like the record sheet (appendix). Cause and analysis was the appropriate method for this investigation as it provides information on the strength of a relationship between specific variables.

The gender of the participants may affect how attractive they perceive the male/ female and the couple as a whole, so an equal number of male and female participants were chosen (10 male and 10 female) through an opportunist sample. I will take the following into account:

• The light in the room- a dull light may distort the photos.
• I must make sure participants are not distracted whilst carrying out the investigation.
• The temperature of the room the study is carried out in which will be about 19-21 degrees. The place has to comfortable with decent lighting and distraction free. The best place to achieve all this is the library, as all these factors are eliminated due to the common environment of a library.
• The colour of photos (black and white or colour) - A low quality photograph may cause bias as it may create a false image of the people and/ or person to overcome this I will only use colour photos on the same quality paper- to eliminate any possibility of bias through quality
• The size of photos- if the photographs are not big enough, it will be difficult for participants to rate people in terms of attractiveness if they cannot see them properly therefore have each photo as a similar size- if one photograph of one couple is considerably bigger than another it will ultimately make the investigation biased.
• Have an equal number of male and female participants- To eliminate any gender bias as some male participants may find it hard to rate male attractiveness.
• The overall shape of someone may have an effect on their attractiveness thus I will only use photos that show the upper part of the couples bodies.- much research has already been carried out studding the attractiveness of people as a whole (i.e. full length photographs), this will make the investigation more original and less biased as all the couples will only have face photos.

The materials I will be using are:

• Paper- forms, instead of using 1 form per person, I will be using one form per couple for participants to fill in.
• Photographs

Participants

20 participants will be drawn from the library using opportunist sample. There will be 10 male participants and 10 female participants to eliminate gender bias and also to explore gender differences in attractiveness rating.

The target participants are males and females of 16-18 year old.

The researcher, me is a 17 year old 6 th  form student from a Northeast School. The participants are all from Newcastle upon Tyne, they were chosen as they were using the school library at the time of the investigation been carried out.

Found 5 photographs of couples from a local newspaper, made sure they were all of similar size, in black and white. Next forms produced with the photos on the back of the score sheet, with a rank of 1-10 (10 being the highest- most attractive, see appendix). There is an equal amount of participants, made sure the study was carried out in a suitable area with an equal number of male and female participants taking part (If it is a mix gender study).

When participating in the investigation, the participants were informed exactly what to do, they were briefed and finally debriefed. There will be a set of instructions to go with the briefing stage.

• Your task is to rate each person in the couple on their attractiveness.
• People’s attractiveness is rated on a scale of 1-10, with 10 being the highest.
• You are then, on sheet number 2, to state if you think the couples suit each other as a couple.
• You will do this by placing a Y (yes) or an N (no) next to the number of the couple’s photographs.

Before participating, people will be given a sheet of paper that briefs them about what kind of investigation they are partaking in, below is what it will say:

“You are now about to participate in a psychological study, you have the right to withdraw at any time during this investigation. If you do choose to withdraw, all evidence of your participation in this experiment will be destroyed. None of the data collected will be traceable back to you”

After their contribution to my investigation, each participant will be debriefed. Again they will be handed a piece of paper that briefly states what I was investigating:

Debriefing:

“You have just finished partaking in my psychological investigation, you still have the right to withdraw any information you have given. My investigation was looking into the attractiveness of couples and the matching hypothesis. Remember none of the data I have collected can be traceable back to you, thank you for taking part’

The data was analysed using influential statistics. The test used was the Spearman’s Rank Order Correlation Coefficient test. This test was selected as it is a two sample test of correlation for use with data that has an ratio level of measurement because they were rated scale 1-10 then converted to ordinal data and was rank ordered (see appendix).

A 5% level of significance was selected as this represents the balance between making a type one and type two error. A type one error being when the null hypothesis is rejected even if it is true, and the probability of making an error like it is equal to the level of significance- 5%. A type two error is when a null hypothesis is reserved when it is actually wrong.

Statistical summary:

The critical value of rs is 0.9 the critical value of rs for a directional hypothesis at the 5% significant level where n= 5, my value from the spearman’s rank was 1.0. This shows a strong positive correlation. As the observed value is higher than the critical value, the alternative hypothesis is accepted, and the null hypothesis is rejected. It can be concluded that any differences in results are not due to chance alone.

A table to show the overall ratings for each person

(Descriptive stats over the page)

Descriptive statistics:

A table to show the mean scores of each person

A bar chart to show:

Matching hypothesis results:

After participants rated the attractiveness of each couple, they also answered a question ‘Do the couples match?’ (See appendix). The participants either answered ‘Yes’ or ‘No’ for each couple. The table above shows the results. For couple 3, all participants said ‘Yes’, couple 1 match the least according to the participants.

Discussion:

Aim of research:

The aim of this investigation was to find out if couples are rated as having similar attractiveness, and if they are suited as a couple.

The hypothesis was that male and female couples would be rated as having similar attractiveness by showing a photograph of the couple and asking participants to rate them separately in terms of attractiveness. This was found true, although the results are not significant, the results obtained did account for the research found in the introduction.

The investigation seemed to follow certain patterns; this lead to the expansion of the study. One pattern found was the common pattern that was the ranking for males in the photos by male participants, and the ranking for females in the photos by the female participants. What was discovered was that males continuously rank other males much lower than they rank the females in the photographs, for example male number  2 rated the male in photograph 1 only 2 points, whereas he rated the female of the couple 8 points. For female participants the opposite is true with a couple of anomalies. For example, on photograph 2, 2 female participants number 19 rated the male as 7 however only rates the female 5, unlike most female participants who consistently ranked the female higher than the male.

Looking at the mean scores (see results) compared to the male participant scores for each male (see appendix) the difference can sometimes be quite considerable. When being asked to rate the males in the photographs, male participants seemed to feel strange about rating them high, even a fair ranking appeared extremely difficult for them to do. Only on photograph 4 did they seem to rate the male in conjunction with the score they gave to the female of that couple (see appendix).

This could be for a number of reasons; firstly, males may have more confidence in their physical attractiveness than females, maybe due to the media focusing on such trivial matters as ‘celebrity diets’. Also males may feel uneasy about judging another male’s attractiveness as they might then be referred to as ‘gay’ in today’s society.

Moving onto the female participants, who (bar 2 photographs) rated the female in the couple higher than the males. It seems socially acceptable for a female to say ‘oh yeah she’s pretty’, however for a male to acknowledge another man’s attractiveness would be frowned upon, in that they would be perceived as holding homosexual tendencies, or this may be due to them being oblivious to what woman find attractive in men.

There are a number of additional factors that might have caused the females to be perceived as more attractive than the males. For example, the photographs of the couples were of their wedding day. This would make both the bride and the groom, however specially the bride, look more attractive. This is because on wedding days the bride is especially groomed to look her best- and so usually looks much better than that in everyday life. This is, a confounding variable, as she is perceived to look more attractive than she actually is.

Another factor that I discovered when studying the matching hypothesis was that the couples who were rated lower received more marks (see appendix) for whether they suited each other or not. Couple number 3 who received an overall ranking of 119 (lowest ranking) received 20 points (highest possible score) for the matching hypothesis. Also couple 5 with 200 points (second highest score) received 10 marks for the matching hypothesis (second lowest).

Relationship to background research

The results gained in this investigation support Murstein’s 1972 research where comparison of actual couples and random couples were judged on attractiveness. The research obtained in this investigation also found that couples are rated as having similar attractiveness.

From the investigation it was determined that the perceived level of physical attractiveness of females is far greater than the perceived level of attractiveness of males. There are many studies that give reasons for this. For example, women usually chose partners that are less attractive than they are. Huston (1973) suggested that people were afraid of being rejected by their prospective partners. They deliberately choose someone who is similar to them, not because they find them most attractive, but because they don’t want to be rejected.

Limitations and modifications

The photographs I used were not as good as they should have been for this particular investigation of attraction because they were black and white and also not good quality photos. To overcome this problem, I would only use large photographs, around 5x5cm for future investigation. I would also use more photos, to make it a more specific test and also to make my results more significant to this area of psychology. I would use approximately 10 photos, as it would give an easy average to work with. Even though, the photographs were not as good as they should have been, there might have been a more major reason for my results. People may rate attractiveness in a totally different way. For example a person may be attracted to someone because of their body language rather than their physical attractiveness. I would conquer this by video taping a couple on their wedding day and ask participants to judge their attractiveness by more factors than just physical attractiveness.

Suggestions for further research

My hypothesis was that there would be a positive correlation between participants perceived scores of attractiveness of photographs of married couples.

For further research into this area of psychology, I would tape several couples on their wedding day and (with their consent) get participants to rate them on many different aspects of attractiveness. Factors such as; proximity, body language, facial features, humour etc. would also be taken into consideration, with the investigation focusing on which of these would be the ultimate reason why people are attracted to others. My hypothesis would be that the more attracted the couples are to each other the longer their marriage would last. This includes factors such as proximity, body language, facial features, humour etc would be the factors to observe. For example the more body language they have towards each other it increases the rate of attraction between them.

From this investigation you can extract the idea that it’s not the most attractive who gets the most partner, everyone finds a partner similar to their own attractiveness.

Bibliography:

MURSTEIN,B.I.(1972) Physical attractiveness and martial choice.

Journal of Personality and Social Psychology, 22,8-12.

PRICE, R.A & VANDENBERG, S.G (1979) Matching for physical attractiveness in married couples. Personality and Social Psychology. Bulltin, 5, 398-400 .

WALSTER,E. ARONSON, E.&ABRAHAM, D. & ROTTMAN, L.. (1966)

Importance of physical attractiveness in dating behaviour.

Journal of Personality and Social Psychology 4, 508-516.

SYMONS,D.(1979) The Evoultion of Human Sexuality. NEW YORK:

Oxford University Press.

MILLER,G.F. (1998) How mate choice shaped human nature: A review of sexual selection and human evolution. In C.Crwford &D.Krebs

(Eds) Handbook of Evolutionary Psychology, Mahwah, NJ:Erlaum.

Document Details

• Word Count 3297
• Page Count 11
• Level AS and A Level
• Subject Psychology

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Research Hypothesis In Psychology: Types, & Examples

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On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

Open Education Sociology Dictionary

matching hypothesis

Table of Contents

Definition of Matching Hypothesis

( noun ) The theory that people select romantic and sexual partners who have similar statuses such as physical attraction and social class.

Matching Hypothesis Pronunciation

Pronunciation Usage Guide

Syllabification : match·ing hy·poth·e·sis

Audio Pronunciation

Phonetic Spelling

• American English – /mAch-ing hie-pAHth-uh-suhs/
• British English – /mAch-ing hie-pOth-i-sis/

International Phonetic Alphabet

• American English – /ˈmæʧɪŋ haɪˈpɑθəsəs/
• British English – /ˈmæʧɪŋ haɪˈpɒθɪsɪs/

Usage Notes

• Plural:  matching hypotheses
• A type of homogamy.
• Also called matching phenomenon .

Additional Information

• Sex and Gender Resources – Books, Journals, and Helpful Links
• Word origin of “match” and “hypothesis” – Online Etymology Dictionary: etymonline.com
• Rosenblum, Karen Elaine, and Toni-Michelle Travis. 2016.  The Meaning of Difference: American Constructions of Race, Sex and Gender, Social Class, Sexual Orientation, and Disability . 7th ed. New York: McGraw-Hill.

Related Terms

• ascribed status
• discrimination

Works Consulted

Branscombe, Nyla R., and Robert A. Baron. 2017. Social Psychology . 14th ed. Harlow, England: Pearson.

Encyclopædia Britannica. (N.d.)  Britannica Digital Learning . ( https://britannicalearn.com/ ).

Wikipedia contributors. (N.d.) Wikipedia, The Free Encyclopedia . Wikimedia Foundation. ( https://en.wikipedia.org/ ).

Cite the Definition of Matching Hypothesis

ASA – American Sociological Association (5th edition)

Bell, Kenton, ed. 2016. “matching hypothesis.” In Open Education Sociology Dictionary . Retrieved March 31, 2024 ( https://sociologydictionary.org/matching-hypothesis/ ).

APA – American Psychological Association (6th edition)

matching hypothesis. (2016). In K. Bell (Ed.), Open education sociology dictionary . Retrieved from https://sociologydictionary.org/matching-hypothesis/

Chicago/Turabian: Author-Date – Chicago Manual of Style (16th edition)

Bell, Kenton, ed. 2016. “matching hypothesis.” In Open Education Sociology Dictionary . Accessed March 31, 2024. https://sociologydictionary.org/matching-hypothesis/ .

MLA – Modern Language Association (7th edition)

“matching hypothesis.” Open Education Sociology Dictionary . Ed. Kenton Bell. 2016. Web. 31 Mar. 2024. < https://sociologydictionary.org/matching-hypothesis/ >.